OPEN DESIGN SYSTEMS

Research in Design Series

Aims & Scope
The book series Research in Design Series (RIDS) covers the discipline of design methodology and is
intended for those who are involved in the methodology and technology of design from a scientific,
practitioner or educational point of view. Individual volumes deal with historical, philosophical,
engineering, and scientific aspects of design methodology and may cover implications for the training
of engineers and in technology education. Contributions from various disciplines are welcomed.

Volume 10
Previously published in this series:

Vol. 9 C.A. Bakker and R. Mugge (Eds.), PLATE: Product Lifetimes And The Environment –
Conference Proceedings of PLATE 2017, 8-10 November 2017, Delft, The Netherlands
Vol. 8 M. Eekhout and P. van Swieten, The Delft Prototype Laboratory
Vol. 7 L.A. van Gunsteren, Quality in Design and Execution of Engineering Practice
Vol. 6 L.A. van Gunsteren, Stakeholder-oriented Project Management – Tools and Concepts
Vol. 5 E. Bohemia, K. Harman and K. Lauche, The Global Studio: Linking Research, Teaching
and Learning
Vol. 4 K. Moraes Zarzar and A. Guney (Eds.), Understanding Meaningful Environments:
Architectural Precedents and the Question of Identity in Creative Design
Vol. 3 M. Eekhout and T. Tomiyama (Eds.), Delft Science in Design 2
Vol. 2 E. van de Kar and A. Verbraeck, Designing Mobile Service Systems
Vol. 1 R. Binnekamp, L.A. van Gunsteren and P.P. van Loon, Open Design, a Stakeholder-
oriented Approach in Architecture, Urban Planning, and Project Management

ISSN 1569-7258 (print)

ISSN 1879-8233 (online)
Open Design Systems

A.R.M. (Rogier) Wolfert

© 2023 The Author.

This book is published online with Open Access on www.ebooks.iospress.nl under the terms of the
Creative Commons Attribution License 4.0 (CC BY 4.0).

The Open Access version of this work is funded by TU Delft OPEN Publishing and will be displayed
on their platform.

ISBN 978-1-64368-416-1 (print)

ISBN 978-1-64368-417-8 (online)
DOI 10.3233/RIDS10
Library of Congress Control Number: 2023942638

Publisher
IOS Press BV
Nieuwe Hemweg 6b
1013 BG Amsterdam
The Netherlands
www.iospress.com
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LEGAL NOTICE
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PRINTED IN THE NETHERLANDS
Contents

Opening 1
Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
Commendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
Reading guide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

I Setting the Odesys scene 17

1 Frame of reference 19
1.1 Design, science, engineering & management . . . . . . . . . . . . . 19
1.2 Systems engineering & thinking . . . . . . . . . . . . . . . . . . . . 23
1.3 Modeling & models . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
1.4 Multi-objective optimisation . . . . . . . . . . . . . . . . . . . . . . 33
1.5 Odesys’ common terms & definitions . . . . . . . . . . . . . . . . . 37
1.6 Odesys’ paradigms & views on world and man . . . . . . . . . . . . 46

2 Design in the context of science & engineering 63

2.1 Scientist versus engineer . . . . . . . . . . . . . . . . . . . . . . . . 64
2.2 Empirical R&D, the 4-Quadrant model . . . . . . . . . . . . . . . . 67
2.3 Values of science & engineering . . . . . . . . . . . . . . . . . . . . 76
2.4 Con-science, the extended 4-Quadrant model . . . . . . . . . . . . . 80
2.5 Open-ended Odesys research questions . . . . . . . . . . . . . . . . 98

3 Managing the service provider organization 101

3.1 Socio-eco purpose, the quality of service concept . . . . . . . . . . . 102
3.2 Social laws & principles, a basis for Odesys . . . . . . . . . . . . . . 113
3.3 Open loops management, an act of U-ncovering . . . . . . . . . . . 116

4 Designing to best fit for common purpose 127

4.1 Common socio-eco interests, the design tY model . . . . . . . . . . 128
4.2 Open designing, an act of U-ncovering . . . . . . . . . . . . . . . . 135
v
vi

5 Mathematical modeling design & decision problems 143

5.1 Multi-criteria decision analysis & preference function modeling . . . 144
5.2 Single- & multi-objective design optimisation . . . . . . . . . . . . . 154
5.3 Conspection & curiosity . . . . . . . . . . . . . . . . . . . . . . . . 162

II Odesys methodology and applications 165

6 Socio-technical systems design & integration 167
6.1 Odesys’ methodology & significance . . . . . . . . . . . . . . . . . . 169
6.2 Odesys’ mathematical formulation . . . . . . . . . . . . . . . . . . . 174
6.3 Threefold modeling framework & the Odesys’ U . . . . . . . . . . . 177
6.4 IMAP & the Preferendus . . . . . . . . . . . . . . . . . . . . . . . . 180

7 Formative Odesys examples 183

7.1 Bridge design (revisited) . . . . . . . . . . . . . . . . . . . . . . . . 184
7.2 Shopping mall (linear & non-linear) . . . . . . . . . . . . . . . . . . 186
7.3 Supermarket (non-linear & non-monotonic) . . . . . . . . . . . . . . 192

8 Summative Odesys applications 197

8.1 Open-ended Odesys’ U . . . . . . . . . . . . . . . . . . . . . . . . . 200
8.2 Norwegian light rail . . . . . . . . . . . . . . . . . . . . . . . . . . . 201
8.3 German power transmission line . . . . . . . . . . . . . . . . . . . . 208
8.4 Dutch rail level crossing . . . . . . . . . . . . . . . . . . . . . . . . 216
8.5 South Korean floating wind farm . . . . . . . . . . . . . . . . . . . 222

III Educating the Odesys engineer 231

9 The art of Open Design Learning 233
9.1 Positioning the Odesys engineer . . . . . . . . . . . . . . . . . . . . 234
9.2 Open Design Learning (ODL) concept . . . . . . . . . . . . . . . . 239
9.3 ODL, an act of U-nlocking . . . . . . . . . . . . . . . . . . . . . . . 258

Open end 265

Conspection & outreach . . . . . . . . . . . . . . . . . . . . . . . . . . . 267
Connect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277
 vii

Appendices 279

A Research & Development Methods 281

B Linear versus non-linear optimisation 285

B.1 Linear optimisation algorithms . . . . . . . . . . . . . . . . . . 287
B.2 Non-linear optimisation algorithms . . . . . . . . . . . . . . . 288

C Preferendus Genetic Algorithm 293

C.1 Normalized scores . . . . . . . . . . . . . . . . . . . . . . . . . 293
C.2 Rank reversal . . . . . . . . . . . . . . . . . . . . . . . . . . . 294
C.3 Modifications to the Genetic Algorithm (GA) . . . . . . . . . . 294

D A-priori versus a-posteriori methods 297

D.1 A-posteriori methods . . . . . . . . . . . . . . . . . . . . . . . 297
D.2 A-priori methods . . . . . . . . . . . . . . . . . . . . . . . . . 298

E Choice matrix algorithms 301

Bibliography 303

About the author 311

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Opening
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Preface

’Odesys, a pure act of socio-technical design integration to confront

conflicts and dissolve problems with and for people.’

Why, so often,
.... do we build what nobody wants?
.... do engineers optimise their solutions based only on physical capabilities and
fail to consider the stakeholders’ desires?
.... do policy makers keep the decision-making process non-transparent and non-
participatory?
.... do conflicts stem from failed attempts to constructively design?
.... do we continue to democratically govern through past compromises instead of
socially designing future syntheses?

These are typical questions arising from real-life experiences within the public
space, our built environment and infrastructure management practices. The actual
answer to these questions is that socio-technical problems are often solved from a
one-sided point of view, without considering the fact that the problem is multi-
faceted. Misrepresenting this complex and interconnected problem nature results
in what we call ‘bridges to nowhere’ solutions. Therefore, a participatory process
that does justice to both the ’hard’ technical and ’soft’ social aspects within solv-
ing these problems is needed. It is thus crucial to truly connect and bridge the gap
between human preferences (‘desirability’) and system performances (‘capability’)
using transparent models for complex systems design and integration solutions
(‘feasibility’). These models offer unprecedented opportunities and ‘bridges to
anywhere’ solutions. Moreover, if stakeholders dare to confront their conflicts and
put their ‘cards’ openly on the table, pure best fit for common purpose design
solutions will become possible. Designing is thus a matter of conflict dissolution.
The state of the art design methodology Open Design Systems (‘Odesys’),
as introduced in this book, enables all the aforementioned and answers the above

3
4 PREFACE

questions. It is Odesys’ purpose to foster adoption of civil infrastructures that sur-

round us every day through a multi-system level socio-technical approach, suppor-
ted by sound mathematical open-glass box models as means of observation during
participatory design and collaborative decision-making. Here, systems thinking
and a stakeholder-oriented focus is required to search for different solutions within
an open-ended solution space, uniting both capability (technics) derived from the
system properties, and desirability (economics) derived from individual subject’s
objectives. This will result in an open dialogue and co-design approach that en-
ables a-priori best-fit for common purpose design synthesis dissolution, rather than
a-posteriori normative design compromise absolution. This makes Odesys a pure
socio-technical systems integration methodology that offers a wide range of multi-
objective design and decision making applications, uniting stakeholder preferences
(‘what a human wants’) and physical assets performances (‘what a system can
deliver’). Moreover, classical multi-objective design optimisation methods suffer
from fundamental mathematical flaws and do not provide a single best-fit design
configuration, but rather a set of design alternatives. This leaves designers without
a unique solution to their problems.
As part of this Odesys methodology, a new Integrative Maximised Aggregated
Preference (IMAP) optimisation method for maximising aggregated preferences is
introduced. According to classical decision theory, decisions are based on pref-
erence. Here, preference is an expression of the degree of ’satisfaction’, and it
describes the utility or value that something provides. That is why we trans-
late all the different stakeholder objectives (including money) into one common
preference domain to find a best-fitting aggregated optimum. The IMAP method
forms the basis of a new software decision support tool called the Preferendus, in
which individual stakeholder preferences, social objective- and technical design per-
formance functions are integrated into one aggregated preference function. Here,
Odesys combines the state of the art mathematical principles of Preference Func-
tion Modeling (PFM) with both the extended social threefolding theory and the
models from the organizational development theory-U. Odesys takes the math-
ematical application of the PFM theory a step further by extending it from an
a-posteriori evaluation to an a-priori design methodology. Moreover, it uses PFM
based multi-criteria decision analysis and the social threefolding theory to derive
the so-called Corporate Social Identity (CSI) indicator, which is an expression
to identify the socio-eco purpose and corporate responsibility of an organization.
Odesys also introduces a new threefold modeling framework linked with the open-
ended Odesys U-diagram, that connects the socio-technical design process, through
a three-layer metamorphosis of picture-purpose and prototype and incorporates
three open-ended design loops: (1) open config – technical cycle, (2) open space
-social cycle and (3) open source - the purpose cycle. Odesys’ added value and
use are demonstrated for design and decision applications within a real-life engin-
PREFACE 5

eering management and service provisioning context showing how to achieve pure
’best-fit’ for common purpose solutions.
First of all, this book can be used by academic colleagues working in the field
of complex systems design. The methodology is not limited to infrastructure and
building applications in public space, but Odesys and its IMAP/Preferendus can
also be used for a much broader range of participatory design and decision-making
problems. Problems that need to be solved within a socio-technical context, where
multiple stakeholders with different conflicting interests are seeking to arrive at
the best solutions for common solutions or where stalemate situations need to
be dissolved through transparent conflict management. Therefore, this book is
also of interest to industrial professionals working within public and/or private
organizations, where socio-technical decision-making can be both hardened and
opened by the Odesys principles and its Preferendus. Only this will enable organ-
izations or cooperating organizations, where old so-called democratic top-down
control decision-making processes govern, to transform themselves into sociocratic
bottom-up organizations where participatory and open-minded design processes
result in future-oriented socially responsible synthesis solutions.
Secondly, this book serves as the primary reference material for a substantial
number of TU Delft systems design and management courses for master students
(MSc) from diverse backgrounds. All of these integrative engineering and manage-
ment courses are being conducted along the principles of the state of the art educa-
tional ODL concept. ODL is a constructivist and design-based learning approach
(“learn to design by real-life designing”) where students actively develop new solu-
tions originating from their inner and outer designs. It forms the fundamental basis
for creating ‘open, integrative and persistent learners’ concerned about dissolving
future world problems. ODL, like Odesys, is not limited to education within a
technical context. It is in fact an educational concept that in principle can be
used in any discipline where there is an openness to apply design-based learning
to develop what does not yet exist, instead of instructivist research-based learning
which investigates what already exists.
Thirdly the name of this book, Odesys, is not just an abbreviation, but is in-
spired by Odysseus, who was a legendary Greek king of Ithaca and one of the most
influential Greek problem solving champions. To become a true Odesys engineer,
three typical sayings from the famous Odysseus stories might be companions on
your personal problem solving journey:
• ‘Find an Odysseus ruse, like the Trojan horse‘, meaning a creative way out
of a seemingly insoluble problem;
• ‘Be able to choose between Scylla and Charybdis‘, meaning how to find/
secure the golden mean even in the case where one has to (merely) balance
between ‘two evils’;
6 PREFACE

• ‘Make use of Cassandra information‘, meaning a prophecy of doom that later

proves to be correct and in particular based on true relevant information that
one should not or does not want to hear.
In closing, I would first like to sincerely acknowledge Ruud Binnekamp and
Harold van Heukelum for their valuable support, their co-creation activities and
their constructive feedback on designing and developing this book. I really enjoyed
our joint Odesys expedition. Moreover, the editorial help of students Lukas Teuber
and Matt Julseth was much appreciated. In addition, I wish you as a reader a true
Odesys journey that starts from an open-minded and ’con-scientific’ perspective,
a perspective that goes beyond a purely materialistic-physical one. Also, I wish
you much success in using Odesys & ODL in your specific context and discovering
its added value as spiritual mind and physical matter converge. I plead for a syn-
thesis solution of a dual gesture: outward opening and inner deepening to unite
the open design impulse. I invite you to contribute to this ongoing development
effort so that Odesys & ODL will not only mature, but also spread its wings to
cover domains other than just the engineering management domains. Finally, I
am convinced that everyone has a designer within themselves; it is the pure art of
Odesys & ODL to awaken this inner designer.

Delft, Prof.dr.ir. A.R.M. (Rogier) Wolfert

June 2023

open-design.school odesys.nl
rogier@odesys.nl
Contributions

In this section, we present the most important new Odesys contributions in this
book. These are deltas drawn in comparison with earlier important works by sev-
eral leading academicians within the field of socio-technical design, organizations
management, mathematical modeling within engineering physics and/or manage-
ment sciences, education- and/or research development (see the Bibliography for
their main reference works). Note that the author had the privilege of working
with some of them at TU Delft, the University of Nizhniy Novgorod, and within
various (inter)national workshops over the past 30 years. Partly based on their
inspiration, we have developed the significant contributions of Odesys which are
concisely summarized below.

Contribution (1)
Compared with earlier work of Ackoff, Van Gunsteren, Van Loon:
• Extension of the earlier open design principles by Van Gunsteren/Van Loon,
with a human oriented threefold decision making U-model, comprising of
a technical, social and a purpose cycle, enabling an idealized design meta-
morphosis ‘picture-purpose-prototype’.
• A full non-linear multi-objective design optimisation approach extended with
(a) systems’ capabilities and human desirabilities, (b) technical design per-
formance functions, as opposed to the linear program approach of objective
functions only, (c) extended towards different domains of application, as op-
posed to architecture only.
• An integrative social threefolding based Preferendus for collective decision
making, taking the collectivist utility based decision-making a step further.

Contribution (2)
Compared with earlier work of Barzilai:
• Extension of Barzilai’s Preference function modeling and measurement (PFM)
principles from a pure multi-criteria decision analysis (MCDA) approach to-
wards a multi-objective design optimisation (MODO) approach.

7
8 CONTRIBUTIONS

• A generalised mathematical threefold framework for multi-objective socio-

technical design optimisation: i.e., a threefold modeling framework of in-
tegrative performance, objective and preference functions supported by a
new optimisation method that enables the integrative maximisation of the
aggregated preferences (IMAP).
• A full intergenerational genetic algorithm (GA) solver to search for the in-
tegrative maximum aggregated preference using PFM principles.

Contribution (3)
Compared with earlier work of Blanchard, Dym, Fabrycky, Little:
• A pure socio-technical design methodology supported by the qualitative
Odesys U-model and the quantitative Preferendus which supports a design
synthesis and goes beyond the usual one-side technical design methodologies
as for example in the classical V-model.
• A new state of the art PFM based optimisation method IMAP that (a)
suffers from fundamental mathematical flaws (b) provides a single best-fit
design configuration, rather than a set of design alternatives following from
classical approaches.
• A new socio-eco common interest diagram which for the basis for the social
design cycle and design to best fit for common purpose. A translation and/or
connection between common socio-eco interests and (a) the individual pref-
erence function (stakeholders individual desires) and (b) design performance
functions (physical/mechanical object behaviour)

Contribution (4)
Compared with earlier work of Brüll, Glasl, Kahneman, Lievegoed, Scharmer:
• Extension of the Glasl’s/ Scharmer’s U-model and theory (a) to design and
learning & development (b) incorporation of Kahneman’s ‘thinking slow’
supported with open glass box modeling (c) integration of epistemological
and ontological U-model approaches.
• Extension of social threefolding principles and elaboration to a service pro-
vider with the so-called economic, isonomic, and ecologic sub-parts and its
socio-eco purpose. This also enables (a) the qualitative basis for the Prefer-
endus (b) to quantify and evaluate the Corporate Social Identity (CSI) using
the socio-eco purpose characteristics and PFM modeling.
• Quantitative support and transparent substantiation for Glasl’s qualitative
model of conflict escalation by confronting the conflict and the Preferendus
‘getting into yes’.
CONTRIBUTIONS 9

Contribution (5)
Compared with earlier work of Metrikine, Neimark, Vesnitskii:
• Extension of the use of mathematical models of systems dynamics, which
were primarily used to study wave dynamics phenomena in elastic systems
(e.g. transition radiation and dynamic system behavior), to an approach
for design optimisation. Incorporation of these physical system dynamics
models into the IMAP/Preferendus.
• Automated search algorithm to find the optimal design parameters given
different physical constraints and/or objective functions, including the in-
tegration surrogate modeling.
• Automated search for best-fit mitigation measures using an integrative ap-
proach of non-linear optimisation, probabilistic Monte Carlo simulation and
PFM for dynamic control on-the-run (incl. Discrete Event Simulating (DES)).

Contribution (6)
Compared with earlier work of Eekels, Heusser, Roozenburg, Zajonc:
• Extension of Eekels/Rozenburg’s R&D process flows, including a new 4-
Quadrants model to position Odesys within the empirical R&D context.
• An extended 4-Quadrant model (compared to the pure empirical one, and
elaborating Heussers’s call for a ’new’ science) to position Odesys within the
con-science context, including open-ended research questions for further self
schooling.
• Integration of the Glasl’s/Scharmer’s U-model with Zajonc’s principles of
the theory mind and its contemplative inquiry.

Contribution (7)
Compared with earlier work of Ackoff, Argyris, Biesta, Schön, Schieren:
• Extension of Steiner Waldorf education for Master students within the age
of 21+ (so far this education concept has only been developed for students
under 18-21 years of age).
• A new constructivist open design learning (ODL) concept that (a) educates
future problem solvers and persistent learners (b) goes beyond research and
inquiry based learning concepts such as organizational/experiential learning
(c) integrates the human learning & development process, viewed from the
general human (threefold) principles. This includes a new ODL U-model and
other new concepts like the ODL response, ODL commendation and so on.
• A new way of design-based learning where students choose their own System
of Interest (SOI) as an ODL learning vehicle, as opposed to a given and
predefined casus that has already been solved by the teachers (such as in
most of the existing PBL/CBL/CDIO education concepts). This includes
Odesys’ U-based modeling and the use of IMAP/Preferendus (’double-U’).
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Commendations

In this section, a number of collegial commendations are included from various

scientific and industrial fields. These are drawn from different perspectives with a
specific focus on parts of this book, such as design and decision science, mathem-
atical modeling, socially responsible systems design, conflict management, design-
based education, and industrial applications. Their substantive endorsements are
given below.

Commendation (1)
“Existing system design methodologies are one-sided because they ignore the dy-
namic interplay between preferences of the users (demand) and the physical per-
formance of the engineering aspects (supply). Moreover, classical multi-objective
optimisation methods contain fundamental modeling errors. Also, these classical
multi-objective optimisation methods do not offer a best-fit design point but rather
a set of design performance alternatives. This leaves designers without a unique
solution to their problems. Finally, current multi-objective optimisation processes
are rather disconnected from design and management practices because they lack
deep involvement of decision makers in expressing their conflicting interests in one
common preference domain.
To overcome these shortcomings, the author of this well-written book offers a new
open design system methodology and a novel integrative optimisation method which
is based on maximising the aggregated group preference. Their added value and use
are demonstrated in real-life design applications, which show how to arrive at a true
best fit for one common-purpose design. This ground-breaking work is based on the
highly original and effective Preference Function Modeling (PFM) methodology in-
troduced and studied by Barzilai. Wolfert and his colleagues have converted PFM
from an evaluation methodology into a design methodology, which I am certain will
be of great interest and value to theoreticians and practitioners alike.”

Simeon Reich (Doctor of Sciences)

Professor of Mathematics

11
12 COMMENDATIONS

Commendation (2)
“It is a real essential advance that Wolfert integrates within the Odesys meth-
odology, the ontological U-model we developed with my colleague Lemson of the
Netherlands Institute for Organizational Development (NPI) with the epistemolo-
gical and now widespread U-theory of Scharmer from MIT, into a holistic model of
great practical value for strategic management, organizational development, design-
based learning and conflict management. Particularly in my work as a mediator
in dramatically escalated multi-party political conflicts, de-escalation was found to
be easier by first finding a consensus on what the conflicting parties perceive as a
horrible and undesirable future to be prevented, before they could agree on positive
perspectives of a desired future and constructive ways to achieve it.
Odesys has the potential to truly connect stakeholders and bridge the gap between
their conflicting interests using transparent and participatory methods and models
to first de-escalate their complex problems and then provide shared solutions.“

Friedrich Glasl (Doctor of Sciences & honorary Doctor)

Professor of Conflict management & Organization development, founder of Trigon

Commendation (3)
“An important challenge of systems design, whether it concerns roads, airplanes
or government policies, is that it has to respond to engineering needs and wants
of many different stakeholders. More than ever, next to research, engineering and
management oriented institutes of higher education need to foster design capabilit-
ies. With a cutting-edge approach, embedded in a harmonious framing of pragmatic
design activity and scientific inquiry, this book provides a rigorous solution for
multi-stakeholder design problems. Wolfert further contributes with a construct-
ivist, experiential design learning approach that recognizes stakeholder preferences
and helps students to address socio-technical complexity in systems design.
I strongly recommend this volume to educators of design, engineering and man-
agement, to researchers interested in preference-based optimisation, and to practi-
tioners who are wondering how to create socially responsible systems.“

Lóri Tavasszy (Doctor of technical Sciences)

Professor of Logistics systems & Freight transportation

Commendation (4)
“The topic of integrating human preferences into system design optimisation is
important. Over the years, many methodologies were proposed and used to tackle
this issue. Nearly all of them suffered from some serious flaws caused by using
inadequate ways to quantify and measure human preferences. Wolfert and his
colleagues offer a novel and promising methodology to address the system design
COMMENDATIONS 13

challenge through the Preference Function Modeling (PFM) that was developed by
Barzilai over the last three decades. PFM was proven to overcome major flaws in
previously used methods and as such it can become a highly useful and effective
tool for future system designers seeking to take true account of the preferences of
various stakeholders involved in the design.”

Boaz Golany (Doctor of Sciences)

Professor of Data & Decision sciences

Commendation (5)
“Our contemporary engineering challenges must increasingly meet multiple object-
ives which even become more complex. Not only technical feasibility or safety is
required, but also economic feasibility, contractual compliance, social responsibility,
environmental management and other requirements must be met simultaneously.
Odesys has so far proved to be ideally suited for finding these best-fit solutions.
Wolfert and his colleagues bring a new perspective within this field of design op-
timisation and operational excellence. Their new Preference Function Modeling
(PFM) based design methodology Odesys, operationalised in the design and decision
support tool the Preferendus, has been applied to several industry use cases. The
Preferendus was capable of outperforming existing design/decision management
approaches to searching for the most optimal synthesis for multiple stakeholder,
ranging from planners, engineers, production managers and/or vessel captains.
The developments the author describes in this book are of great significance in
bringing the Odesys methodology to industrial value within a broad engineering
management context.“

Sander Steenbrink (Doctor of technical Sciences)

Director Corporate Research & Development at Boskalis
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Reading guide

This Open Design Systems (Odesys) book is split into three main parts:

I – Setting the Odesys scene. In the first part (Chapters 1-5), we set the
Odesys scene by first defining our frame of reference and Odesys’ key starting
points and paradigms, including a view on world and man. We then put forward
our perspectives on design and management within the context of science and
engineering. We also present our view on how to mathematically model design
and decision problems. In other words, we gradually zoom in from the science
and engineering context and the respective positions of design and management,
through the socio-eco management organization with engineering assets in order
to finally mathematically- and U-model the participatory design and decisions
related to these engineering assets considering the stakeholders’ conflicting socio-
eco interests.
Key topics that are introduced within this part are: (1) R&D methodologies
and the 4-Quadrants models to position Odesys (2) the socio-eco organisation
model as the basis for quality of service (QoS) engineering asset management (3)
social threefolding and its laws and principles as the basis for the participatory
socio-technical systems design integration and for the Corporate Social Identity
(CSI) (4) the socio-eco design to best fit for common purpose diagram (5) the ex-
tended Odesys U-models for open loops management, designing and learning (6)
preference function modeling (PFM) theory and its basic principles (7) PFM-based
multi-criteria decision analysis (MCDA) and multi-objective design optimisation
(MODO).

II – Odesys methodology and applications. In the second part (Chapters

6-8) we introduce the Odesys methodology, address the development gaps, and put
forward both the state of the art IMAP optimisation method and the Preferendus
design and decision support. We describe how Odesys and IMAP/Preferendus
overcomes other existing shortcomings compared with contemporary and single-
sided multi-objective design optimisation methods. Moreover, we present Odesys’
U-model, including a threefold modeling framework, which incorporates three

15
16 READING GUIDE

open-ended design loops: (a) open config – technical cycle, (b) open space - social
cycle, and (c) open source - the purpose cycle.
Key topics that are introduced within this part are: (1) Odesys’ mathematical
formulation (2) the function of IMAP and the Preferendus (3) the threefold optim-
isation framework of preference-, objective- and design performance functions (4)
the open-ended Odesys U-model and the technical, social and purpose cycles (5)
formative Odesys examples as learning vehicles (6) summative real-life Odesys ap-
plications as design demonstrators (7) validation of IMAP/Preferendus synthesis
with min-max compromise and or single-objective design solutions.

III – Educating the Odesys engineer. In the third part of the book we first
academically position the Odesys engineer as a true systems integrator within the
domains of scientific research and engineering development, closing the loop with
Chapter 2. Next, with the knowledge of the required integrative position of the
Odesys engineer, we present a fully congruent and new education concept with this,
called Open Design Learning (ODL). With this we close the loop with Chapters
3 and 4, and introduce in addition to the open loop management and the open
design U-models, a third U-model but this time for the ODL concept (ODLc).
Key topics that are introduced within this part are: (1) the 4-Quadrant model
applied (2) the position of the Odesys engineer within both 4-Quadrant models
(3) the key principles of the ODL concept (4) the ODL U-model as the basis for
design learning.

We consider this book to be a never ending work in progress and therefore ”open
end” the book with a section on further developments and outreach. Linked to
current Odesys conclusions, we outline the scope and potential for confronting the
conflicts in various applications, including stalemate situations. Finally, the book
is larded with so-called incitements (mainly included in Parts I and III) . These
are contextual opening questions or problems to spark the reader’s imagination
for a particular topic, create awareness or provide food for thought.
Last but not least, all quantitative examples or design applications from Chapters
5, 7, and 8 have been worked out and can be found on the Odesys Github:
github.com/TUDelft-Odesys/.
 Part I

Setting the Odesys scene

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Chapter 1

Frame of reference
Before we introduce the open design system (Odesys) methodology, we first set the
scene for the most important concepts, terms, principles, and definitions. These
will be specifically coloured for their prominent place within the Odesys and open
design learning (ODL) context. For the sake of clarity and to prevent confusion,
we list these as a concise ’portal’, and as used in this book.

1.1. Design, science, engineering & management

We will introduce the following concepts of design, engineering, and management,
and place them in the scientific research and development context. First, we will
define the ’broad’ interpretation of design within the Odesys context.

Design
To design is to imagine and specify things that do not exist, usually with the
aim of bringing them into the world. The “things” may be tangible – machines
and buildings and bridges; they may be procedures – the plans for a marketing
scheme or an organization or a manufacturing process. Virtually every professional
activity has a large component of design, although usually combined with the tasks
of bringing the designed things into the real world. So how does a designer reason?
In a certain sense, design is the opposite of induction. The aim of induction is
theoretical knowledge; design is aimed at a functioning thing. Induction is a
process of abstraction and designing a process of concretization: an innoduction
process, see Roozenburg & Eekels (1995). In contrast to research, designing is a
future-oriented action, where a new articulation is created from the unknown (i.e.,
de-sign: ‘a not yet drawn mark’). The core of design, then, is the transformation
of the functional needs of a new artefact into the description and manifestation of
its true meaning and form.
In addition to its meaning as a verb (to design or designing), design can also be
used as a noun. Design means (in a non-artistic sense) a plan or scheme in the

19
20 CHAPTER 1. FRAME OF REFERENCE

mind (inner) for a potential realization in the observed world (outer). According
to Steiner (1995), development purpose is related to the inner human motive (i.e.,
‘the impulse that gets you in motion/ gets you motivated’) that transforms a de-
sire via an intent into an inner and/or outer design. According to Ackoff (2006),
the so-called intentional or ‘idealized design’ serves as the motivator/stimulator
for the devise and design process. A true living dialogue in the space between
subject and object creates an open space where new designs can emerge. In the
words of Goethe: ”only human can sense the interactive experience experiment as
the mediator between subject and object”.

Incitement 1.1 Design, a broad perspective

(Prof. Herbert Simon’s research was noted for its interdisciplinary nature such as cognitive
science, computer science, public administration, management, and political science. He
won a Nobel Prize & Prof. Russell Ackoff was a pioneer in the field of operations research,
systems thinking and management science).

For Simon and Ackoff, design is problem solving, is tinkering with artefacts. So, it was his
key ambition to have design driven curricula. One can imagine a future in which our main
interest in both science and design will lie in what they teach us about the world and not
what they allow us to do in the world. Design like science is a tool for understanding as well
as acting.
Moreover, Simon saw design theory as a bridge to connect ‘epistemic communities’ that are
usually disconnected. In essence, composers, medical professionals, engineers, and managers
are all doing the same thing. They are designing, i.e. they are ‘devising courses of action
aimed at changing existing situations into preferred ones.’ Understanding the core under-
lying problem solving processes would enable these professionals to engage in meaningful
conversation.
In everyday life, we solve all kinds of problems. Not just unpleasant ones but also fun ones.
Consider the following travel commercial. ”From the moment you leave for the airport to
the moment you arrive home after your holiday, we have organised your trip. Flights, car
hire, transfers, activities, accommodations, meals, and stopovers. We can put together your
personalised holiday, so you don’t have to worry about anything in Australia. We have a
perfect plan in mind for you!” So, is a travel agency actually also a co-design office?

The following formal definition of engineering design is the most useful in the
open design systems context, see Dym (2013): ‘Engineering design is a systematic,
intelligent process in which engineers generate, evaluate, and specify solutions for
devices, systems, or processes whose form(s) and function(s) achieve ‘stakeholder’
1.1. DESIGN, SCIENCE, ENGINEERING & MANAGEMENT 21

objectives and users’ needs while satisfying a specified set of constraints. In other
words, engineering design is a thoughtful process for generating plans or schemes
for devices, systems, or processes that attain given objectives while adhering to
specified constraints’. For further reading, see Ackoff (2006); Bohm (1994); Dym
(2004); Roozenburg & Eekels (1995); Scharmer (2016); Steiner (1995).

Science
Science, any system of knowledge that is concerned with the physical world and its
phenomena and that entails unbiased observations and systematic experimenta-
tion. In general, science involves a pursuit of knowledge covering general truths or
the operations of fundamental laws. Science can be divided into different branches
based on the subject of study. The physical sciences study the inorganic world
and comprise the fields of astronomy, physics, chemistry, and the earth sciences.
Social sciences like anthropology, psychology, management, and economics study
the social and cultural aspects of human behavior. For further reading, see Bohm
(1994); Bortoft (1996); Heusser (2016/2022); Roozenburg & Eekels (1995); Simon
(2019).
Incitement 1.2 Mind and matter

(Prof. Erwin Schrodinger, physicist and Nobel Prize

winner in physics)

What is life, mind, and matter?

“..And thus at every step, on every day of our life, as

it were, something of the shape that we possessed un-
til then has to change, to be overcome, to be deleted
and replaced by something new. The resistance of our
primitive will is the psychical correlate of the resist-
ance of the existing shape to the transforming chisel.
For we ourselves are chisel and statue, conquerors and
conquered at the same time it is a true continued ’self-
conquering’ (Selbstüberwindung)...”

Engineering
Engineering is the pursuit of optimum conversion of the resources of nature to
the purpose of humankind. The field has been defined as the creative process to
design or to develop structures, machines, apparatus, or manufacturing processes,
or works utilizing them singly or in combination; or to construct or to operate the
same with full cognisance of their design with respects to an intended fit for pur-
pose (quality of service). The words engine and ingenious (i.e., ‘inborn nature’) are
derived from the same Latin root, in-generare/in-gignere, which means “to create
22 CHAPTER 1. FRAME OF REFERENCE

or generate / to give birth.” The early English verb engine meant “to contrive.”
Thus, the engines of war were devices such as catapults, floating bridges, and
assault towers; their designer was the “engine-er,” or military engineer. The coun-
terpart of the military engineer was the civil engineer, who applied essentially the
same knowledge and skills to designing buildings, streets, water supplies, sewage
systems, and other projects. For further reading, see Blanchard & Fabrycky (2011);
Dym (2013); Hastings (2014); Wasson (2015).

Management
Management (or managing) is the art and science of managing resources of a pro-
ject or service providers. Management is setting the strategy of these organizations
and coordinating the efforts of its people to accomplish its objectives through the
efficient and effective application of available resources, such as financial, natural,
technological, and human resources. Two concepts are used in management to
differentiate between the continued delivery of products or services and adapting
of products or services to meet the changing user needs. The term ”management”
may also refer to those people who manage an organization: managers. Because
the term management is often confused with leadership, the following explains the
difference between the two.

Incitement 1.3 Explanations using systems thinking

Take an automobile for example which is a simple mechanical system that you are all familiar
with. Why is the motor in the front? Well, you probably know the reason, it was because
it was originally called the horseless carriage. Therefore the motor was put where the horse
was in front of the cart.
Do you think that somebody that did not know that can find that out by taking the auto-
mobile apart? The automobile was originally a six passenger vehicle, why? Why was it not
five and not four, fifteen, or nine, why was it six? Will taking it apart tell you? Of course
not.
How many of you have ever been to Britain? You know they drive on the wrong side of the
road, why? Do you think that taking British cars apart is going to tell you why they drive
on the left and we drive on the right? Of course not.
Seemingly questions about objects called systems cannot be answered by the use of detailed
analysis (only).
1.2. SYSTEMS ENGINEERING & THINKING 23

Management versus leadership A manager is usually focused on controlling

or dealing with situations, matter or people within the workplace which often
involves constantly reassessing and adjusting results to measure efficiency and
improve effectiveness. Note that management differs from leadership. Leadership
tends to focus more on increasing fit for common purpose by motivating, inspiring,
and encouraging others to pursue an idealized design rather than ensuring tasks
are completed through management. So, management focuses on optimising the
planning and execution of a process (fitness for only on effectiveness and efficiency,
rather than fit for purpose), while leadership focuses on optimising a system as
a whole (focus on fitness for purpose). For further reading, see Argyris & Schön
(1995); Bower (1997); Scharmer (2016); Schön (1987); Senge (2006).

1.2. Systems engineering & thinking

Systems engineering is an interdisciplinary field of engineering and engineering
management that focuses on how to design, integrate, and manage complex sys-
tems over their life cycles. At its core, systems engineering utilizes systems thinking
principles to organize this body of knowledge and products. The field of systems
engineering is related to systems thinking, and (open) systems theory.

System
A system is a group of interacting or interrelated elements that act according to a
set of rules to form a unified whole, from Greek συστ εµα, organized whole, a whole
compounded of parts or a sum of the vital processes in an organism. A system
is composed of sub-systems: e.g., in case of technical systems the sub-systems are
the engineering assets (and its physical subsystems) and in organizational/social
systems these could be the (sub)departments (and people). One could even fur-
ther zoom-in to object/component or subject/person level respectively. A system,
surrounded and influenced by its environment, is described by its boundaries,
structure and purpose and expressed in its functioning: the so-called embedding
systems dimensions. Systems can be further classified and discerned as open and
complex systems.
System classification A system can represent both physical and non-physical
artefacts. Physical systems can be technical and/or mechanical systems comprising
of engineering assets and their components. These systems, also sometimes called
deterministic systems, are characterized by the following features (a)’integrate
the parts’ (b) ’are causal’ (c) neither the parts nor the whole are purposeful.
On the contrary, non-physical systems can be social and/or biological systems
comprising of their living (sub)parts. These systems, sometimes also called organic
or human (related) systems, are mostly characterized by the following features (a)
24 CHAPTER 1. FRAME OF REFERENCE

’differentiate from the whole’ (b) ‘goal-oriented’ (c) both the parts and the whole
are purposeful, see Figure 1.1.
So, we can distinguish between systems consisting of living elements (subjects) and
dead elements (objects or engineering assets). A special subclass of social systems
are so called management systems. A management system is a set of policies,
processes, and procedures used by a human or an organization to ensure that it
can fulfill the tasks required to achieve its purpose and goals. Typical examples
are planning-, information-, safety-, and/or organization systems.

Figure 1.1: Purposive metamorphosis.

In addition, something remarkable can be observed when you consider a ‘dead’

mechanical system next to an organic ‘living’ system. For it is common knowledge
that nature behaves as a threefolding system and a mechanical system mostly
shows itself as a twofolding system, see Figure 1.2.

Figure 1.2: Twofold material/mechanistic system (digital ‘under-nature’).

An interesting fact when comparing multiple living threefolding systems is that

there can be a so-called inversion principle, see Lievegoed (1996) and/or Figure 1.3
This means that if you compare plants and humans with each other, for example,
they are actually a mirror image of each other and we will see this mirror image
again later in organisational development within the context of society.
1.2. SYSTEMS ENGINEERING & THINKING 25

Figure 1.3: Threefold living/organic system (nature) and its inversion principle.

Complex systems A complex system is a system composed of many objects

and/or subjects which may interact with each other. Examples of complex sys-
tems are organisms, infrastructures such as transportation or energy systems, soft-
ware systems and/or social organizations (like companies or cities). Systems that
are ‘complex’ have distinct properties arising from relationships or interactions
between their sub-systems or between a given system and its environment, such as
non-linearity, emergence, adaptation, and control loops, amongst others. A special
class of complex systems are socio-technical systems. A socio-technical system is
the term usually given to any combination of social and technical elements that
exhibit purposeful behavior. Participative socio-technical system design is the key
of this book.
Open versus closed systems The open systems that we know of are systems
that allow interactions between their internal elements and the environment. An
open system is defined as a “system in exchange of matter with its environment,
presenting import and export, building-up and breaking-down of its material com-
ponents. Closed systems, on the other hand, are held to be isolated from their
environment. Equilibrium thermodynamics or dynamics of mechanical systems,
for example, are fields of study that applies to closed systems. In social sciences,
schematically, if there is an interaction or feedback loop between ideal and mater-
ial or subjective and objective then the system is an open system, otherwise it is
a closed system. A closed system offers a deterministic relationship. The idea of
open systems was further developed in systems theory.
26 CHAPTER 1. FRAME OF REFERENCE

Incitement 1.4 Systems’ emergence

Avalanches often occur in spots where

the layers of snow are unstable. Cu-
mulative forces build and push one an-
other to extremes, at which point they
can release unprecedented power in the
mere blink of an eye. Whereas natural
phenomenon can cause irreparable dam-
age, this ‘avalanche’ rolls itself back up
as if nothing has happened. Interlocking
or uncovering rings regather themselves
into emerging ‘mountains’.
From the exhibition Momentum 2020 in Museum Voorlinden, by artist
Zoro Feigl, see youtu.be/LEe4KiNSTHQ.

Systems theory
Systems are the subject of study for systems theory and other systems sciences.
Systems theory is the interdisciplinary study of systems, i.e. cohesive groups of
interrelated, interdependent parts that can be natural, social, or human-made.
General systems theory is about developing broadly applicable concepts and prin-
ciples, as opposed to concepts and principles specific to one domain of knowledge.
It distinguishes dynamical or active systems from static or passive systems. Active
systems are activity structures or components that interact in behaviors and pro-
cesses. Passive systems are structures and components that are being processed.
For example, a program is passive when it is a file and active when it runs in
memory. Every system is bounded by space and time, influenced by its environ-
ment, defined by its structure and purpose, and expressed through its functioning.
A system can be more than the sum of its parts if it expresses synergy resulting
in emergent behavior.
Synergy, symbiosis, synthesis The term synergy originates from the Greek
synergos, “working together”, symbiosis from the Greek symbiosis, “living to-
gether” and synthesis from the Greek syntithenai, ”to put together”. Let us
start by defining symbiosis. Symbiosis is any form of close and long-term bio-
logical interaction between two different biological organisms (with different prop-
erties/species). From this “living apart/separately but together” (i.e., a LAT
relationship) emergence (also called system articulation) can occur. Or in other
words, symbiotic life emerges from coexistence (e.g. think of the unique human
speech formation or music, a coexistence of tones, rhythm, timbre through which
music and speech experiences ‘emerge’). Primal dune formation that begins with
rippling patterns on the flat beach, created by wind or water, are an example of
1.2. SYSTEMS ENGINEERING & THINKING 27

an emerging structure in nature. Other concepts which are closely related are
synergy and synthesis. We will use synthesis in the context of design and symbi-
osis/synergy in the case of management and organisations. In all such cases, there
is emergence because from the fusion of the parts an extra ‘invisible’ dimension can
emerge so that it holds that the ’whole is greater than the sum of its parts’. Note
(1): for all these ‘syn-’ concepts, multiple parts (greater equal two) can converge
to emergence. We will use synthesis as an emergence of two and synergy as an
emergence of three or more. Note (2): sometimes this concept is also applied to
thinking or conversation between people. In this case, one refers to dialoguing or
dialectical thinking in which through the ’union’ of thesis (one) and antithesis (the
other) a new synthesis emerges, see Buber (2004) or Hegel (2021). See Figure 1.4
and/or Figure 1.5 for an abstract representation of ’syn-concepts’ and emergence
and/or some typical emergent behavior examples, respectively.

Figure 1.4: Conceptual representations of symbiosis, synergy, synthesis and emergence: a (perpetual)
force of life.

Emergence (or emergent behavior) is a common feature of complex systems,

which are characteristics of a system that are not apparent from the components
individually, but emerge from the interactions, dependencies, or relationships they
form when brought together in a system. Emergence broadly describes the ap-
pearance of such behaviors and properties, and has applications to both social
and technical systems. It was already recognised by Aristotle (i.e., the ‘1+1+1=4’
effect). Let us continue with some other high-level systems theory principles.
Changing one part of a system may affect other parts or the whole system. It
may be possible to predict these changes in patterns of behavior. For systems that
learn and adapt, the growth and the degree of adaptation depend upon how well
the system is engaged with its environment. Some systems support other systems,
maintaining the other system to prevent failure. The goals of systems theory are
to model a system’s dynamics, constraints, conditions, and to elucidate principles
(such as purpose, measure, methods, tools) that can be discerned and applied to
other systems at every level of nesting, and in a wide range of fields for achieving
28 CHAPTER 1. FRAME OF REFERENCE

optimised equifinality. See Figure 1.4 and/or Figure 1.5 for an abstract repres-
entation of syn-concepts and emergence and/or some typical emergent behavior
examples, respectively.
Dynamical systems In mathematics, a dynamical system is a system in which a
function describes the time dependence of a point in an ambient space, such as in
a parametric curve. In physics, a dynamical system is described as a ”particle or
ensemble of particles whose state varies over time and thus obeys differential equa-
tions involving time derivatives”. These can be either linear or non-linear systems
with related differential equations. In linear systems, the effect is always directly
proportional to the cause. In non-linear systems, a small perturbation may cause
a large effect (see butterfly effect), a proportional effect, or no effect at all. At any
given time, a dynamical system has a state representing a point in an appropriate
state space. A state variable is one of the set of variables used to describe the
behavioral state of a dynamical system, also known as a system’s configuration.
As an example think of a car’s throttle position that, as a variable, determines the
car’s overall speed. In case of a manual gearbox the overall speed also depends
on the selected gear, which is another variable. Note that system dynamics (as
opposed to dynamical systems) is an approach to understanding the non-linear be-
havior of complex systems over time using stocks, flows, internal feedback loops,
table functions, and time delays. Originally developed for organizations to improve
their business processes, system dynamics is currently being used throughout the
public and private sector for policy analysis and design.

Systems thinking
Systems thinking is an approach that views an issue or problem as part of a wider,
dynamic system. It entails accepting the system as an entity in its own right rather
than just the sum of its parts, as well as understanding how individual elements
of a system influence one another.
When we consider the concepts of a car or a human being, we are using a systems
thinking perspective. A car is not just a collection of nuts, bolts, panels and wheels.
A human being is not simply an assembly of bones, muscles, organs and blood.
The notion that the system as a whole has properties provided by the elements
that individual elements cannot provide is called emergence: i.e., ‘the whole is
more than the sum of its parts’ (see also the previous subsection). In a systems
thinking approach, as well as the specific issue or problem in question, you must
also look at its wider place in an overall system, the nature of relationships between
that issue and other elements of the system, and the tensions and synergies that
arise from the various elements and their interactions. According to Ackoff (1999),
systems thinking combines analysis (zooming in) and synthesis (zooming out) in
a three step process:
1.2. SYSTEMS ENGINEERING & THINKING 29

1. Identify a containing whole (system) of which the thing to be explained is

part.
2. Explain the behavior of properties of the containing whole.
3. Then explain the behavior or properties of the thing to be explained in terms
of its role(s) or function(s) within its containing whole.

Figure 1.5: Emergence and systems articulation (here different types of ‘form’ emergence).

A kind of similar way of systems thinking is, according to Simon (2019), the no-
tion of properties of so-called ‘hierarchical systems’. These are systems that are
composed of interrelated subsystems, each of them being in turn hierarchic in
structure until some lowest level of elementary subsystem is reached (for instance,
animals including organs including tissues including cells). This hierarchy offers
significant advantages in dealing with external complexity. Infrastructures and/or
organizations, for instance, are hierarchically constructed artefacts created by hu-
man beings to navigate in an efficient and effective way. A good example of the
use of systems thinking within the context of engineering asset management is the
innovative multi-system intervention 3C concept, which was developed by Wolfert
about a decade ago and has since been successfully applied, see Kammouh et al.
(2021). Let us conclude with two other important concepts of systems thinking.
Phenomenology A special type of systems thinking is part of the phenomeno-
logical or Goetheanian science in which Goethe discerned a hidden relationship of
system parts that explains how one form can transform into another form while
being part of an underlying archetypal form or primeval phenomenon (Ur-phäno-
men). So, he suggested that researchers and developers seek the natural, lawful
organizing ideas or archetype pattern behind specific natural phenomena or within
(living) organic systems. They must adopt a more living, more humane, experien-
tial approach aspiring to enter into the living essence of the living system zoomed
into its primeval phenomenon (i.e., which appears or is seen as the basic element).
The experimenter aspires to allow the phenomena to reveal its inherent order and
system laws. While often invisible, these system laws are clearly objective, not
subjective, and not invented by the experimenters. Goethe intuited the practice of
rational science promoted a narrowing and contracting interplay between human-
ity and nature. A special and typical phenomenological or Goetheanian archetype
is the human threefold of: (1) empiric (sense nerve); (2) rhythmic (heart lung);
30 CHAPTER 1. FRAME OF REFERENCE

(3) metabolic (organ limbs), which can be zoomed in and out within the human
system including the use of the principle of self-similarity, see e.g. Heusser (2016).
Self-similarity A well-known concept from biology or mathematics implies the
following. A self-similar object looks exactly or approximately like a part of it-
self, or in other words, the system as a whole has the same shape as one or more
of its parts. Many objects in the real world are self-similar: parts of them ex-
hibit the same properties at many scales. Self-similarity is a typical property of
fractals, or spiral shapes and patterns (such as Romanesco broccoli which exhib-
its strong self-similarity). A special example of ’self-similarity’ is the so-called
Droste effect, see Figure 1.6. Finally, the Sierpiński triangle (sometimes spelled
Sierpinski), is a fractal attractive fixed set with the overall shape of an equilateral
triangle, subdivided recursively into smaller equilateral triangles (i.e., principle of
self-similarity). This is one of the basic examples of self-similar sets—that is, it
is a mathematically generated pattern that is reproducible at any magnification
(zooming out) or reduction (zooming in). For further reading see, Ackoff (1999);
Bortoft (1996); Heusser (2016); Velmans (2017); Varela (2017).

Figure 1.6: Self-similarity and the Droste effect.

Problem solving & systems thinking

Ackoff (1991, 1999) identifies four methods of ‘problem solving’: 1) Absolve, 2)
Resolve, 3) Solve, and 4) Dissolve. His point was that the further you take your
problem solving the more likely you will make the problem go away forever. There
is even an extra twist: Absolving and Resolving do not count as solving the prob-
lem. Solving, though valid, involves current knowledge. If there is a known solution
one shouldn’t have to waste their time on it. As a manager, a paid problem solver,
only Dissolving problems is a worthwhile use of time. Dissolving requires an open
1.2. SYSTEMS ENGINEERING & THINKING 31

and non-conformist manager that is interested and connected to unknown prob-

lems and has the ability to develop new knowledge and prospective expertise for
a resilient future.

Incitement 1.5 An ups and downs elevator story

The manager of a large office building had been receiving an in-

creasing number of complaints about the building’s elevator ser-
vice, particularly during rush hours. When several of his larger
tenants threatened to move out unless this service was improved,
the manager decided to look into the problem.
He called on a group of consulting engineers who specialized in
the design of elevator systems. After examining the situation, they
identified three possible courses of action: 1) add elevators, 2) re-
place some or all of the existing elevators with faster ones, or 3) add
a central computerized control system so that the elevators could
be ‘routed’ to yield faster service.
The engineers then conducted cost-benefit analyses of these altern-
atives. They found that only adding or replacing elevators could
yield a large enough improvement of service, but the cost of doing
either was not justified by the earnings of the building. In effect,
none of the alternatives was acceptable. They left the manager
with this dilemma.
The manager then did what a manager seldom does when he is
anything less than desperate: he consulted his subordinates. He
called a meeting of his staff and presented the problem to them
in the formal of what he called a ‘brain-storming’ session. Many
suggestions were made, but each was demolished. The discussion
slowed down. During a lull the new young assistant in the personnel
department, who had been quiet up to this point, timidly made a
suggestion. It was immediately embraced by everyone present. A
few weeks later, after a relatively small expenditure, the problem
had disappeared. Full-length mirrors had been installed on all the
walls of the elevator lobbies on each floor. The young personnel
psychologist had reasoned that the complaints originated from the
boredom of waiting for elevators. The actual waiting time was quite
small, but it seemed long because of the lack of anything to do while
waiting. He gave people something to do: look at themselves and
others (particularly of the opposite sex) without appearing to do
so. This kept them pleasantly occupied.

A systems thinking approach to problem solving recognizes the problem as part of

a bigger system and addresses the whole system in any solution rather than just
the problem area. A popular way of applying a systems thinking approach is to
examine the issue from multiple perspectives, zooming out from single and visible
elements to the bigger and broader picture. Systems thinking is best applied in
32 CHAPTER 1. FRAME OF REFERENCE

fields where problems and solutions are both high in complexity. According to
Simon (2019), ‘problem solving’ is design, that is tinkering with artefacts, and
thus the ‘sciences of the artificial’ is a meta-design theory to solve problems.
For further ’systems’ reading, see Ackoff (1999); Blanchard & Fabrycky (2016);
Bohm (1994); Dym (2013); Glasl & Lievegoed (2016); Lievegoed (1991); Lorenzelli
(1995); Neimark, (1978, 2003); Senge (2006); Simon (2019); Thom (2019).

1.3. Modeling & models

Modeling allows for better understanding of a problem and presents a means for
manipulating the situation to analyze the results of various inputs (“what if” ana-
lysis) by subjecting it to a changing set of assumptions. A model is an abstraction
of reality or a representation of a real object or situation. In other words, a model
represents a simplified version of something. It may be as simple as a drawing
of house plans, or as complicated as a miniature but functional representation of
a complex piece of machinery. A more usable concept of a model is that of an
abstraction, from the real problem, of key variables and relationships. These are
abstracted in order to simplify the problem itself.
Some models are replicas of the physical properties (relative shape, form, and
weight) of the object they represent. Others are physical models but do not have
the same physical appearance as the object of their representation. A third type
of model deals with symbols and numerical relationships and expressions. Each
of these fits within an overall classification of four main categories: physical mod-
els, schematic models (e.g. a logical diagram), verbal models, and mathematical
models. Of particular interest to Odesys are the mathematical models.

Mathematical models
Mathematical models are perhaps the most abstract of the four classifications.
These models do not look like their real-life counterparts at all. Mathematical
models are built using numbers and symbols that can be transformed into func-
tions, equations, and formulas. They also can be used to build much more complex
models such as matrices or linear programming models. The user can then solve
the mathematical model (seek an optimal solution) by utilizing simple techniques
such as multiplication and addition or more complex techniques such as matrix
algebra or Gaussian elimination.
Mathematical models can be classified according to their use (description or op-
timisation), degree of randomness (deterministic and stochastic), and degree of
specificity (special or general). Of particular interest to Odesys are the mathem-
atical optimisation models.
1.4. MULTI-OBJECTIVE OPTIMISATION 33

Mathematical optimisation models

An optimisation model is a mathematical representation of a real-world problem
that is made up of three key features: 1) decision variables that define the degrees
of freedom, 2) constraints that define boundaries that have to be respected, and
3) objectives that define the various (and often conflicting) goals to be achieved.
Within the optimisation procedure a search is carried out to find the optimal con-
figuration of decision variables that does not violate the constraints and performs
best in relation to the objectives.
For general ’modeling’ reading, see Ackoff (1991, 1999); Barzilai (2022); Blanchard
& Fabrycky (2011), Neimark (2003) amongst many others.

1.4. Multi-objective optimisation

Multi-objective optimisation is an area of multiple criteria decision making that
is concerned with mathematical optimisation problems involving more than one
objective function to be optimised simultaneously. Multi-objective optimisation
has been applied in many fields of science, including engineering, economics and
logistics where optimal decisions need to be taken in the presence of trade-offs
between two or more conflicting objectives. Minimising cost while maximising
comfort while buying a car, and maximising performance whilst minimising fuel
consumption and emission of pollutants of a vehicle are examples of multi-objective
optimisation problems involving two and three objectives, respectively.
For general reading on engineering design optimisation, see e.g. Hillier & Lieber-
man (2020); Martins & Ning (2021) amongst many others.

Optimisation problem formulation

In general, an optimisation problem can be mathematically represented in the
following way:
Given: a function U (x, y) : A → R from some set A to the real numbers, where A
is constrained by y.
Sought: an element x0 ∈ A such that f (x0 ) ≤ f (x) for all x ∈ A (”minimisation
objective”) or such that f (x0 ) ≥ f (x) for all x ∈ A (”maximisation objective”),
where x or x0 are design/decision variables.

Design/decision variables
The variables x that the designer or decision maker has control over. A given set
of these variables x ∈ A values determine the state of the design/decision system
and is considered a design configuration. Design variables determine the degrees
of freedom of the design/decision system.
34 CHAPTER 1. FRAME OF REFERENCE

Constraints
In mathematics, a constraint is a condition of an optimisation problem that the
solution must satisfy: x ∈ A, where A is constrained by the domain conditions y.
The set of candidate solutions that satisfy all constraints is called the feasible set.
So, in other words, constraints are a fixed set of requirements which cannot be
violated in a given problem formulation. Constraints divide all possible solutions
in two groups: feasible and infeasible. The set of candidate solutions that satisfy
all constraints is called the feasible set. Constraints, when related to the social
science (subjects/ stakeholders), can be considered negotiable: soft constraints.
Contrary, constraints that are related to the natural sciences (objects/engineering
assets) are not negotiable: hard constraints. Note that in modern mathematical
language, the domain A is part of the definition of a function rather than a property
of it.

Objective function
The function U is called an objective function, where the objective is a goal-
oriented requirement which is to be followed to the greatest extent possible (either
by minimisation or maximisation) given the problem’s constraints. The objective
function is called a preference function or fitness function for maximisation and
a loss function or dissatisfaction function for minimisation. A feasible solution
that minimises or maximises, if that is the goal, the objective function is called an
optimal solution.

Object & subject

An object is something that is tangible and within the grasp of the senses. In
physics, a physical body or object is an identifiable collection of matter.
A physical object, in engineering, is often called an engineering asset. A subject
is a human, or a person, who has individual preferences, who makes his/her own
decision and has a free will.

Solution space
The set of feasible design/decision alternatives: i.e., a set of possible candidate
solutions, as defined by the constraints y (within the constrained domain A). The
domain A of U is called the search space or the choice set, while the elements of A
are called candidate solutions or feasible solutions (i.e., here A is the intermediate
space spanned by the overlap of both the capability space (what the object can
provide), and the desirability space (what the subject considers desirable). Note
that this set can be empty in which case the design/decision making problem is
infeasible.
1.4. MULTI-OBJECTIVE OPTIMISATION 35

Measurement
By an empirical system E we mean a set of empirical objects/subjects together
with operations (i.e., functions) and possibly the relation of order which character-
izes the property under measurement. A mathematical model M of the empirical
system E is a set with operations that reflect the empirical operations in E as well
as the order in E when E is ordered. A scale s is a mapping of the objects/subjects
in E into the objects in M that reflects the structure of E into M. Measurement is
the mapping of an empirical system E into a mathematical system M. The purpose
of modeling/mapping E by/into M is to enable the application of mathematical
operations on the elements of the mathematical system M (see Barzilai (2022)).

Preference
Preferences are central to design/decision theory because of the relation to human
behavior. Preference literally means ”to esteem or value (something) more than
others, set before others in liking or esteem” and directly from Latin praeferre
”place or set before, carry in front,” from prae ”before” + ferre ”to carry”. So,
preference is a measure of human desirability. Preference is an expression of the de-
gree of ‘satisfaction’ or ‘well-being’, and it describes the utility or value something
provides. In other words, preference is a statement of an individual stakeholder’s
interest and a measurement of satisfaction (ophelimity): i.e. the fitness for pur-
pose. Preference is also synonymous to choice/decision as one chooses/decides for
those objects that one prefers (i.e. one prefers A over B expressed as A ≻ B).
The meaning of preference scores can only be derived from their relative position
for which the ratio of differences is a real number expression. Scores are expressed
as real numbers (scalar or bare quantity) on a defined scale from, for instance
0 to 100, where 0 is mapped to the ‘worst’ alternative and 100 is mapped to the
‘best’ alternative. Note that at least three alternatives are needed for the construc-
tion of proper preference scales that enable mathematical operations, for details
see Chapter 5. The mathematical preference modeling foundations, including the
economic theory, are laid down in Barzilai’s preference function modeling theory.
Moreover, the interested reader is also referred to Lacan’s psychoanalytic model of
desirability/subjectivity, see Desmet (2019). As conative states, desires are closely
related to preferences. The difference between the two is that desires are directed
at one object while preferences concern a comparison between different objects,
of which one is preferred over the others. A desire (i.e., a moral wish rather than
an instinct driven craving) is transformed via an intent/interest into a preference-
based decision/design, see Steiner (1996). Note that money is not a property of an
object but relates to a human’s willingness to exchange money to satisfy desires
related to the acquisition of the object and thereby a measurement of preference.
Also note that economics in essence is all about balancing the fitness for purpose
36 CHAPTER 1. FRAME OF REFERENCE

quality between supply and demand (object-subject). For further reading, see the
pure economics work of Barzilai (2022) and/or Desmet (2019).

Preference function
A function that relates objective design/decision variables (i.e., physical proper-
ties) values to subjective (i.e., psychological properties) preference ratings. This
function links the social sciences domain (human subject) to the natural sci-
ences domain (physical object). The correct mathematical modeling of preferences
within the context of proper measurement scales are found in the pure economics
work of Barzilai (2022). Note that principles and foundations of this novel theory
are summarised as the preference function modeling/measurement (PFM) theory.

Preference aggregation
Preference aggregation is a key principle within design/decision making. It determ-
ines how individual preferences are integrated in group decision making, which
is thereafter reflected in the design. A straightforward and commonly accepted
approach for the aggregation of preferences is to use the weighted mean of the
individual preference scores. However, this is not correct as the operations of
addition and multiplication are not defined on these preference scores, Barzilai
(2022). Instead, aggregation of preference scores should be done according to the
mathematical operations that are defined in the one dimensional affine space. The
overall group preference score is the synthesis that provides the “best” fit of all
weighted (relative) scores for all different subjective objectives. In other words, the
correct way of preference score aggregation, according to preference function mod-
eling/measurement (PFM) theory, is based on finding the aggregated preference
score that minimises the least-squares difference between this overall preference
score and each of the normalised individual scores of all stakeholders’ criteria.
For doing so, we use the Tetra software which incorporates a solver based on
the before mentioned preference aggregation (for Tetra see: scientificmetrics.
com). We use PFM because it is based on a mathematical well-founded theory of
preference measurement. Note that classical multi-criteria design/decision making
analyses, including the Pareto analyses, which use the weighted arithmetic mean
algorithm, contain modeling errors that render their outcomes meaningless.
1.5. ODESYS’ COMMON TERMS & DEFINITIONS 37

Incitement 1.6 Problem solving, connecting and observation

Look at the orange dot (above). Which of the two is bigger? You probably think it’s the one
on the right. Look at the two horizontal lines (above). The top one seems shorter, right?
Our brain likes to see objects in relation to other objects to assess how big something is. Also
remarkable is that your eyes project the image you see upside down and your brain converts
it back the right way. Actually, we should reflect on this extraordinary collaboration. And
what would this mean for our other inner senses processing? Would these also undergo a
reversal process within inner self consciousness? Maybe a U-turn in the consciousness?
Look at the white square (below), can you observe four lines which span this square? Look
at the white squared box below with the nine coloured points, can you connect these points
by means of four lines? And what does this mean for your process of observing, sensing and
generating?
Let’s look again at observing. There seems to be an apparent contradiction between ob-
serving contents (objects) that are already there, and thinking as the activity that generates
and connects understanding contents? What appears to me in the observation without me
producing it, I might be able to connect this with the thought-contents (concepts and ideas)
that I myself produce. Would insight arise in this way? And, would there also be a possib-
ility of perceiving and observing our thoughts or thinking process? If so, what possibilities
might this offer for gaining insights?

1.5. Odesys’ common terms & definitions

In this section we will introduce some commonly used terms and define their
interpretation within the Odesys context. Also, typical Odesys terms and their
relation to similar terms and the most common abbreviations will be listed.

Purpose
Purpose means ”intention, aim, goal; object to be kept in view; proper function for
which something exists”. Etymologically it is equivalent to Latin propositium ”a
thing proposed or intended”. ’On- or for purpose’ means ”by design or intention-
ally”. According to Steiner (1995), purpose is related to the inner human motive
38 CHAPTER 1. FRAME OF REFERENCE

(i.e., ‘the impulse that gets you in motion/ gets you motivated’) that transforms
a desire/wish via an intent/interest into a (moral) design/decision. The inten-
tional or so-called idealized design serves as motivator/stimulator for the devise
and design process, see Ackoff et al. (2005, 2006). Note this is one of the reasons
to develop the inner purpose via a self-chosen/motivated system of interest as part
of the open design learning (ODL) concept.
Purpose is also directly linked to the concept of quality: i.e, real service quality
or quality of service (QoS). Fit(ness) for purpose is a fair balancing act between
user demand (wanted by the subject) and engineering asset supply (offered by
the object) and expresses the system’s QoS (on product delivery and/or ongoing
during operation), which is in essence a pure economic balancing act. For further
readings, see Ackoff (2006); Hastings 2014; Van Gunsteren (2013).

Principle of reflection
The principle of reflection is an essential element of modeling that states that op-
erations within the mathematical system are applicable if and only if they reflect
corresponding operations within the empirical system, see Barzilai (2022). For
instance, the difference between two time events (year numbers 2010 and 2020) is
defined because the operation of subtraction is defined in the mathematical system
(one dimensional affine space) that represents time. Conversely, the addition of
two year numbers is not defined in the one dimensional affine space that represents
time and therefore the outcome has no empirical meaning. In technical terms, in
order for the mathematical system to be a valid model of the empirical one, the
mathematical system must be homomorphic to the empirical system (a homo-
morphism is a structure-preserving mapping). In a broader sense the principle of
reflection also relates to the essence of engineering design where modeling plays
a very important role as a reflection of the reality of object behavior. In most
of engineering problems a reality test can be performed to check the validity of
the model. However, for mathematical modeling of human behavior in the social
sciences such a test is not readily available. Therefore in the social sciences domain
one has to resort to meticulously scrutinizing each step in the process of sound
modeling of human behavior, see Van Gunsteren (2022). This means that for the
part of open design systems that deal with human behavior, one needs to make
use of mathematical models that are based on proper axioms.
1.5. ODESYS’ COMMON TERMS & DEFINITIONS 39

Incitement 1.7 Only right thinking does not determine reality!

In the outer world, in as much as this world is today dominated by outer science, when
one speaks of knowledge, no doubt one will always say: Yes, knowledge, it must always
result in the truth if one has right judgments, if one has thought the right thing. Lately, to
characterize what is profoundly wrong in this supposition—that it must always come true in
knowledge, in truth, when right judgments are made—we use a very simple equation, which
we want to recount here again, showing that the right does not have to lead to reality.
”A little boy, who was always sent by his parents to get sandwiches on a Sunday morning,
got 10 Euro and he got six sandwiches for it. If you bought one sandwich, it cost 2 Euro.
But he always brought home six sandwiches for 10 Euro. The little boy wasn’t very good
at math, and he didn’t care if it’s true that he always takes 10 Euro with him, even though
a sandwich costs 2 Euro and he still gets six sandwiches for his 10 Euro.
But then one Sunday they had a lodger, a university student and a good mathematician.
He now saw that the little boy was going to the bakery, and that he was given 10 Euro. The
student knew that a sandwich costs 2 Euro and he said: So you must take five sandwiches
home with you. He could calculate well and he thought the right thing: one sandwich
costs 2 Euro, he gets 10 Euro, so he will most certainly take five sandwiches home. But
behold, the little boy came with six. Then the student said: ”but that is completely wrong,
because a sandwich costs 2 Euro and you have been given 10 Euro, you cannot possibly get
six sandwiches, because for 10 Euro you only get five sandwiches from 2 Euro. One must
have made a mistake or you have stolen a sandwich”. On the second Sunday the boy again
brought six sandwiches for 10 Euro. For it was customary in that place that on Sundays
with five one always got one more, so that indeed, if one bought five sandwiches for 10 Euro,
one got six. It was a very pleasant habit for the customers.”
Well, the student thought very correctly, he made no mistake in his thinking, but this correct
thinking did not correspond to reality. We must admit that right thinking does not reach
reality because reality simply does not align itself with right thinking. You see, as in this
case, it can thus be shown that in fact the most conscientious, complex ideas, which can
only be thought out logically, can come out right, but can be completely wrong against
reality. This can always be the case. Therefore, the principle of reflection should be applied
especially to those things that arise purely from the mind.

U-model
The U-model forms the basis for the theory-U, which is a change management
method based on the foundation of human experiences (integration of an open
mind, heart and will) and more particular the principles of human learning and
development behavior, see Scharmer (2016) and/or Figure 1.7. The U-model was
originally developed by Glasl and his colleague Lemson from the Dutch Institute for
40 CHAPTER 1. FRAME OF REFERENCE

Organisational Development (NPI) as an open socio-technical process to come from

a phenomenological diagnosis of the present state to designs for the future (drawing
on Goethean/anthroposophical scientific principles by Steiner), see Glasl (1998)
and/or Figure 1.7. They described a process in a U-formation consisting of three
levels (technical and/or instrumental subsystem, social subsystem, and cultural
subsystem). In general, this U-procedure (or method) transforms observations into
intuitions and judgments about the present state and design/decisions about the
future. The three stages represent explicitly recursive reappraisals at progressively
advanced levels of reflective, creative, and intuitive insights, thereby enabling more
radically open systems intervention and redesign. The stages are a metamorphosis
from: a) phenomena - picture (a qualitative metaphoric visual representation),
b) idea - purpose (the design idea or formative principle), and c) validation -
prototype (is this fit for purpose?). The first three then are reflexively replaced by
better alternatives (new idea new image new phenomena) to form the final design.
In contrast to that earlier work on the U procedure, Scharmer’s renewed theory-
U starts from a different epistemological view (imagination, inspiration, intuition)
that is grounded in Varela’s approach to neurophenomenology, see Varela (1991), as
opposed to the more ontological approach of Glasl’s U-procedure (picture, purpose,
prototype). It focuses on the process of becoming aware and applies to all levels
of systems change and/or (re)design.

Figure 1.7: The basic U-diagrams from Glasl (left) and Scharmer (right), which will be our starting
points as of Chapter 3 and onwards.

In this book, the U-model and/or theory-U has been further transformed, exten-
ded, and made specific for (1) open loops management (2) open design systems
(3) educational purposes within the open design learning (ODL) method, see Bin-
nekamp, Wolfert et al. (2020). Specifically, in this ODL process students combine
1.5. ODESYS’ COMMON TERMS & DEFINITIONS 41

both the human development principles following from the U-model theory and
the engineering systems development principles according to the V-model theory
(see Chapter 4,9). These U and V models theories seamlessly connect the open
human and systems development, mainly because of their congruence. For further
U-model readings, see Glasl (1998); Glasl & Lievegoed (2016); Scharmer (2016).

V-model
The V-model is a logical model or graphical representation of a systems design/
development life-cycle. It is used to produce rigorous design and development
life-cycle models and engineering management models. The V-model summar-
ises the main steps to be taken in conjunction with the corresponding deliver-
ables within a system validation framework, or engineering asset/project life cycle
design/development. It describes the activities to be performed and the results
that have to be produced during service or product design/development.
The left side of the ”V” represents the systems design decomposition of require-
ments, and creation of system specifications. The right side of the ”V” represents
systems integration of parts and their final verification and validation. Note here
the formal distinction between verification and validation. Where verification is
focused on checking whether subsystems meet their requirements, validation is
focused on evaluating whether the system as a whole is working as intended. A
systems engineer compiles a verification plan which describes how each subsystem
will be verified against its specific requirements. However, before the verification
plan is made a validation plan is compiled reflecting how the user needs as part of
the operational concept design will be operationally evaluated as a working system.
Together with the U-model, the V-model can be represented as a W-model which
truly connects the human devise & design U-model with the systems design &
develop V-model, forming an integrative open systems development approach (life-
cycle: devise, design, construct, integrate, operate). For further V-model readings,
see Blanchard & Fabrycky (2011); Wasson (2015).

Reflective practitioning
In general, a reflection process is termed as a cycle that must be repetitive. The
four aspects of the reflective cycle are to teach, self-assess, consider, and practice.
Reflective practice is said to be a process to learn from and through experiences
on-the-run for the acquisition of new understandings and perceptions for practice.
Reflection is a fundamental part of learning and teaching. It generally aims to en-
hance your professional knowledge and actions. The reflective practice is explained
as a practice that helps a student to be aware of inherent learning and knowledge
from their experience. Concepts such as double-loop learning, the learning society,
and reflection in- or on action are now a part of education language. Note that
42 CHAPTER 1. FRAME OF REFERENCE

where double-loop learning ‘was over its top or ended’, the U-model based learning
and development started. For further readings, see Scharmer (2016); Argyris and
Schön (1995); Palmer and Zajonc (2010).

Money
”Money” is not a (physical) property of an object, but rather relates to economics
which is part of the social sciences (demand versus supply as driving factors for
the pricing of goods). In other words, money is not primarily related to the ob-
ject but to affordability as determined by the human subject. Let’s look further
at the quality and quantity of money, and in particular what a €50 note could
demonstrate us. If we look at such a €50 note we can just consider it purely
quantitatively and say this represents €50 as it says on the note. In a similar way
you can also look qualitatively. We could say look at the essence of money and
how is money used between people. Then we will recognise that money can rep-
resent a buy-, loan- or gift money form between people (threefold nature of money
as a transaction means between people). When we consider these three types in
more detail qualitatively, it can be concluded that buy-money has a ”value for
money (fairness)” character, that loan-money is about reciprocity (“mutuality”)
and carries an equal agreements nature, and that gift-money possesses uncondi-
tionality and/or ”freedom” as a quality, see Figure 1.8 (note: we will return to this
in detail later in this Chapter). This qualitative consideration is then no longer so
much about the €50 purse, but what the essence of the interpesonal use of money
represents. Even more simply, you can also separate quantitative and qualitative
by simply looking at a word. The word may consist quantitatively of a number of
letters but only a number of letters together in a correct order form a word, and
that word together with other words gives a meaning only in its context. Both
these perspectives complement each other and do not contradict each other.

Figure 1.8: The quantitative and qualitative meaning of money.

1.5. ODESYS’ COMMON TERMS & DEFINITIONS 43

Living (design) dialogue

Dialogue assumes a conversation and a necessity to listen to the other. Its cre-
ator/’father’ Martin Buber, see Buber (2004) indicated that a real discovery of a
true ’I’ lies in the encounter with ’You’, and ’I’ does not exist without a relation
with ’You’. According to Buber, dialogue constitutes the basis of philosophy in
general due to the fact that it is the only effective form of communication in con-
trast to one-sided expressions of opinions. In other words, in the space between
one and the other (subject-object and/or subject-subject), a place can be found
where new ideas can emerge. From this principle arises the so-called design dia-
logue as part of the U-model and/or the ODL concept. A design dialogue is a
way of ‘intuitive thinking’ via concentrative inter-sensing-acting on practice that
brings together awareness and insights as stepping stones towards the creation of
new design. This living design dialogue is an active ‘inner’ dialogue with yourself
and/or an ‘outer’ dialogue with the model that represents the design problem.
For further readings, see Bohm (2004); Buber (2004); Palmer and Zajonc (2010);
Scharmer (2016); Zajonc (2008).

Terms: differences, synonyms & abbreviations

Recognizing that terms are (mostly) used interchangeably in different areas of
science and engineering, we want to facilitate the reader here by presenting some
subtle differences and similarities of commonly used Odesys terms. Finally, the
most frequently used abbreviations will be listed.
Methodology versus method Methodology refers to the overarching strategy
and rationale of a research or development project. It involves applying from all
available methods those that are of interest to a specific research or development
project to arrive at the intended outcome (new knowledge or new product). For the
overarching R&D methodologies, see the process flows in Chapter 2. Note that the
Open Design Systems methodology is a particular colouration of this development
process flow. Methods are the specific tools, techniques, and procedures which one
can use either to: 1) collect data and investigate behavior (research) or 2) devise
the new engineering product (development). For an overview of R&D method, see
Appendix A.
Modeling versus simulation System’s simulation is the operation of a model
in terms of time or space, which helps analyze the performance of an existing
(science) or a proposed (engineering) system. In other words, simulation is the
evaluative process of using a model to study the performance of a system. It is
an act of using a model for simulation. In other words, simulation is a process in
which the sensitivities of an ’as-is’ system are analyzed under different scenarios,
and is hence the opposite of design, in which the parameters of the system are
optimised to obtain new forward-looking solutions.
44 CHAPTER 1. FRAME OF REFERENCE

Experimenting versus observation Experimenting is the systematic observa-

tion of an existing (science) or proposed (engineering) system for the purpose of
analyzing its performance. It is congruent to simulation, however, experimenting
does not make use of a model. It is an act of using an experiment for observation.

Table 1.1: Terms & synonyms.

Term Relates to and/or is synonymous with

Alternative Variant, option, configuration
Conspection Validation, appraisal, or reflection
Constraint (mathematics) Boundary condition, required limit, or limit state.
Criterion Design/decision/evaluation aspect
Decision making Evaluation, selection, making trade-offs, assessing, ap-
praising, or resolving
Delivery Deployment or installation
Design (noun) A plan, scheme (in the mind), or an idea
Design (verb) Planning, creating, originating, generating, construct-
ing, developing, or configuring
Design configuration A design alternative, design solution, or design variant
Design point An optimal design configuration
Desires Needs, demands, or requirements
Emergence The properties of a system as a whole that the parts do
not have on their own or articulation
Functionality Design to Y (tY) or design for value
Means Resources
Object A physical or engineering asset
Objective (mathematics) Criterion, goal, target, need
Open glass-box Antonym of black-box: open and transparent
Open-ended Without an end date or planned way of ending or not a
fixed end (no 0/1)
Preference Value, utility, worth, satisfaction, or well-being
Preferendus A software engine (tool) that integrates ODESYS, PFM
and the IMAP algorithm
Purpose Intent or ideal (idealized design)
Quality of Service Functional performance or design tY aspects.
Solution space To a design space, management space, feasibility space,
feasible region
Stakeholder Actor
Subject A living human
Tetra A software tool for PFM-based MCDA solving
1.5. ODESYS’ COMMON TERMS & DEFINITIONS 45

Synonyms Throughout the book we will use different terminology depending on

the specific context. To prevent confusion, we list the most frequently used terms
alongside related terms and/or synonyms, see Table 1.1.
Abbreviations In this book, we will often use various abbreviations. In principle,
when a new abbreviation is introduced, it will be entirely spelled out the first time.
Here are the most important and common abbreviations spelled out and listed (see
Table 1.2).

Table 1.2: Abbreviations.

Abbreviation Meaning
CSI Corporate Social Identity
DES Discrete Event Simulation
EAM Engineering Asset Management
GA Genetic Algorithm
IMAP Integrative Maximised Aggregated Preference
MC Monte Carlo
MCDA Multi Criteria Decision Analysis
MILP Mixed Integer Linear Programming
MitC Mitigation Controller
MODO Multi Objective Design Optimisation
ODESYS Open Design Systems
ODL Open Design Learning
PDP Project Development Plan
PFM Preference Function Modeling/Measurement
QoS Quality of Service
R&D Research & Development
SAMP Strategic Asset Management Plan
SODO Single Objective Design Optimisation
SE Systems Engineering
SoI System of Interest
SOP Service Operations Plan
3C Centralize Cluster Calculate
4Q 4-Quadrant
46 CHAPTER 1. FRAME OF REFERENCE

Incitement 1.8 Reflective practitioning and system of interest

People who call themselves practitioners often think they are acting according to the most
practical points of view. A closer look, however, will reveal that this so-called ’practical
thinking’ often has nothing to do with thinking, but consists of little else than further
toil with inherited or handed over views and learned thinking habits. Therefore, in the
educational process there will have to be reflection with the practitioner context, but always
’intuitive thinking’ from the open will truly guide open design learning. Moreover, there
are three things to consider if man is really to take up education in the sense of practical
thinking: first, man must develop an interest in the external reality surrounding them, an
interest in facts and objects. Interest in the world around us, that is the magic word for
integrative learning. Passion and love for what we do, that is the second. Fulfilment in what
we are thinking about, that is the third.
Whoever realises these three things: interest in the world around us, passion and love for
what we do, and enjoyment in what we do and think about, will soon find that these are the
most important requirements, which can be placed on a practical development of thinking.
Besides cognition, shouldn’t education also focus on an experiential context of the student’s
own choosing, integrating practical thinking with intuitive thinking?

1.6. Odesys’ paradigms & views on world and man

With the mother statements ”Everyone in the world must have access to clean
drinking water” and ”Every child must be able to go to school”, nobody will dis-
agree in principle. However, the worldview and paradigms from which we operate
can cause fundamental differences in the related strategic directions for response.
After all, these form the basis of our actions, the basis of the motive from which
you consciously make decisions and implement these. As a result, a given problem
can lead to very different actions or responses (i.e. design solutions) when acting,
for example, from freedom and trust instead of coercion and control, or from a
top-down versus a bottom-up approach. A schematic diagram capturing different
strategic directions of response is seen in Figure 1.9. For further inspiration and/or
reading, see the related societal and man views works of Desmet (2022); De Wit
(2021); Van Egmond (2014); and/or Zoeteman (2009), amongst others.
Intermediate note: examining the UN’s 17 global Sustainable Development Goals
(SDGs) that form the basis of the so-called Agenda 2030 (see: sdgs.un.org/
goals), we are struck by the absence of an overarching paradigm, such as that of,
1.6. ODESYS’ PARADIGMS & VIEWS ON WORLD AND MAN 47

for example, the social threefold order (an ordering principle that we will discuss in
detail later in the Chapter). Moreover, we also miss a number of explicit running
conditions and especially that of freedom of thought (following the philosophy of
freedom from Chapter 2). These omissions, amongst others, negate all of these
17 goals and thus place them in the realm of social coercion. Indeed, this even
deprives thinking of individual ability to distinguish: i.e., the human quality of
thought comes under threat because it does not contain an ideal of human freedom.

Figure 1.9: Strategic directions of response

These goals can even be explained as a supreme ideal of eliminating all distinctions
between people and in certain areas, that is certainly appropriate. When we
for example talk about human dignity, we argue that this should be equal, and
that the value of all human beings is equal. However we can not then say that
all people are equal and that they should all and everywhere fulfil these goals
equally. Actually, these goals can be embraced by all of us, such as: ’improving the
climate’, ’equitable quality of education’, ’promoting well-being’, and ’improving
48 CHAPTER 1. FRAME OF REFERENCE

social cohesion’: these are mother statements we can all agree on. So the problem is
not so much these goals as such, but the means used to achieve them. For example,
techniques are used to convince groups of people of predetermined content, through
a-posteriori intransparent participatory processes where they were not involved
in the solution with precisely a negative impact on human well-being. So how
could we achieve engaged participation through open a-priori design processes
best fit for local purpose? And does this not mean precisely a call for regional
development goals (RDGs) rather than SDGs? Or another example, what students
who have the future end up acquiring during their long period of educational
development determines much for the future of all of us. We recognize that the
state increasingly interferes with the form of education and is it actually their
responsibility to determine the content of it? In any case, for both examples, we
argue that if you control people, or a group of people, a little every day for long
enough, after a long time they will be affected in the way they form their ‘own’
opinions and judgements, as it will impact their critical thinking abilities.
Within the Odesys philosophy, we aim to design new solutions for the future from
the ’golden mean’ principle (Aristotle) with ‘man and his nature in the centre’.
Further on in the book, it will become clear that these ‘mensch’ paradigms will not
lead to a set of half-baked compromises, but to synthesis solutions based on a best
fit for common purpose idea . This again, does not necessarily mean that everyone’s
preference is equal, but that these will be taken into account equitably in the
design process. Moreover and in short, it is important to clarify these paradigms
as a starting point. In other words, to arrive at a so-called ’idealised engineering
design solution’ for a particular problem within a human and embedding societal
context, we need an integral human-centered world view. After all, according to
the generally known principle ”what you see is what you get (WYSIWYG)”, you
could argue that if you only see a human being as a machine (or vice versa) you
also only find look-alike solutions. Therefore a balanced starting point here is a
holistic view of man and the world in which engineering objects, with physical/
mechanistic properties (material), and living subjects, with spiritual properties
(immaterial), interact.
1.6. ODESYS’ PARADIGMS & VIEWS ON WORLD AND MAN 49

Incitement 1.9 Threefold

’All good things come in threes’ is a well-known saying. The 3

is also frequently encountered in other Dutch language, sayings,
songs or traditions: e.g.,
Three times is a charm (‘driemaal is scheepsrecht’)
He looks as if he can’t count to 3
One egg is not an egg, two eggs are half an egg, three eggs are
Easter eggs
Three days of Carnaval, then on to Easter
3 times 3 is 9, everyone sings his own song
Big, bigger, biggest, or good better best etc.

Apparently, we do have a special relationship with the number

3, maybe it’s in our blood, but in any case it has a magical
attraction. According to Pythagoras, 3 is the first ”true”
number. It forms a geometric shape: the triangle, which
stands for independence, solidarity and strength. In Chinese
philosophy, there is a powerful statement about three:” The
Tao made the one. The one has made two. One and two made
three. Three has made the rest of things”. Could humans and
the world ultimately also rest on a primal triad?

Note that a trichotomy is a three-way classificatory division.

Some philosophers pursued trichotomies. In mathematics, the
law of trichotomy states that every real number is either posit-
ive, negative, or zero.

Key paradigms & starting points

Let us first distinguish the difference between a mechanical (technical or engin-
eering) system and a living organic (human or social) system, as depicted in
Figure 1.10. The mechanical system is characterised by the following features
(a)’integrate the pieces’ and (b) ‘causal – as a result from (in-output). In contrast,
the living organic system is characterised by (a)’differentiate from the whole’ and
(b)’purposive – towards a goal’ : i.e., a living system is teleological (Aristotle: ’only
nature is purposive’). Note: teleology is a reason or an explanation for something
which serves as a function of its purpose, as opposed to something which serves
as a function of its cause. And in particular, it characterises a living system by
the concept of metamorphosis and often refers to the morphological and physiolo-
gical changes an organism undergoes during its purpose-directed development. It
should therefore be noted that only a human system can be goal-oriented and a
mechanical system never of itself (this will come back later in Chapter 4, when
we will talk about design which in itself is goal-oriented only through the actions
and/or participation of man and his interests).
50 CHAPTER 1. FRAME OF REFERENCE

We can now ask ourselves, in the context of these two system types, what actually
constitutes a society with different organisations and people? And so that brings
us to our first paradigm, the paradigm of the ‘social organism’ This paradigm reads
as follows (see e.g. Glasl (1998) and Lievegoed (1996, 2013), amongst others):

PI - ‘the society, its organisations and their humans are a living social organ-
ism: a “bio-topos” which is a purposeful bio-dynamic system, rather than just a
mechanical behaving system’

Note: we will see later that the engineering assets (the objects of a mechanical
subsystem) of an organisation will also occupy a special place within this ‘living’
organism.

(a) A typical mechanistic system. (b) Typical living organic systems.

Figure 1.10: Mechanistic versus organic system

Besides this PI paradigm, we also need a holistic systems-thinking-based approach

to study and/or improve these integrative social engineering systems. The fol-
lowing starting points are important here: (1) observation of both the subject
and the object in its total context taking into account the so-called embedding
system dimensions (e.g. societal, natural, regulatory etc.), (2) observing the sys-
tem as a whole using zooming-in and zooming-out principles, (3) recognising and
using the phenomena of systems emergence (or systems articulation), symbiosis
and self-similarity, (4) systems can always be considered both qualitatively and
quantitatively, (5) using the principle of human reflection with the real world.
The first four points speak for themselves and their reference principles within
open design systems theory can be found in previous sections. However the fifth
point, the principle of human reflection, deserves some clarification here (in ad-
dition to the principle of reflection as described in Chapter 1). Ultimately, the
fifth aforementioned point, the principle of real-life human reflection leads to a
new final paradigm with specific relevance to the engineering management context
(which will be shown later in this Chapter and others).
1.6. ODESYS’ PARADIGMS & VIEWS ON WORLD AND MAN 51

This so-called principle of human reflection paradigm reads as follows, see for ex-
ample Steiner (1996); Scharmer (2016); Heusser (2016), amongst others: i.e.,

PII – ‘relate every (dynamic living) thing/being you see in the world to the
general human or relate every living thing/being you see in the world to what you
see in humans’

Note: this paradigm has far-reaching implications for problem solving within the
context of designing and managing socio-technical systems. We will first see that
in commonly used mathematical formulations of these types of problems, funda-
mental modeling errors arise when this paradigm is overlooked (see the Preferen-
dus, IMAP, 3C and MitC concepts and their integrative modeling approaches as
‘true’ real-life answers to this). We will also see that, only from this paradigm
combined with an open design systems thinking approach, socio-technical prob-
lems can be solved for the future. The current single-sided engineering view shows
that zooming in and only using so-called ‘meta-modeling’ approaches (the term is
misleading as it does not follow a meta- or integrative approach at all) does not
overview the systems as a whole with unusable micro additions as a result, see
the 3C concept developed by Wolfert and its multi systems intervention modeling
approach as a ‘true’ real life answer to this, Kammouh et al. (2021). The latter
element in particular, in the current publishing spirit that is driven by a perverse
incentive (pure visibility drive), leads to an avalanche of papers that may be in-
teresting to H-indices and the ‘outside image’ of the journal, but have no real-life
value at all. Finally, current ‘engineering education’ (at least within technical uni-
versities) also shows an approach in which the principle of human real-life reflection
is partly or sometimes even completely overlooked, resulting in far-reaching con-
sequences for our current society (see the ODL concept and its integrative design
learning approach as a future-oriented answer to this).
In summary, after all the above and particularly in the light of the last paradigm,
we must now consider the generic nature of human beings more closely to let
these characteristics return in the living developing engineering asset management
organisation. Before we continue with the human view, here are some special
quotes from Simon (2019), which help motivate why we need a human view within
the Odesys design context: i.e.,
“. . . We can conclude that, in large part, the proper study of mankind is the science
of design, not only as the professional component of a technical education but as a
core discipline for every liberally educated person. . . Eventually it becomes clear
that human beings themselves belong to the realm of the artificial. Indeed, they
are probably the most important class of ‘artifacts’ given that they are able not
only to create other artifacts but also to re-engineer themselves to best fit changing
circumstances...”.
52 CHAPTER 1. FRAME OF REFERENCE

Incitement 1.10 I am not I, who am I

(Juan Ramon Jimenez, poet)

I am not I.
I am this one
walking beside me whom I do not see,
whom at times I manage to visit,
and whom at other times I forget;
the one who remains silent while I talk,
the one who forgives, sweet, when I hate,
the one who takes a walk when I am indoors,
the one who will remain standing when I die.

The general human, a holistic perspective

This subsection can be seen as a perspective on the general human what we need
to further interpret the PII paradigm from the previous section. We will provide
specifics here of some specific characteristics when studying humans from a holistic
perspective. These insights were gained mainly through Steiner’s years of study
of man, which was then also confirmed from other viewpoints and/or further elab-
orated by various (spiritual) scientists among them doctors/medics, biologists,
and/or psychologists. We build on several of them in this book and we base the
human view within the Odesys design and decision context on at least the works
of: Barendregt (2022); Bohm (2004); Bortroft (1996); Desmet (2019); Dijksterhuis
(2006); 2011), Gallagher (2013); Heusser (2016); Husemann (1994); Jung (2001);
Kahneman (2013); Lievegoed (1996); Mosmuller (2018, 2021); Soesman (1998); Si-
mon (2019); Varela (2017); Velmans (2017); Zajonc (1995, 2008), amongst others.
This holistic view of man (and its nature) will be used in the further development
of an organisation with its specific management and design processes. It provides
insight to understand and improve such an organisation and its processes and sys-
tems within its societal context. Some key principles, elements, and/or properties
of this man and world view are summarised hereafter. Note that we specifically
developed the conclusions here and the figures, models, and/or diagrams hereafter,
fit for the Odesys specific context of use in the rest this book. Only where im-
ages have been used one-to-one, the source will be specifically indicated (in the
text). These pictures will not be described in detail because they speak for them-
selves (and have been described in detail in other books by the aforementioned
scientists), but here the specific interpretation and use of what is relevant to the
engineering asset management (EAM) and Odesys context will be briefly and fur-
ther explained. The overarching starting point is the threefolding nature principle
for the following five starting points.
1.6. ODESYS’ PARADIGMS & VIEWS ON WORLD AND MAN 53

(SP #1). Man’s body, in other words his physical support structure, consists
of three parts, three specific echelons/spheres/realms that are connected as unity.
Everyone can observe these three structures when you see someone walking by,
namely a (wo)man has a head, torso, and limbs. From this starting point, the
following three subsystems can be distinguished, each with its own particular and
internal physical autonomy (as further detailed in Figure 1.11):
• Empiric: head-nerve-sense system (upper pole)
• Rhythmic: heart-lung system (middle pole)
• Metabolic: organs-limbs system (under pole)

Figure 1.11: Threefold of the human body.

Note: following the self-similarity principle, it is also possible to zoom in further

into other physical sub-systems of the body to then see a similar tripartite division.
A special example of this is the study of an intestine/colon and its gut-wall. It
turns out that this in itself again has a tripartite division of ‘observation/ separ-
ation/ thinking’, ‘circulation/rhythm/feeling’ and ’regeneration/ supply/ willing’,
which are represented as a tripartite sub-system in the gut-wall (see e.g. Huseman
(1994) and/or Heusser (2016) among others for comprehensive medical studies and
many other examples). A remarkable well known expression in this context is the
following, “it is my gut feeling that we have to act like. . . .”. Apparently, gut feel-
ing says something about how to act and is thus linked to the will? For here, the
notion of self-similarity is sufficient in itself.
Especially for analysing an organisational structure, with its different qualities
within it, the following ’translation table’ is important, see Figure 1.12. It shows
the translation of the threefold view of man into an organic system: i.e., a tripart-
ite/ threefold organisation, which basically breaks down into ’the eyes and ears’,
’the heart’, and the ’engines’ of an organisation. We will use this translation table
later in Chapter 3 for composing an organisational structure of the engineering
54 CHAPTER 1. FRAME OF REFERENCE

asset management (EAM) organisation. With this, it will appear that we can
structure the various organisational components and its identity, consider them
more closely, and/or improve them from a qualitative (and later, as of Chapter 5,
also from a quantitative) point of view.

Figure 1.12: Translation table (from human to a living system), as input for a threefold organisation.

(SP #2). In addition, we can recognise another tripartite division in human

beings. The threefold of man into body/ soul/ mind, which allows us to distin-
guish between corpus (’carrier’), ego (’motivator’), and spirit (’unique self)’), see
Figure 1.13.

Figure 1.13: Threefold of human being, based on Lievegoed (1996, 2013).

The body (’carrier’) is the physical carrier of the two other parts of beings. The soul
(’psyche’) in which inner momentum, feeling, sensing, and comprehension combines
into a personal motive, is sometimes also known as the ego. The mind, sometimes
called the spirit-self, provides the spiritual (unconscious) source to which the body
is connected with the soul as the mediator. A symbiosis of these three parts of hu-
man being leads to emergence of the individual and unique ’I’: i.e., individuation,
1.6. ODESYS’ PARADIGMS & VIEWS ON WORLD AND MAN 55

a process by which the individual self develops out of an undifferentiated uncon-

scious, see Jung (2001); Lievegoed (1996, 2013); Steiner (1996); Varela (2017),
amongst others.
This tripartite division is especially important for distinguishing between the or-
ganisational structure of man (how the body is built up), how man comes to act
and behave, and how their experience and/or motive ’steers’ this. The latter is
in fact an inner process, an unobservable process for others, to which the act of
designing or managing is most linked (after all, designing is ’making’ a conscious
plan or scheme from the mind to realized matter or action). In other words, the
process of planning or designing is a process from inner sensing to outer realisation.
We will see this in more detail later in the U-model theory and its application.
(SP #3). Subsequently, using the principle of self-similarity, the aforemen-
tioned threefold division can be further zoomed in to a more detailed division
of the senses, resulting in the so-called twelve (inner-outer) senses, see Soesman
(1998) and/or Figure 1.14. It should be noted that there are thus senses directly
connected to the empiric system, which take care of external perception, say ’the
eyes’ of a social organism (’shedding light on matter’). The other senses are, as it
were, tucked away inward and are therefore called the inner senses from which per-
ception (’the heart of the matter’- intimacy representation) and movement (‘the
organic engines - warmth and energy) also originate.
Incitement 1.11

Quickly consider the following question: A bat and ball cost

$1.10. The bat costs one dollar more than the ball. How much
does the ball cost?

A number came to your mind. The number, of course, is 10:

10¢. The distinctive mark of this easy puzzle is that it evokes
an answer that is instinctive (impulsive), appealing, and wrong.
Do the math, and you will see. If the ball costs 10¢, then the
total cost will be $1.20 (10¢ for the ball and $1.10 for the bat),
not $1.10. The correct answer is 5¢. It is safe to assume that the
fast and impulsive answer also came to the mind of those who
ended up with the correct number — they somehow managed to
control the (instinctive) impulse.

(SP #4). The fourth and final ’human’ starting point links the process of
design and decision making to the threefolding nature of man. We will now further
describe the important so called M-threefold of motive-momentum-management:
i.e., from motive and momentum arriving at a strategic action of response. To
arrive at ’actions of response’, a human basically has two main directions. First,
they can act or react from antipathy or passion so that their soul motive falls, as
it were, to an instinctive part of their nature (via the sentient body towards the
56 CHAPTER 1. FRAME OF REFERENCE

physical body, as an instinct to ‘survive’). In this case, a human acts purely from
their soul ego and drift-being resulting in impulsive and instinctive action. This
instinctive or impulsive type of thinking is sometimes called ‘system-1’ or thinking
fast, see Kahneman (2013). However, a human also has a second possibility to
come to actions (or reaction). Namely, by connecting their own inner free will
of the ”self” with consciousness, rather than only through the ego, to come to
designs or decision making. In this case, a human proceeds to conscious action by
uncovering their own will and thus arrives at individual self-realisation and ’self-
manmade decisions’. In other words, a person can connect their consciousness to
their so-called ”blind spot” or ”silent self” from an object of desire through an
intent rather than an instinct in order to arrive at an intuitive thought, see Steiner
(1996); Dijksterhuis (2011); Mosmuller (2018) and/or Zajonc (2008).

Figure 1.14: The link between the threefold man and the twelve senses.

Note: intuition means literally ’in-sight, looking inward, contemplation’ (also

called a spiritual perception as opposed to instinct which means natural prompting
and is said to be blind, since it is an act of non-conscious or impulsive recogni-
tion). The resulting action or movement, (which is actually the resultant of the
emerging I-force and the consciousness process mentioned earlier,) says something
about man’s unique self and his individual purpose, which can be transformed
into an ‘action of response’. We argue here that this intuitive thinking can also
be complemented by a form of logical thinking that is also a formal process of
1.6. ODESYS’ PARADIGMS & VIEWS ON WORLD AND MAN 57

the mind. This form of formal thinking is referred to as ’system-2’ or thinking

slow, see Kahneman (2013). This mode of thinking is one of deliberation (where
deliberation is etymologically interpreted as the beginning of a liberation/freeing
process). Deliberation is a process of thoughtful consideration of options, usually
a-priori to joint decision-making. Deliberation emphasises the use of (deductive)
logic and reason, as opposed to creativity or dialogue (inductive). Whereas delib-
eration is primarily seen as a logical act of reasoning with one’s outer environment,
intuitive thinking is an act of unlocking one’s inner will. When the two coincide
(are unified) then by definition this leads to a synthesis solution as a free thinking
result (see dialectical thinking principles of Hegel (2021) and/or Steiner (1996)).
We will see this unification process of intuitive and slow thinking later (Chapters 3,
4) on as an important foundation for Odesys’s extended theory-U, that is a theory
of redesign and change management, see Scharmer (2016) and/or Glasl (1998). For
the time being we will suffice with the new integration of the aforementioned fast-
slow (system 1-2) with the instinctive-intuitive thinking in the context of design
and decision-making, resulting in the so-called nine-fold human design and decision
making diagram, see Figure 1.15.

Figure 1.15: Ninefold of human being linked to design and decision making, further developed by
Wolfert from Kahneman (2013) and Steiner (1996).

Note that the motive gives direction (a purpose) to the action; the impulse (stimu-
lus to act or change of momentum) actually moves someone. For example, a person
acts out of a sense of gratitude towards a helpful friend (motive) and gives him a
present to thank him (impulse). Motives, individual or collective, are determined
contextually or strategically from the consciousness, where drives or instincts are
originating from the physical elements of the body. For Steiner, however, there is
one exception later acknowledged and elaborated by Gallagher (2013); Mosmuller
58 CHAPTER 1. FRAME OF REFERENCE

(2018); Varela (2017), amongst others. Namely, if a person has their action stim-
ulated by a freely produced ”thought”: i.e., an ”intuition” (concept, idea), then
the free will that produces this thought becomes a free will that can realize the
thought, see Steiner ( 1995). So the thought is the motive force and the impulse
comes from the thinking will, see also see Chapter 2. This intuitive act of thinking
allows an individual to act freely: i.e., to design and to decide from an open mind,
open heart and open will, which forms the basis of Glasl’s U-procedure and/or
Scharmer’s theory-U. We will see there that this intuitive thinking is actually an
up- and downward, a double, movement in which outer and inner coincide, result-
ing in a new decision or design (hence already the double arrow here), see Scharmer
(2016) or Glasl (1998).
(SP #5). We have seen that a symbiosis of these three parts of human being
leads to the individual ’I’. We might therefore now ask what it would mean if there
were an imbalance or disintegration of the human threefold and its subsystems.
What would happen if one of the realms took over or overpowered one or both of
the others? What might happen if the principle of living apart but not together is
applied? In that case a disruption of the articulation of the ‘I’, the identity of the
individual, comes about. This is for us the definition of disease/illness: i.e., disease
arises from, and/or is, a disunity of the human threefold. We will see later that this
disunity/disintegration of the threefold can also lead to ”social illness” within the
societal context or within an organization. Therefore, as a manager, a (re)designer
of an organizational context, we will have to be able to act as a kind of ‘business
doctor’(physician) who is able to integrate human knowledge and competencies
into organizational art, which is the so-called dialectical 3-k integration which we
will see later in the ODL concept (see Chapter 9, and note that ’3-k’ in Dutch
stands for ’kennis-kunde-kunst’).

The society, a social threefold perspective

An overarching starting point is that a society (zoomed out world around us) is
a living organism which is not simply a sum of the individual subsystems, but a
symbiotic system with an emerging social force as a result. As a next starting point
we zoom out from the human threefold principle using the self-similarity principle,
allowing us to recognise at least three kinds of independent societal dimensions/
spheres/ realms/ domains again (see Figure 1.16) : i.e.,
• Economic/ commercial (e.g., manufacture, service provider, contractor etc.);
• Political/ judicial (e.g. government, public administration etc.);
• Cultural/ spiritual (e.g., art-, media, science & education institutions etc.)
This threefold is called the social threefold (‘trias societas’) and is based on a
social theory which originated in the early 20th century from the work of Steiner
(2013), and later worked on by Brüll (2019); Large (2010); Selg (2011), amongst
1.6. ODESYS’ PARADIGMS & VIEWS ON WORLD AND MAN 59

others. The conviction here is that when economy, culture, and polity are relatively
independent of one another, they check, balance, and correct one another and thus
lead to greater social health and progress. A healthy societal life is thus based on
a division into three autonomous areas which are ‘living apart but together’ (i.e.,
a so-called LAT relation).

Figure 1.16: The social threefold.

Social threefolding aims to foster the main principles:

• Fraternity and associative cooperation in the economic life;
• Equality and human equality/ dignity for the conditions in the political life
(rights and duties);
• Freedom and idealized design to create/manifest in the cultural life.
Intermediate notes: (1) didn’t we already see this threefold with their specific
qualities in the different forms of money (buy-fraternity, loan-equality and gift-
liberty)? (2) the well-known ‘trias politica’ can be found in the essence of legal
life via the self similarity principle, see Figure 1.17. The ’trias politica’ consists,
or should consist in a healthy situation, of three qualitative parts to serve, to
accommodate and to create of laws, regulations and policies. (3) remarkably,
these three qualities are similar to French Revolution’s slogan, Liberté, Égalité,
Fraternité. (4) zooming out into the 17th-18th centuries teaches us the following:
at the time of the French Revolution, which was mainly about equality in legal
life, Europe had two other revolutions a cultural revolution (’Sturm und Drang,
Idealismus’) from Germany and an industrial revolution from England (economic
motive).
Steiner, amongst others (see e.g. Brüll (2019), Large (2010)), suggested the
three would only become mutually corrective and function together in a healthy
way when each was granted sufficient independence. He argued that increased
60 CHAPTER 1. FRAME OF REFERENCE

autonomy for the three spheres would not eliminate their mutual influence, but
would cause that influence to be exerted in a more healthy and legitimate manner,
because the increased separation would prevent any one of the three spheres from
dominating the others, as they had frequently done in the past.

Figure 1.17: Trias societas/politica: the social threefolding realms and its self-similarity principle.

Recapitulating, we saw how the ordering principle, the social threefold divi-
sion, divides societal life into three autonomous areas which are ‘living apart but
together’ (LAT relation) and have specific embedding societal dimensions. Now
the question remains how within the symbiosis of these three spheres, and from
the interaction of the people within and between spheres, an emerging social force
can result. For this we must consider the so-called prime social phenomenon, see
Brüll (2019), Large (2010), Selg (2019). The gist of this phenomenon is as follows.
When two people face each other, one always tries to rock the other to ‘sleep’
and the other always tries to stay ‘awake’. This, to paraphrase Goethe, is the
primordial phenomenon of social science. The social phenomenon takes place in
the encounter of man with man and is ideally that which makes people rise above
himself. So from the human interspace, the social force emerges in a human dia-
logue, see Buber(2004) and/or Bohm (2004). In other words, the emergent social
force which characterises the social identity of a society originates from the human
interspace acting from and between the tripartite societal realms. We can now
also conclude that when there is an imbalance between the three realms because,
for instance, one of the realms exerts a supremacy on the other(s), no healthy
sociality can emerge: i.e., a sick or a-social society is a society with an unbalanced
social threefold. Realising this can help both in the diagnosis and the remedy of
this illness.
We end this subsection with the overview of the main threefolding models we
have looked at so far, with their interrelationships and/or inversion principle of
1.6. ODESYS’ PARADIGMS & VIEWS ON WORLD AND MAN 61

plant, man, and society, see Figure 1.18. We will regularly revisit these threefold-
ing results in the following sections to better understand and model the act of
managing and designing.

Figure 1.18: The threefold of plant, man and society and their inversion principle.

Note that within the societal systems economy is about cooperation in the world
(’looking each other straight in the eyes and perceiving each other’s need in the
world’) and is therefore connected to the upper pole area, that of observation
and translation (from ‘outside to inside’). Culture is the source for society (what
would a society be without) and is therefore connected to the lower pole area of
(re)generation and (re)purpose. This may sound counter intuitive but we will see
later that creation and free individual will ’belong’ in this area. Politics and its
legal life is about setting conditions and policies and supporting the other areas,
something typically linked to the middle pole area of equality and distribution.
This page intentionally left blank
Chapter 2

Design in the context of science & engin-

eering
Science (or research) and technology (or engineering development) are almost
identical in the eyes of most people. This misconception originates in the enorm-
ous, and continuous, influence of science on technology. In this manner one may
equate the methods of technology, including design methods, with the methods of
scientific research. The honourable state of service of the methodology of scientific
research probably contributes to this misconception. The methods of science have
been contemplated – obviously with success – for several centuries, while the meth-
ods of technology have been the subject of study for only a few decades now. In
this Chapter we explain first the fundamental differences between empirical science
and technology, the differences in aim and in process, and how they interact and
provide mutual assistance, based on a new state-of the art Research & Develop-
ment (R&D) process flow diagram, as developed by Binnekamp and Wolfert.
Moreover, we will distinguish the difference between physical (object) and so-
cial (subject) sciences both from an empirical viewpoint and from a new four
quadrant (4Q) model for the observable reality, as developed by Binnekamp and
Wolfert and introduced for the first time here. Finally, we invite the reader to
take a self-schooling path in order to understand the process of design which is a
conscious human act from a plan or scheme in the mind towards matter for, and in
connection with, other humans. We argue here that this process cannot be purely
one-sidedly empirical. Therefore the empirical scope will be extended in another
new four quadrant (4Q) model, as developed by Wolfert, in which object/subject
forms one axis and matter/mind forms the other. The purpose of this new 4-
Quadrant model is to facilitate the previously mentioned positioning question of
Odesys within a holistic scientific context.

63
64 CHAPTER 2. DESIGN IN THE CONTEXT OF SCIENCE & ENGINEERING

Bringing this Chapter out in the ‘open’ at all is exciting, of course. Especially
since most scientists and designers a-priori often avoid the fact that science and
engineering is more than just empiricism. Before you continue reading, we would
like to share the following quote with you: ”Ask us or dialogue with us and not
only with yourself if you want to understand us....and, if you are really willing
to empathize with us, don’t just impose your existing interpretations on us, like
many critics do”. We hope this open-ended Chapter will contribute to a better
understanding of the relation between the essence of open design systems and con-
scientific perspectives on R&D. This is an invitation to open your mind, heart, and
will for the remainder of the book that follows. Note: we will summarize in Sections
2.1 and 2.2 only the essential basic elements of R&D (research and development)
for the Odesys methodology. These build on, and/or show, specific elaborations
of the classic work of Roozenburg & Eekels (1995). An enlarged reference list for
a more extended con-scientific perspective is included at the beginning of Section
2.3. Other important basic work references are included in the text.

2.1. Scientist versus engineer

The word science is derived from the Latin word scientia, meaning ‘knowledge’.
Science is defined as a systematic enterprise that builds and organizes knowledge
in the form of testable explanations and predictions about the universe. The
word engineer (Latin ingeniator) is derived from the Latin words ingeniare (‘to
create, generate, contrive, devise’) and ingenium (‘cleverness’). Engineers are pro-
fessionals who invent, design, analyze, build, and test machines, complex systems,
structures, gadgets, and materials to fulfill functional objectives and requirements
while considering the limitations imposed by practicality, regulation, safety, and
cost. It is important to make this distinction because the line of reasoning and
end result that scientists aim for is fundamentally different from that of engin-
eers. Where scientists (‘researchers’) strive for knowledge acquisition, engineers
(‘developers’) strive for design action. This closely relates to the two domains that
humans function in: the domain of the material reality and the domain of the
mind. Given these two domains we can distinguish two directions: 1) a process
from the outside to the inside (from the material reality to the domain of the
mind) that we call knowledge acquisition - and, 2) a process from the inside to
the outside (from the domain of the mind to the domain of the material reality)
that we call design action. The process from the outside to the inside is directed
towards acquiring new knowledge of the world. The process from the inside to the
outside is directed towards change of the world, i.e. designing/creating/developing
new engineering solutions, see Figure 2.1. Both scientists and engineers start with
a problem. This problem points to an unsatisfactory situation which one wants to
change into a more satisfactory one.
2.1. SCIENTIST VERSUS ENGINEER 65

Incitement 2.1 Science of the artificial

(Prof. Herbert Simon, interdisciplinary scientist and Nobel Prize winner in economics)

Some radical viewpoints on (humanity) ‘sciences of the artificial’, which could be used for
engineering design conspection?

“. . . Eventually it becomes clear that human beings themselves belong to the realm of the
artificial. Indeed, they are probably the most important class of ‘artifacts’ given that they
are able not only to create other artifacts but also to re-engineer themselves to best fit
changing circumstances (i.e. ‘reconfigure the appreciative basis for their existence’).”

“ .. We can conclude that, in large part, the proper study of mankind is the science of design,
not only as the professional component of a technical education but as a core discipline for
every liberally educated person.”

“. . . Human beings’ external environment is complex, but their inner environment, the ‘hard-
ware’, is straightforward. It consists of a system that is basically serial in its operation, that
can process only a few symbols at a time and that is relatively slow to transfer information
to long-term memory. Superimposed on this are sets of generic control and search-guided
mechanisms, and memory-based learning and discovery mechanisms that permit the system
to adapt with gradually increasing effectiveness to the particular environment in which it
finds itself.”

“. . . From a reading of evolutionary history — whether biological or social — one might con-
jecture that there has been a long-run trend toward variety and complexity. If there is such
a trend toward variety, then evolution is not to be understood as a series of tournaments for
the occupation of a fixed set of environmental niches, each tournament won by the organism
that is fittest for that niche. Instead evolution brings about a proliferation of niches (i.e., a
purposive accumulation).”

“. . . Our essential task — a big enough one to be sure — is simply to keep open the options
for the future or perhaps even to broaden them a bit by creating new variety and new niches.”

“. . . Many of us have been unhappy about the fragmentation of our society into two cultures.
Some of us even think there are not just two cultures but a large number of cultures. If
we regret that fragmentation, then we must look for a common core of knowledge that can
be shared by the members of all cultures. A common understanding of our relation to the
inner and outer environments that define the space in which we live and choose can provide
at least part of that significant core.”
66 CHAPTER 2. DESIGN IN THE CONTEXT OF SCIENCE & ENGINEERING

For scientists the problem is that the available knowledge (a collection of factual
statements about the world) is not aligned, or is insufficiently aligned, to the
empirical facts. The facts are unassailable; hence the aim of scientific research
is to change, and respectively expand, the collection of factual statements (which
appeared to be insufficiently true) in such a manner that they align better with the
facts. The scientist (researcher) aims to elaborate from existing observations of
the past and of the empirical world a new comprehensive theory and/or knowledge
that can explain these observations.

Figure 2.1: Methodology of science/researcher and technology/engineer (elaboration of Roozenburg

& Eekels (1995)).

For engineers the problem at the onset is that the facts are not aligned with
our values and preferences concerning these facts. And since (in the first instance)
our values are unassailable, this discrepancy leads to us making it our aim to
change the facts, i.e. changes to the material world. We want to create a material
condition which agrees with our values and preferences. This requires design or
development action, which requires technical means and must be engineered, i.e.
designed. The engineer (developer/designer) aims to develop from an impossibility
in the now and in the material world a fit for purpose artefact that transforms this
impossibility into a new possibility in the future.

Figure 2.2: A scientist versus an engineer.

2.2. EMPIRICAL R&D, THE 4-QUADRANT MODEL 67

The essential scientist/engineer differences are summarized in Figure 2.2. For

this picture, suppose a scientist and an engineer are both lying on the beach in
the sun. The one wants to understand the relationship between the position of
the sun and moon and the tidal movement and the other wants to develop a new
product that can extract energy from the sun (and/or from tides, wind, or waves).

2.2. Empirical R&D, the 4-Quadrant model

Now that we have distinguished between scientists and engineers, we must also
further define the methodologies and/or methods they use, because their processes
are completely different. Clearly, research and development are somehow similar.
They have the same number of elements which, moreover, relate to one another
in a similar manner. One could conclude that technology is merely a form of
applied science and that, if you have scientific research, you ‘automatically’ have
technology and engineering development. We will show, however, that this train
of thought, which is indeed widely prevalent, is incorrect. To do so, we will explain
the essential methodological differences between the two process flows. Note that
in this section we discuss R&D within empirical/observable reality context (within
the open-ended con-science section 2.4 this will be elaborated further).

R&D methodologies
Figure 2.3 shows the basic process flows of scientific research and engineering
development methodologies, one beside the other. We shall refer to these from
now on as the research and development process flows, respectively. We will now
outline the differences between these R&D process flows.
Two types of problems We already stated that both process flows begin with
a problem. These problems appear to be different already:
• The research process flow is triggered by a discrepancy between current facts
(derived from observations) and our existing knowledge. The aim of the
process is adjustment of our knowledge to the facts. In other words, we want
to understand something that we do not fully understand now. This is the
scientific knowledge acquisition process.
• The problem at the onset in the development process flow is a discrepancy
between current capabilities and our values. The aim of the process is ad-
justment of current capabilities (by means of the engineered system) to our
values and preferences. In other words, we want to be able to do something
we cannot do yet by changing the material world. This is the engineering
design process.
68 CHAPTER 2. DESIGN IN THE CONTEXT OF SCIENCE & ENGINEERING

Figure 2.3: The Research and Development process flows, within an empirical/observable reality,
further developed by Binnekamp and Wolfert from Roozenburg & Eekels (1995).
2.2. EMPIRICAL R&D, THE 4-QUADRANT MODEL 69

Observation versus needs Research occupies itself with the existing real world
and with our representation thereof in factual statement. Development, on the
other hand, occupies itself with a not yet existing, but (hopefully) feasible world, or
worlds. The observation phase in research originally started with the observation
of facts from the empirical world that did not agree with existing theory. In
order to improve the theory, we need more than the establishment of one or a
few ‘anomalous’ instances. We therefore need purposeful observation to show that
the facts indeed do not agree with existing theory. This phase leads, by means of
induction, to the construction of a hypothesis. The analysis phase in development
is aimed at possible worlds guided by our needs. In this phase one can ask oneself
in reasoning under what conditions a world that has been thought up will be both
feasible and desirable. This phase leads, by means of deduction, to the set of
requirements that the engineered system will be judged upon and a provisional
design prototype as a first functional impression of the solution to the problem.
Note that both the construction of a hypothesis and the creation of a prototype
require creativity.
Results versus solutions The following two parallel elements of the two process
flows are ‘results’ and ‘solutions’. It should be possible to derive the phenomena to
be explained or predicted by means of deduction, from the theoretical relationships
acquired from induction. This is what one tries to do in the ‘deduction phase’.
We can state that deduction in the research process flow leads to a categorical
explanation and/or prediction of one or more aspects of reality. Arriving at results
is done by means of a chosen research method. The results will be tested against
the hypothesis to prove the general validity of the new theory. The application of
development methods will lead by means of ‘innoduction’ to a provisional solution
that meets the user needs, see Roozenburg & Eekels (1995). This is what will
be verified against the requirements. Appendix A contains an overview of the
different research and development methods.
Testing versus verification Testing within scientific research can direct itself
to the explanatory power or the predictive power of the postulated laws or theor-
ies. In view of the inductively acquired hypothesis, deductively a prediction has
been made (with or without the help of an experiment) on facts to be observed
in the future. In the testing procedure these facts are observed and compared
with the prediction. Does it fit the observations? If not, to what extent do the
observations ‘support’ the hypothesis, that is how ‘true’ is the hypothesis? During
verification in the development process flow, comparisons are made as well, albeit
not between fact and theory, but between (simulated) system behavior and the
desired behavior of the system to be developed. Does the engineered system meet
the requirements on all system levels? If not, what adjustments need to be made
to (parts of) the system?
70 CHAPTER 2. DESIGN IN THE CONTEXT OF SCIENCE & ENGINEERING

Evaluation versus validation In the research process flow ‘evaluation’ does

not judge only on how well predictions fit observations. A decision is also taken
of whether the goal laid down (improved theory) has been sufficiently attained, or
whether more observations are required. Hence, the feedback arrow which runs
in Figure 2.3 from the element of ‘evaluation’ back upwards. If the evaluation
has been satisfactory, it is decided to add the knowledge which the process has
yielded to the acreage of knowledge in the domain of the mind. Usually this takes
place more explicitly in the form of a scientific publication. In the development
process flow we encounter the element ‘validation’ at this level. As with research,
validation does not judge only on whether or not the obtained solution meets the
requirements, but also whether the goal (an improved system) has been sufficiently
attained, or whether an adjustment of the needs is required. Finally, the decision
can refer to choosing an attractive alternative from the collection of generated
solutions. The process ends with the yield of a number of acceptable solutions,
or - one decision step further - with the manufacturing or implementation of the
most attractive solution, i.e. engineered system (artefact).

The 4-Quadrant R&D model

Natural science is one of the branches of science concerned with the description,
understanding and prediction of natural phenomena, based on empirical evidence
from observation and experimentation. Physics is a natural science that involves
the study of matter (tangible objects) and its motion through space-time, includ-
ing concepts such as energy and force. Technics (technology) as part of physics is
the theory of practical/industrial arts/crafts and/or the application of such know-
ledge to achieve practical/industrial goals. Social science is one of the branches of
science, concerned with the study of societies and the relationships among persons
(living subjects) within those societies. Economics is a social science that studies
the production, distribution, and consumption of products and services. Manage-
ment as part of economics is defined as the organization and coordination of the
(human) activities of business organizations to achieve defined objectives. Man-
agement consists of the interlocking functions of organizing, planning, controlling,
and directing a (human) organization’s resources in order to achieve its objectives.
We have now distinguished between the physics from the natural domain and
management from the social domain and for this we have already made the dis-
tinction between scientific research (investigation) and engineering development
(design). This allows us to define a research and development framework contain-
ing four types of R&D domains. Figure 2.4 shows the distinction between: 1) the
research-oriented approach aimed at understanding, focusing on either physical ob-
jects or human organization processes, resulting in new knowledge following from
a research question and, 2) the development-oriented approach aimed at enabling,
2.2. EMPIRICAL R&D, THE 4-QUADRANT MODEL 71

focusing on either objects or processes, resulting in new solutions. Note: the Fig-
ure 2.4 is the so-called 4-Quadrant (4Q) model and was developed by Binnekamp
and Wolfert, as an extension of Roozenburg & Eekels (1995).

Figure 2.4: 4-Quadrant (4Q) model with four types of empirical R&D domains.

Depending on the quadrant, the methods used to arrive at the graduation de-
liverable will differ. Figure 2.5 shows typical methods, either for research (Q1/2)
or development (Q3/4). Scientific research, when focusing on physical objects,
makes use of research methods such as lab testing, statistical analysis, sensor-
ing/monitoring, data mining, etc. Scientific research, when focusing on human
organization processes, makes use of research methods such as case studies/focus
groups, surveys/interviews, statistical analysis, evaluation, etc. Engineering de-
velopment, when focusing on physical objects, makes use of development methods
such as physical/numerical modeling, technical optimisation, product lab testing,
proof of concept validation, etc. Engineering development, when focusing on hu-
man organization processes, make use of development methods such as systems
modeling, multi-objective optimisation, simulation/programming, model testing
and validation, etc. A more exhaustive list of research and development methods
is provided in Appendix A.

Finally, the formal distinction made in this section between the scientist/ re-
searcher and the engineer/developer does not imply that they work entirely in their
own specific cycles and that the work of a researcher has no development com-
ponents at all, or vice versa that a development/design project has no research
72 CHAPTER 2. DESIGN IN THE CONTEXT OF SCIENCE & ENGINEERING

component at all. For example, it is not uncommon that, before the start of a
design process, more knowledge is required. For the acquisition of this knowledge
the research cycle can be used. For instance, in order to optimise an engineering
system the relation between the different engineering variables needs to be better
understood. In other words, it can be that the engineer must carry out some
(minor) empirical research as part of the development process. We emphasize that
in that case the research cycle precedes the development cycle and the line of reas-
oning for the research cycle will be opposite to the development cycle as mentioned
earlier. The main focus of the engineer, however, will be on the development cycle.
Conversely, a researcher may need to design a specific experiment to answer their
main question or to prove a hypothesis. For this, the researcher can go through
a (mini) development cycle as part of the research process. The latter should not
be referred to as ’research by design’, which is a misleading term in the context of
research and development because the main focus of the researcher is to acquire
knowledge by designing an experiment as part of the research method (in this
example).

Figure 2.5: 4-Quadrant model of empirical R&D domains and the nature of their R&D methods.
2.2. EMPIRICAL R&D, THE 4-QUADRANT MODEL 73

4-Q model applied to a real-life project Let us consider the ‘Rotterdam-

sebaan road project’ aimed to improve the accessibility to The Hague and the
region. For each quadrant we give examples of possible research questions (RQ)
and development statements (DS) relating to this project and the related research
or development process, see also Figure 2.6. Moreover, the interested reader in
industrial R&D management and innovation is referred to Van Gunsteren & Vlas
(2022). The example RQs and DSs read as:
Q1 - RQ: “What is the effect of the tunnel boring machine on the geometrical
location of existing infrastructure?”
Such a question relates to the physical effects of the tunnel boring machine on
its environment, such as existing real estate. It requires measuring over time the
exact position of building components by means of measuring instruments. It
would require lab testing of the instruments, statistical analysis of the acquired
sensor and monitoring data over time, and possibly data mining. The end result
would be insight into whether or not change of the geometrical position of building
components can be attributed to the tunnel boring machine.
Q2 - RQ: “What is the relation between project management team composition
and acquiring project sustainability goals?”
Such a question relates to gathering insight into the effect of team composition
on the realization of certain project goals that determine whether or not goals
with respect to sustainability have been achieved. It would require multiple case
studies of which the Rotterdamsebaan road project would be just one, setting up
and organizing surveys or interviews, and performing statistical analysis on the
results obtained. The end result would be insight into which factors in relation to
team composition contribute to achieving goals that define sustainability.
Q3 - DS: “To develop a machine that enables the re-use of existing asphalt for new
roads.” Such a statement relates to the design of a crushing, filtering, and mixing
machine that maximises the output of asphalt material that is of good enough
quality to be used for the new roads. It will require physical and/or numerical
modeling of the recycling process, possibly in combination with lab testing and
optimisation of the machine so that it can produce material of good enough quality.
The end result would be a design of a first ‘proof of concept machine’.
Q4 - DS: “To develop an optimisation tool that enables finding the optimal infra-
structure intervention strategy for an infrastructure network.”
Such a statement relates to optimising the timing of infrastructure interventions
(e.g. maintenance, renovation, etc.) so that they have minimal impact on society.
It would require modeling of the infrastructure network planning, optimisation
of intervention measures using simulation, and finally testing and approving the
created decision support model.
74 CHAPTER 2. DESIGN IN THE CONTEXT OF SCIENCE & ENGINEERING

Figure 2.6: 4-Quadrant model of empirical R&D domains and types of research questions (RQ) and
development statements (DS) for a real-life construction project.

4-Q model applied to an engineering management faculty We now con-

sider research and development within both civil and/or offshore engineering and
construction engineering and management domain. For each quadrant we give
examples of possible research questions (RQ) and development statements (DS)
relating to this faculty and the related research/development process, see also
Figure 2.7. The example RQs and DSs read as:
Q1 - RQ: “What is the buckling behavior of I-section high strength steel columns?”
Such a question relates to the physical behavior of a steel column. It requires
lab testing where by means of measuring instruments the buckling strength under
different conditions can be determined. It would involve mathematical modeling
of the material behavior. The end result would be an improved insight into the
material behavior.
Q2 - RQ: “What is the last planner system’s (LPS) impact on project cultures in
terms of partnering?”
Such a question relates to gathering insight into the effect of a specific project
management control system on the project’s culture and related associative or-
ganizational properties. It would require multiple case studies, setting up and
organizing surveys or interviews, and performing statistical analysis on the results
obtained. The end result would be insight into whether the application of the LPS
has an effect on the project culture and related mutual understanding.
2.2. EMPIRICAL R&D, THE 4-QUADRANT MODEL 75

Q3 - DS: “To develop a pile driving system that enables the removal of piles
without disturbing sea life.”
Such a statement relates to the design of a pile driving machine that uses a novel
pile driving technique such that vibrations and noise disturbance are minimised.
It will require physical and/or numerical modeling of the pile driving process,
possibly in combination with lab testing, and optimisation of the machine so that
it can meet the stated requirements. The end result would be a design of a first
‘proof of concept machine’.
Q4 - DS: “To develop an optimisation tool that enables keeping a project schedule
at target delivery date.”
Such a statement relates to mitigating the effects of risk events and project disturb-
ances so that they have minimal impact on the project delivery date. It would
require modeling of the network planning, optimisation of mitigation measures
using simulation, and finally testing and approving the created decision support
model.

Figure 2.7: Four types of R&D projects and related research/ development methods for an engineering
management faculty.
76 CHAPTER 2. DESIGN IN THE CONTEXT OF SCIENCE & ENGINEERING

Incitement 2.2 Ethics and morality

(Dr. Rudolf Steiner, philosopher/ humanities scientist)

A radical viewpoint on ethics and morality, which could be used for engineering design con-
spection?

”... There is no separate science of good action. There are no general prescriptions for what
people should do. Ethics can only exist as descriptive of facts. It can describe what norms
and values were or are actually used by individuals or groups, identifying the motives and
momentum that have worked or are working concretely in individuals.”
”. . . Ethical research has only one fundamental hold: the ability of individual human beings
to act freely. Free action occurs when the ‘contradiction’ between motive and impulse is
removed by the fact that both coincide in pure intuitive thinking. It is not some interpret-
ation of the term ’good’ that is the criterion, but the understanding that free actions exist.
The basis for ethics is the intrinsic ability of individual human beings to act freely.”
”. . . Morality can only be created by a human being. Where an individual initiates some-
thing out of free will and thus from the idea, morality is created. The individual adds
something from himself, out of the ‘ego-reach’ (from the inner self), to the world. The one
who acts dutifully, maintaining standards obediently and decently, is by no means despic-
able, but neither is he moral. He continues the past without creating future. Moral is the
action that creates future.”
”. . . The so-called noble statement is actually an aberration: ’There is a need to edu-
cate people first, to improve morality’. Spiritual science, however, says: emphasis on this
principle does not do it alone, but the means by which the soul can be ennobled must be
imparted. For if by a spiritually directed worldview the souls are ennobled and sharpened,
then circumstances and external relations, which are always a mirror image of what man
thinks, will emulate. Not by circumstances are people determined, but, insofar as circum-
stances are social, these circumstances are made by people. If a man suffers under social
conditions, he suffers in truth from what his fellow men inflict upon him.”

What might this mean for the open design system and its outcomes? At least let it be
food-for-thought during the conspection of socio-technical design synthesis outcomes...

2.3. Values of science & engineering

In this section, we want to capture the essential difference between the value of
science and engineering. This ultimately determines how we can position design
within the empirical R&D context (see further Chapter 9).
2.3. VALUES OF SCIENCE & ENGINEERING 77

Scientific research
Any scientific theory lasts only until it is replaced by a ‘better’ one. This negation
principle is based on Popper’s falsification principle. A theory like ’all swans are
white’ is a theory that satisfies Popper’s principle because it is falsifiable. The
theory holds until the first non-white swan is seen. An important consequence
of Popper’s principle is that a theory can never be seen as the ultimate truth. A
statement such as ’the science is settled’ is therefore contrary to Popper’s principle.
Note: this means that a hypothesis and a related theory including its possible
limitations will hold until it is overturned by a new one and the aforementioned
limitations are (partially) dissolved.
The question then is how ’better’ is defined as the motivation to exchange the
old theory for the new one. For this, ’Ockham’s razor’ is used. Ockam’s razor is a
principle that states that when two explanations exist for the same phenomenon,
the simplest explanation should be chosen. The principle of scientific progress is
not complicated. A theory is used as an explanation for phenomena in reality.
A useful example is the transition from the geocentric model of the universe to
the heliocentric model. The old theory was that the Earth is at the center of
the universe. However, this theory could not be used to explain why some planets
exhibit retrograde motions. For example, Mars moves from right to left, but some-
times this movement is reversed, after which the planet continues from right to
left. In order to be able to explain these movements, a complex system was used.
Epicycles played an important role in this. These are auxiliary circles to be able
to explain the retrograde movements. A new theory, where the sun was placed
in the center, could explain these movements without having to use complicated
models such as the epicycles. The new theory thus satisfies the principle of Ock-
ham’s razor and must be preferred over the old theory. A final important question
within the context of (empirical) sciences and engineering is the issue of reliable
‘verification and validation’. Reproducibility/replicability is a core principle in
this. In all four quadrants, the reliability of knowledge and product acquisition
or generation is paramount. Empirical claims about research and development
should become credible not by the status or authority of their originator, but by
the reproducibility of their supporting evidence as a means of verification. Sci-
entists/researchers try to transparently describe the methodology and resulting
evidence used to support their claims/hypotheses. Engineers/developers seek to
demonstrate that their new product meets the user requirements it was designed
for and that the result adds value. So as a result, we can state that verification:
(1) in the empirical research/science context is about the replicability of the res-
ult of observation/new knowledge , (2) in the development/engineering design is
primarily about the replicability of the constructed artefact/new product. (2) is
most probably different for mind sciences (e.g. mathematics or logics), because of
78 CHAPTER 2. DESIGN IN THE CONTEXT OF SCIENCE & ENGINEERING

its deductive axiomatic nature or specific other reasons (see next section).
Note that within the empirical context we argue that a distinction should be
made between living and dead nature (between subjects and objects). Replicabil-
ity/reproducibility assumes randomness in time and place (e.g. an experiment in
which salt is dissolved in water can be repeated at any time and place with the same
result). We argue that in the case of living nature there can never be 100% ran-
domness and this is why in empirical social science sufficient repetition of and/or
conditioning circumstances regarding a social experiment must be observed. In
other words, one cannot apply to living nature one on one a purely materialistic-
mechanistic research approach. Therefore there are even social scientists who go a
step further and place their research approach under the denominator of (social)
constructivism (instead of empirical sciences) in which the validation/ validity is
’more important’ than the verifiability/ repeatability because knowledge and real-
ity are actively created by social relationships and interactions (verification shift
towards reproducibility of the construct rather than an exact replicability of the
results, which is sort of similar to verification within an engineering design context,
see next section).
The question remains how to deal with the conspection in an engineering design
context, especially when it concerns socio-technical problem solving? We will
address this fundamental question, particularly for the Odesys methodology, in
more detail in the following sections.

Engineering development
Science deals with objective explanations of natural phenomena as stated before.
Human values ideally have no place in this process. The opposite holds true for
engineering development. The process of engineering development is initiated by
a subjective discrepancy between what human society wants and what the current
state of technology has to offer. What is considered the ‘best’ engineering solution
is also subjective as it depends on human individual values and preferences. There-
fore, there can be no single objective best solution. Could we thus conclude that
design is not a part of empirical sciences (we return to this in the next section)?
As David Hume stated: “Beauty is no quality in things themselves: It exists
merely in the mind which contemplates them; and each mind perceives a different
beauty. One person may even perceive deformity, where another is sensible of
beauty; and every individual ought to acquiesce in his own sentiment, without
pretending to regulate those of others. To seek the real beauty, or real deformity,
is as fruitless an inquiry, as to pretend to ascertain the real sweet or real bitter.”
Ethics relates to moral values. Because engineering is tied to values and/or
preferences it must therefore also relate to ethics. As Steiner stated: ”There is
no separate science of good action... and, the basis for ethics is the intrinsic
2.3. VALUES OF SCIENCE & ENGINEERING 79

ability of individual human beings to act freely” (see incitement 2.2). Should
engineers still be critical of the technology that is their livelihood, or should they
only be interested in making their machine work, indifferent to any long-term
social impact? For example, the American Society of Civil Engineers answered
this question by adopting a code of conduct for their members (already in 1914).
According to this code engineers uphold and advance the integrity, honor, and
dignity of the engineering profession by: 1) using their knowledge and skill for
the enhancement of human welfare and the environment, 2) being honest and
impartial and serving with fidelity the public, their employers, and clients, 3)
striving to increase the competence and prestige of the engineering profession,
and 4) supporting the professional and technical societies of their disciplines (see:
asce.org/career-growth/ethics/code-of-ethics).
The question then arises how to value the ‘enhancement of human welfare
and the environment’ of a given engineering artifact. If we take a military drone,
specifically engineered for destruction: did the engineers working on this project
enhance human welfare and the environment? We can also take a Dutch brewery
called Gulpener that recently re-engineered their brewery installation and takes
pride in how it values socio-eco principles. Although both examples are value
driven, we can use Maslov’s theory of the hierarchy of motivation/needs, for ex-
ample, to add some perspective to the valuation of engineering activities. At the
bottom of human needs, according to Maslov, are physiological/ biological needs
that are vital to human survival. Some examples of physiological needs include
food, water, and breathing. The military drone may also relate to this level as it
closely relates to needs for survival. At the top are transcendence needs. ”Tran-
scendence refers to the very highest and most inclusive or holistic levels of human
consciousness, behaving and relating, as ends rather than means, to oneself, to
significant others, to human beings in general, to other species, to nature, and to
the cosmos, see Maslow (1971). The Gulpener brewery, considering their socio-eco
purpose motivation, would relate to this holistic human conscious level.
Designing leads to the blueprint of the product plus directions for its particular
use. These in turn are, as a description of a larger class of possible realizations,
of a general nature. Therefore engineering designing follows a line of reasoning
from general to general which Roozenburg & Eekels (1995) call innoduction. Note
that this innoductive line of reasoning is also applicable to pedagogy. Ideally the
final design best represents all stakeholder values and preferences. In that case
the optimal design solution is a mirror of all stakeholders values and preferences.
The validation step is where this check is carried out: Does the proposed design
solution indeed meet the users common socio-eco interests and is the designed
artefact to the valuable for the user? So, outcome verification shifted towards
reproducibility of the construct rather than an exact replicability of the results,
and has thus become, so to speak, secondary to validation (human value validation
80 CHAPTER 2. DESIGN IN THE CONTEXT OF SCIENCE & ENGINEERING

which is ‘random’ by nature, and process verification rather than result verifica-
tion)! According to this reasoning, we could conclude that engineering design is
an academic discipline while maybe not belonging to the empirical sciences, which
we will further explore in the next section.
Incitement 2.3 From mind to matter

Three men talk about their just-completed fence in a garden. They

see the center-to-center distance of the posts they observe, the type of
wood used, they measure the height of the fence and run through a few
other design variables. . . . These same three men (two residents of the
house to which the garden and fence belong and a friend who maintains
their garden) were talking some time ago with local residents, contextual
stakeholders, who also live in this environment. They talked about how
to fit a new fence within the neighboring gardens, they discussed when
it would be most convenient for this fence to be constructed, and many
other common interests to design a new fence. . . . The same three men
(one is an artist singer, the second an arborist, and the third a professor)
talked about their ideals in art, nature, and education. From these
idealized design principles, they intended to create an artful fence in
which the tree would recur as a very essence with beautiful branches
and where the design process would be open-ended by nature. One good
evening, they had a ‘gut intuition’ and made a sketch based on a woven
fence and then built it accordingly...

The question now is, do we see their three stories reflected in the end
result or is this just a bare fence? Can we observe their ideas back in the
created artefact, are these in there, or are these only in the three men or in
both? And, what does this answer mean for art and architecture history,
where the ‘creator’ is mostly no longer there (who or what then does tell
his ”story” )? How did that path from mind to matter really unfold and
how did their ‘gut intuition’ come about? Finally, can we actually see the
gravity and normal force(s) from the chair standing by the fence? And
are these forces a cause or an effect of something? And how do we model
and determine these forces?

2.4. Con-science, the extended 4-Quadrant model

In this open-ended section, we first call for an extension of current mainstream
materialistic science to a holistic science from the whole human both with its inner
and outer world: i.e., a science from a spiritual consciousness as a continuation
of pure sensorial empirical science. Both of these are complementary and form
sciences as a whole, as indicated for instance by Goethe, see Bortroft (1996). We
therefore term this integrative form of science here: con-science (with a reference
to a conjunction of conscious and science: con-science, as conceived by Wolfert).
Secondly, in this section we give an initial impetus for scientists and/or students
to transform within themselves to embark on a new integrative path of intuitive
2.4. CON-SCIENCE, THE EXTENDED 4-QUADRANT MODEL 81

thinking and develop within themselves a different kind of scientificity: i.e., ‘food
for thoughts’. So if you dare to take seriously at all that science is not bounded
by the physically sensible, perhaps for the time being only as a hypothesis, then
it is possible to start familiarising yourself with this extension and then assess for
yourself what this scientificity and intuitive thinking schooling could offer (towards
a holistic scientist or an ζ-engineer, see Chapter 9). Third, we conclude this section
with some research questions for further self-exploration. If readers engage with
these, it will help them better understand Odesys’ position within this holistic
science context (see further Chapter 9). After all, humans as designers design
from mind towards matter for and in connection with other humans. We argue
here that this position cannot therefore be purely one-sidedly empirical. We leave
it to the reader to determine (t)his position after reading this section. Finally, it
requires from the reader, first of all, an open-mind to enable a movement from
science to con-science (in Dutch from ‘wetenschap naar gewetenschap’), a journey
on the edges or boundaries of the empirical/ materialistic science.
In addition to Wolfert’s (practical) experience and many years of being connec-
ted with and/or training within this con-scientific context, the following references
are also works on which the following sub-sections are based in part and are re-
commended for interested readers who would like to further educate or develop
themselves in this field: e.g., Barendregt (2022); Bortoft (1996); Gallagher (2013);
Hegel (2018, 2021); Heusser (2016, 2022); Husemann (1994); Katz (2011); Mos-
muller (2018, 2021); Selg (2022); Simon (2019); Soesman (1998); Steiner (1995,
1987); Steiner & Mulder (2022); Varela (2017); Van Lommel et. al (2009); Velmans
(2017); Zajonc (1995, 2008).

Natural-matter, formal-mind & phenomenology

We argue here that the open design process cannot be purely one-sidedly empir-
ical. Therefore the empirical scope will have to be extended in another new four
quadrant (4Q) model in which object/subject forms one axis and matter/mind
forms the other. The purpose of this new 4-Quadrant model is to facilitate the
previously mentioned positioning question of Odesys within a holistic science con-
text. Before introducing this extended model, we will first distinguish three areas
within science: natural-matter, formal-mind and phenomenological sciences.
Physics as part of natural-matter sciences In Greek-Roman times, man
began describing cosmic movement in familiar modeling based on the geocentric
worldview of Ptolemaeus: the earth at the centre and the sun and moon circling
around it, the planets whirling in intricate orbits between them and the fixed stars
in stable distribution across the sky dome as a spinning background. Then around
1540, Copernicus appears on the scene. He posits that the sun is in the centre: the
heliocentric worldview, with which, incidentally, he also came into conflict with
82 CHAPTER 2. DESIGN IN THE CONTEXT OF SCIENCE & ENGINEERING

the church that proclaimed the geocentric worldview. In the early 1600s, a new
researcher named Kepler stands up. Among other things, he concludes, partly
based on the empirical preliminary work of his teacher Brahe, that the planet
Mars does not follow a pure circular orbit. In the years between 1609 and 1919, he
describes three new laws of motion, and with these we see Kepler slowly moving
towards a mechanistic worldview.
Then, in 1687, Newton appears on the scene with his main work Philosophiae
Naturalis Principia Mathematica (note part of philosophy sciences). In it, he for-
mulates his four laws of nature in which (gravitational) force now plays a role
instead of motion-causing planets spirits. Newton in this way breaks with Aris-
totle’s 2000-year-old thesis that everything falls down because the centre of the
earth is the ’natural place’ of matter, but in the cosmos this does not apply ac-
cording to him. Incidentally, over two centuries later, Steiner (1987) points out
that Newton’s understanding of Kepler’s third law is purely mathematical. Such
a step requires abstraction that separates experience from science. While that
was necessary for the next step in our consciousness and development, this purely
mechanistic view does not help us to prepare for the next step. He further points
out the curious thing in Kepler’s third law in which time is squared. What is es-
sentially happening then? Perhaps this is a hint to approach time differently than
we are used to in our present time. Time should perhaps be linked less as a fourth
dimension to our physical three-dimensional space, but rather as something that is
essentially non-physical, showing itself in our physical world as linear chronological
time (an ’independent’ one-dimensional affine space).
To this day, the gravity force remains a riddle. It can be computed fine and is
very useful in engineering, but what is essential remains a mystery. For how can In
empty space bodies attract each other (note: so are we dealing here with a law of
nature based on thoughts alone?). In May 1920, Einstein gave a lecture in Leiden
in which he reintroduced the now abandoned idea of ether as a medium based on
his general theory of relativity but now as an imponderable non-physical medium.
Later, Einstein says that empty space becomes curved in the presence of a (large)
object causing a second moving object to follow its trajectory according to that
curvature, which can be a circle or elliptical orbit. That approach solves some
shortcomings in Newton’s theory, but it also does not yet explain what gravity
really is.
In 2009, a Dutch physicist Erik Verlinde advanced the hypothesis that gravita-
tional force may well have something to do with communication. Thus, he was able
to derive Newton’s law of gravitation based on differences in information density in
space. Thus, like Einstein, he arrives at something that also suggests the existence
of an all-pervading ether, and that gravity can be considered an effect of something
and not something fundamentally. That would both put the theoretical idea of
dark matter in a different light and make the idea of the Big Bang obsolete. It
2.4. CON-SCIENCE, THE EXTENDED 4-QUADRANT MODEL 83

is probably too big a leap but, when an information-bearing ether comes into the
picture, the idea that information qualifies as thoughts or words (‘dia-logoi’) comes
to mind. In other words, when we look at this dialectically (as in Hegel’s scientific
system) could force be the synthesis or unity within the threefold of matter-being/
non-matter-essence/ force- concept? Like energy is the synthesis of visible matter
and invisible light? Or even one step further, could we also experience such a con-
nection spiritually/religiously with the word of creation that in ’in the beginning
there was...’. Sprouts for a holistic or con-science practiced by scientists who in
continuation to their empirical experiences take spiritual experiences seriously and
are willing to further ‘ex- and/or investigate’ (in Dutch ‘onder- en bovenzoeken’)
these hypotheses from an open basic attitude, standing between mind and matter.

Mathematics as part of formal-mind sciences Spirituality is about the

personal handling of the unknowable. The unknowable, or unknowable part of our
reality, is the part to which reason and logic have no access. Spirituality is about
that which transcends everyday life: not living from a system or a set of rules but
from an experience-based vision of man and life. ’Certainty’ is hardly for sale in
our world, and that is not an annoying side effect but rather a meaning of the first
order. The nature of uncertainty in the world is fertile ground for philosophical
contemplation. We might ask ourselves the question, do coincidences exist or not?
Or is this uncertainty question irrelevant? When rolling a die, we simply postulate
that the probability of each outcome is 1/6 and that seems to work perfectly well
despite the fact that the movements of a dice could also be perfectly well described
by very complicated deterministic mechanical laws. In other words we choose to
use a probability model because it suits our intuition. This model provides an
explanation in terms we find acceptable and a working with ”not knowing for
sure” from a paradigm chosen by the scientist. For some phenomena it is obvious
to use a probability model, for other phenomena there seems to be more freedom
of choice.
Spiritual life could be described as living in connection with all that is hap-
pening In the world around you. A sudden insight into the world is ’spiritually’
charged for this very reason because it brings the one who undergoes it deeper into
connection with the reality of the world around us. When this insight is (for now)
only to be followed by yourself, we quickly speak of faith. We could ask ourselves
the question of what is the difference between faith and insight (quite a difficult
question, by the way)? If we all shout things we think we know based on what
scientists say then in a sense our ”knowledge” is also a belief. Think of regular
scientific insights like ’diseases are hereditary’, ’viruses exist’, and ’particles col-
lide in an accelerator’. These I have to believe on the basis of statements made by
scientists who have studied them. Actually then it does not make much sense to
think about faith and insight in this way because a lot of what we would say in
84 CHAPTER 2. DESIGN IN THE CONTEXT OF SCIENCE & ENGINEERING

life is based on faith in this way. Perhaps then the question of how we relate to
the source of (spiritual) insight is more essential?

Incitement 2.4 Self-observation of mind vs. Empirics-observation of matter

Facts, such as the Eifel Tower stands in Paris, can be ascertained by outer experience and
through the empiric system. A fact that ’salt dissolves in water’ in a certain place, with a
certain composition can also be ascertained empirically, but cannot be ratified as a universal
truth all-encompassing law: i.e., an outer experience. However, no one can say that they find
the mathematical truths through outer experience; but one finds them because everything
is realised inwardly: ’an act of the mind’. Logic arises in the inner and not from perception.
If one wants to show that the three angles of a triangle add up to 180 degrees, one does so
by drawing a parallel line with the base line through the top angle and putting the three
angles together in a plane; then angle a = d, b = e, c equals itself; and so the three angles
equal a stretched angle, equal to 180 degrees. Whoever has once realised this, knows that
it must be so for all triangles, just as one knows, once one has realised it, that three times
three is nine.
These most trivial and universal truths of all, the arithmetic, the geometric, are found in
the inner world, and yet people do not argue about them. There is absolute agreement
about them, because today man is so far along to see these things. There is no agreement
only so long as pure truth is clouded by the passions, by sympathy and antipathy? Could
it then sometimes be a great truth, a great law, that the most individual truths, found
in the most inner and pure way, would at the same time be the most universally valid
ones? And could it be that when design, which is also an act of the mind’, is emptied of
passion, manipulation and power, and gone through in a pure and inner way (if possible
supported by pure open source mathematical models derived from the mind), leads to a truth
of and for all concerned? Or is design actually a part of the spirit-sciences or humanities
(just as mathematics is part of mind sciences)? And, is it an empirical fact or an inner
experience that the most beautiful Carnaval is celebrated in Oeteldonk, and for whom is
this a (universal) truth? What do you see and/or perceive when you look at a man in a
farmer’s keel in the middle of the summer at a certain spot in a certain city, and what do
you see and/or perceive when you look at this same man with the same farmer’s keel at the
same spot in the same city six months later during Carnaval? What can this tell you about
your produced ‘thought-content’ and your observation with the ‘naked eye of the beholder’ ?
Note: mathematics, which takes place in the inner mind, can, for example, prove that the
surface area of two figures is equal, but it cannot, and need not, answer the question of
what identity means. Indeed, this inner concept transcends mathematics, because it occurs
elsewhere, namely in everything that is.
2.4. CON-SCIENCE, THE EXTENDED 4-QUADRANT MODEL 85

Next we might ask if mathematics itself is to be practiced in a spiritual way, or

is mathematics spiritual by nature and not just logically rational? Many people
will want to speak against this and argue that mathematics is simply just practiced
with axiomatic and deductive logic, proving one theorem after another. When we
look at it that way, there is no spirituality. But that is actually quite a flat view
of mathematics in which mathematics is completely reduced to logic. Namely, no
mathematician is able to derive his or her results via only logical steps from basic
axioms. Logic is necessary but is not the core of mathematics. Indeed, surely no
one wants to maintain that mathematics consists merely of algorithmic reasoning
that a computer could also generate? If that were the core of mathematics who
would want to do it? No, the core of mathematics consists of the unpredictable
non-conformist and/or non-conventional ways found for problems and the way we
communicate these solutions. Mathematics therefore stands above all for creativ-
ity. In communicating mathematics, we are always in need of a creative leap.
There is no mathematical language that can replace understanding and insight,
no formula that can express everything. What exactly happens the moment one
thinks one understands what another is trying to explain to me? What does it
mean that a particular result is understood by only a few people in the world?
Mathematics viewed in this way is in itself spiritual because it allows people to
connect with the world with each other’s thoughts and with the question of what
everything in mathematics now represents in the world around us: the principle of
reflection (see Chapter 5 and onwards for this principle within the Odesys context).
Intermediate note: if we apply complex contour integration to solve differential
equations representing a wave radiation problem within a moving object system,
we can obtain poles that lie on the real axis (with an imaginary part equal to zero).
This leads to meaningless results, although the mathematical operations can be
performed well. Therefore, we have to apply the principle of reflection and assume
here that in reality there will always be viscosity in a real-life system and that
radiated waves always go from the source to the outer environment. Therefore,
these poles will actually move into the complex plane and thus contribute in a
physical way to the resulting mathematical outcome, see Wolfert (1999).
We argue that according to the definition in this section, this is a spiritual
(mind) act, because we are connecting abstract mathematical theory with a living
world around us. The truly meaningful moments are those in which you recognize
why we must find a certain proposition or assertion to be true: logical verification
is then nothing but a process after the fact although necessary but not really
the core of mathematics. The moments of truth finding are often very spiritual
moments in which the mathematical scientist connects with what is real. Such
event transcends the everyday and one really does not have to be spiritual in any
sense to see and feel the extraordinary power of this and the authenticity of such
moments. Mathematics develops in the moments when things are problematic
86 CHAPTER 2. DESIGN IN THE CONTEXT OF SCIENCE & ENGINEERING

and at times when more is required than technical skills alone. These skills are
necessary but ultimately they are not what matters. You could therefore make a
comparison with art and speech. Human speech formation or music does not come
about through technique alone, but it is the whole symbiosis of tones, rhythm,
timbre through which music and speech ’emerge’. It is true that a good (speech)
technique is necessary to be able to speak or play music. Technique is a means
and not an end in itself. Art cannot be reduced to technique. To claim that
mathematics is only logic is the same as claiming that art consists only of technical
use of inner and outer instruments.
Phenomology (Goetheanian science) Regular materialistic-mechanical sci-
ence has brought us tremendous natural scientific and technical knowledge. With
this form of empirical science using the physical-sensory, it often becomes possible
to satisfy human needs and (physical) desires in an easier way than before. How-
ever, it is not convenient to employ this form of science when the object of desire:
(1) cannot be reached at all (or because it is only an instinct-driven object), (2)
brings adverse consequences for man and environment, or (3) leads to loss of the
actual human in man. Then this outward form of science will lead to disastrous
events and crises which we have recently experienced.
For these reasons, it is good that other types of research also exist. That
is the inward research, the research into and/or through our consciousness. An
extremely successful example of this is the following. Around the year 1800 the
physical theory about colours was that they are one-dimensional phenomenon.
Light comes from different wavelengths and one of them determines the colour
of light. The artist and (spiritual) scientist Goethe who was also interested in
observation phenomena came up with another hypothesis in 1810. He stated that
colours are a three-dimensional phenomenon for the following reasons: if we have
1,000 cubes that are plain but differently coloured, it is not possible to line them
up in such a way that the colours flow evenly. Nor is this possible in the flat
plane. However, in a larger cube of 10 times 10 times 10 it is possible to arrange
the colours in such a way that the colours flow evenly in all directions. We call
this observation phenomenological as it relies on direct observation independent
of thought. The physicist insist that colours are a one-dimensional phenomenon.
In the 19th century, the physician Young and later the physicist Helmholtz tried
to unify Goethe’s observations with those from physics. They hypothesized that
the eye has three different receptors for colour perception. If this is so, then
a single wavelength transmits three impulses to vision. Colours are then one-
dimensional in their formation but three-dimensional in their perception. Thus
was born the Young-Helmholtz theory, also known as the trichromatic theory,
which is a theory of trichromatic colour vision - the manner in which the visual
system gives rise to the phenomenological experience of colour. This hypothesis
and associated theory was finally demonstrated for the first time by Svaetichin
2.4. CON-SCIENCE, THE EXTENDED 4-QUADRANT MODEL 87

in 1956, which was some 150 years after Goethe’s phenomenological observation.
Our contemporary spin-off is that there has been a multi-billion dollar industry
of colour photography, colour monitors, flat screens, and projectors based on the
fact that we have three receptors for colour perception. Finally, we might wonder
if we can ‘read’ back the human threefold using the self-similarity principle (as
Husemann phenomenologically showed in his gut study, see Chapter 3)?
This way of Goethean science requires a certain degree of open-mindedness
and courage. Actually, following this path, we want to understand something
without a preconceived paradigm or a specific hypothesis, certainly not that of
‘the sum of the parts constitutes the whole: integration of pieces’. Goethe, on the
contrary ‘differentiated from the whole’ and looked from this whole the other way
round, trying to perceive the primal phenomenon and trying to figure out what
metamorphosis in development this phenomenon shows, see also Bortroft (1996);
Heusser (2016); Husemann (1994); Selg (2022); Zajonc (2008).

Scope & span of science, the extended 4-Quadrant model

Mainstream (empirical) science has retreated into a part of reality and declared it-
self unfit to judge spiritual observations and theories. This situation is shown
schematically in the new extended 4-Quadrant model presented below, as de-
veloped by Wolfert, see Figure 2.8. This Figure presents a broader and holistic
definition of science, compared to strictly empirical science from the previous sec-
tion. We call this the extended ’con-scientific’ 4-Quadrant model (compared to
the empirical 4-Quadrant model of section 2.2). The vertical axis of this Figure
addresses the polarity between the spiritual and the material, the polarity between
mind and matter, or between outer mechanistic-matter sense perception and the
inner spiritual-mind (psychological) experience. The horizontal axis shows the
opposition between one’s own subjective experience and the universal objective
reality. Between these two pairs of opposites, which span four quadrants, man
must hold his ground and must acquire reliable knowledge of the world within him
and around him, i.e. conduct science, see Figure 2.8. This Figure is ‘a best fit for
purpose’ classification/ordering within the context of this book and we will inter-
pret and elucidate it further using the following definitions (so it is a classification
amongst others). Note that these definitions are ultra-short definitions of concepts
that entire studies are about.
Empiricism places emphasis on observational evidence via sensory experience
as the source of knowledge. Empiricism is associated with a-posteriori knowledge,
which is obtained through observation and experience. The empiricists consider
that knowledge can only be gained through studying or observing the physical
world outside the mind, namely through sensory experiences.
Rationalism places emphasis on reason as a source of knowledge. Rationalism
is associated with a-priori knowledge, which can be independent of real-life ex-
88 CHAPTER 2. DESIGN IN THE CONTEXT OF SCIENCE & ENGINEERING

periences. More formally, rationalism is defined as a methodology or a theory

”in which the criterion of truth is not sensory but intellectual (intuitive from the
mind) and deductive”.The rationalists consider that knowledge is in-born and the
intellect, the inner world of the mind, can therefore directly grasp logical truths.
Rationalists argue that there are certain principles in logic, mathematics, ethics,
and metaphysics that are so universally true that denying them causes a contra-
diction.

Figure 2.8: Four types of con-science domains, the extended 4-Quadrant model developed by Wolfert
and broadening the empirical 4Q model from Section 2.2.

Constructivism opposes the philosophy of objectivism (the ’sum of’ rationalism

and empiricism), embracing the belief that a human can come to know the truth
about the (natural) world not mediated by scientific approximations with different
degrees of validity and accuracy. According to constructivists, the world is inde-
pendent of human minds but knowledge of the world is always a human and social
construction. The ’truth’ is thus a social construction and can therefore take dif-
ferent forms every time (just as a single design question can also lead to different
artifacts). According to constructivists, there is no single valid methodology in
science but rather a diversity of useful methods. Social constructivism contends
that categories of knowledge and reality are actively created by social relationships
and interactions. These interactions also alter the way in which scientific episteme
is organized.
Spiritualism is the metaphysical school of thought opposing physicalism and also
is the category of all spiritual consciousness, awareness, and/or inner experiences,
which can in part be objectified. The generation of knowledge here is therefore
subjective and of a spiritual (”ideological”) nature. Art is a good example of this,
2.4. CON-SCIENCE, THE EXTENDED 4-QUADRANT MODEL 89

both from the point of view of the artist and the beholder or spectator. It was for
example Steiner, who again connected with Plato, who saw reality as a ’re-union’
of spirit (inner experience) and matter (outer observation). It is a ’unifying’ theory
that can be used to explain a much larger part of the world than purely from the
material point of view. Even spiritual consciousness persists after death and can
be contacted by the living, as seen by some spiritualists; and the afterlife, or the
”spirit world”, is not a static place but a place in which spirits continue to evolve.
We add the following ’definition’ notes to the four quadrants from Figure 2.8:
(#1) Sociology and psychology have a place in multiple quadrants because it de-
pends on which research method/ viewpoint one uses to arrive at knowledge and
how the results are verifiable (objectively or subjectively). The position of ethics
and religion in quadrant Q1 may also shift in to quadrant Q2 depending on the
view followed (axiomatically deduced or mindfully obtained). It may even be the
case that ethics has no position within this diagram, since there is no separate
‘science of good action’ (see Steiner’s incitement from the previous section)?
(#2) The sub-fields in quadrants Q1 and Q2 (mind/spirit) together belong to the
branch of philosophy (sciences). Philosophy is the systematic study of general and
fundamental questions, such as those about existence, reason, knowledge, values,
mind, and language. Humanities are academic disciplines that study aspects of
human society and culture. Today, the humanities are more frequently defined as
any fields of study outside of natural sciences, social sciences, formal sciences (like
mathematics), and applied sciences. The humanities include the studies of parts
of philosophy such as language and all forms of arts, is interdisciplinary, and may
be considered both a humanity and a science.
(#3) Actually, mathematics and ethics could also both have a place in Q2. As
for applied mathematics, we argued in the previous section that this also has
a spiritual component as soon as you connect pure mathematics with the true
world (it requires the principle of reflection). It then requires a reflective dialogue
to connect the mathematics via your individual thoughts/experiences (meditative
aha-erlebnis); the difference between computer logics and applied mathematical
modeling or between AI and art. The same goes for ethics, which can also be partly
placed in Q2 (see Incitement 2.2, and the previous note (#1)). For metaphysics,
this could go the other way. This is now pictured in Q2 but could also be a
universal truth in Q1 according to some people (see e.g. Steiner’s phenomenology
of mind/spirit, in one of the following sub-sections).
(#4) Various approaches in pedagogy derive from constructivist theory. They
usually suggest that learning is accomplished best using a hands-on approach.
Learners learn by experimentation, not by being told what will happen, and are
left to make their own inferences, discoveries, and conclusions. So, learners do
not acquire knowledge and understanding by passively perceiving it within a dir-
ect process of knowledge transmission, rather they construct new understandings
90 CHAPTER 2. DESIGN IN THE CONTEXT OF SCIENCE & ENGINEERING

and knowledge through experience and social discourse (later we will get back in
Chapter 9 with respect to the constructivist ODL education method).
Now (after these definition notes), let us continue our scope & span of science
’pathway’. Contemporary mainstream (empirical) science has retreated in the
lower left quadrant Q3, where it deals with universal truths and a materialistic
view of reality. This restriction to this one quadrant is a choice made by science
itself. While the advantage of that choice is the stimulation of technical progress,
the disadvantage is that science has little or nothing more to say about the figure
as a whole and thus about the total greater reality in and around man. In itself,
of course, that need not be an objection. In itself, of course, this need not be
an objection, but it becomes a problem when that self-selected limitation to the
quadrant Q3 is overlooked an erroneous statements are made about the entire
figure about the real world and humankind. Moreover, we argue that the retreat of
science in the objective materialist quadrant Q3 is not problem-free either. While
the accompanying development of technology has brought much social progress, its
one-sided bias has been so great that nature and the physical environment (earth)
are now in danger of collapsing under it. Apart from this one-sided technological
approach, man’s orientation has also become one-sided so that he seeks his meaning
and satisfaction of needs solely in the material. This keeps failing to work because
man seems to be more than the material bottom of the figure. In short, we should
not define and/or limit science and its scope too narrowly.
What then might actually be the broader (philosophical) definition of science?
What actually is science and what actually makes it possible to say: this is science
and that might not be science. We argue in this section that science in particular
is determined by the approach and/or attitude you take as a scientist towards the
phenomena you want to investigate. The phenomena you are confronted with can
be in all areas: in dead or in living nature, in matter or mind. So in science, it
is all about demonstrating a certain objectivity and being able to look at these
phenomena in such a way that you don’t incorporate your own opinion and your
own wishes about the outcomes beforehand and that you therefore consider them
this way. In short, an objective scientific basic attitude means that you do not
put your own subjectivity into something beforehand. This requires a reverent
attitude and strict discipline. When we devise a scientific theory from this basic
attitude on the basis of observed phenomena, it has scientific value if we can
verify it. Observations need not be limited only to an outer sense perception but
also through an inner sense perception, experience, and/or introspective methods
(meditative/metaphysical/supernatural).
Intermediate note: There is currently a growing interest worldwide in medita-
tion and research into its effects. One of the first and leading researchers to engage
in this is the late Francisco Varela. At the end of the 1980s, he was one of the ini-
tiators of the Mind and Life Institute which brings together meditators and brain
2.4. CON-SCIENCE, THE EXTENDED 4-QUADRANT MODEL 91

researchers (see: mindandlife.org). Many more results are expected to follow.

Among others, Varela (2017) and Zajonc (2016) speak of a second renaissance:
this time reaching back not only to the western ancient Greek tradition, but also
to the still living eastern meditation tradition. Thus, despite the serious threats
to present man and earth today, there is hope for the future.
Since in all cases in all four quadrants the reliability of knowledge acquisition
(i.e., verification) is paramount, it must be demanded that also the subjective
knowledge experience of the right sided quadrants Q2/Q4, especially the upper
right side, is also verifiable. Even for many para-psychological events, for example,
this is practically quite possible. However, when it comes to one’s own verified
experiences, the usual confidence in human perception often (and perhaps wrongly)
takes on a strange connotation. Hence, we will take a closer look at the concept
of verifying. In the regular mainstream science (research), the chosen research
method and new knowledge results should be reproducible. For engineering design
(development) this is different, namely the newly constructed artefact should be
reproducible, but the experience and/or value is user specific and these are not
reproducible because they say something about the inner (unique) experience of
human beings. So, in short, we could say that the way of verification depends on
how you solve problems and what domain of science you are in.
Actually, this distinction in verifiability applies not only between engineering
and science, but also to different strands within empirical science. Namely, if we
dissolve salt in water, the salt dissolves in it and if we do it again, it happens
again independent of place and time. But if, for example, we give a cat a candy
and then spray it with the plant sprayer immediately when it wants to grab it,
we will see that if we do this experiment again that the cat will then naturally
anticipate this in one way or another. From this we can conclude that the repeat-
ability/reproducibility of an experiment that we can see so beautifully in dead
nature (resulting from its randomness) is already much less clearly achievable in
living nature. In fact, we should always look in this way at the domain of science
in which we are working: what are the laws there, how can we do an experiment
there at all? If we want to do research in the field of spiritual reality, we need to
take a much more radical step, because the idea of experimentation is something
we have developed in the physical sensory world in a certain way so that we can
say yes, we can take salt and water at random and throw them together and then
see that the same thing happens every time and in every place. You cannot bring
this randomness into the spiritual world of man. Namely, the spiritual world does
not allow itself to be controlled in that way by human randomness, because you
have to transform yourself into a kind of ‘bowl’ in which something can emerge
that wants to disclose itself in it (see also Odesys’ paradigms & views on world
and man in Section 1.6). This is actually quite similar to when you are dealing
with other people. When people conduct an experiment together, the intercon-
92 CHAPTER 2. DESIGN IN THE CONTEXT OF SCIENCE & ENGINEERING

nection, the specific setting, and the interspace will determine what interaction
and/or dialogue emerges in the now: presencing. Of course, it is possible to re-
peat human experiments to a small extent, but at the same time it is immoral
to manipulate other people for a scientific setting (and its verifiability). So with
people themselves, where that form of experiment is not really an option, you must
look for the appropriate verification method. Similarly, in regular psychological
and sociological research there is a search for how you can do a form of verifiable
research that at the same time produces something for us as human beings.
This problem (not being able to easily and directly verify) plays a significant
role in psychic-spiritual research all the more. Indeed, there we cannot do an
experiment in the traditional way and this requires a deep personal development
of a new way of thinking for acquiring knowledge. We argue here that in that case
the way to acquire knowledge and insight is through Goethean phenomenology,
which uses thinking to find only the objective lawful ordering of the real (primal)
phenomena given as observation. This way of thinking and acquiring knowledge
has been extensively investigated and described by Steiner in his works so that
those who engage in con-science, with the thoughts/concepts contained therein,
offer a form of thought training. They should be for the reader of his works a
psychic-spiritual means of self-education, a path of schooling in the proper sense, a
spiritual path of knowledge acquisition especially for the scientist and the engineer.
One might call this the phenomenology of the mind as already proposed by Hegel.
Steiner’s most important work (partly inspired by and building on Goethe, Hegel
and Aristotle) in this context for training spiritual thinking is the book Philosophy
of Freedom, see Steiner (1995). Note that on can find more contemporary literature
in this same area of phenomenology of mind, see e.g., Gallagher (2013); Varela
(2017); Velmans (2017).

Phenomenology of mind/spirit
The following is an attempt to capture the essence of the book Philosophy of
Freedom (originally written at the end of the 19th century: here see Steiner, 1995)
as a first incentive to every scientist and engineer who deals with the integration
of the inner world of man (subject) and the outer world of things (object) around
him. It should be noted at the same time that this book is not actually a book in
the traditional sense, as it is much more of a practice book than a reading book.
Therefore it is not easy to summarize. Steiner himself says the following about
this, particularly addressed to university academics and students: ”... this book
is meant so that, page by page, we must directly activate our own thinking that,
in a certain sense, the book itself is only a kind of ‘score’ and we must read this
score with inner active thinking in order to continually proceed from our own self
from thought to thought...”
2.4. CON-SCIENCE, THE EXTENDED 4-QUADRANT MODEL 93

Let us start by considering the following question: What might phenomenology

(looking at primal phenomena, as described in the previous section) mean if one
employs and applies it to observe one’s own thinking, and can thinking observe
itself? In Philosophy of Freedom, he calls us to try this and, in doing so, to have
a unique experience: the experience of thinking activity becoming observation
content and observing becoming pure thinking. The observation content is now
not given but produced, and observing is not the finding of something existing
but the production of an observation content. The thinking activity that observes
itself is the observation content that produces itself. This self-knowing thinking is
the foundation that he declares absolute.

Incitement 2.5 Learn to think

(Dr. Mieke Mosmuller, philosopher/ physician / writer)

Some reflections to put our thinking into perspective.

”...You do think you think, and you do have a lot of

thoughts, but that is something different from actually
performing thinking as an inner activity. We think
that we think because we have thoughts and because
we can learn. You can absorb a lot and you can put
that into your memory and you can perhaps reproduce
that, so you think?”

“...The human being is not only constantly developing

in outer appearance, but also in his inner being. A
crisis, which literally means ‘judgement’ (separation),
offers a special opportunity to look at this developing
inner self. Questions about what is good or right, what
is truth, what is truthfulness, what is the value of
human beings and what is morality are questions that
are particularly among people. One of the overarching
questions is that of freedom and unfreedom.”

“...When you say: man is a higher animal, you actually

exclude what that very specifically human is - but that
also gives certain possibilities, because when you no
longer want to see that in human thought there is a,
let’s say primal being of freedom, and you abolish that,
you say: man is a higher animal, you have thereby at
the same time created the possibility of treating that
man as an animal as well.”

What might this mean for the open design system

within the con-science context? For more inspirations,
see: miekemosmuller.com.
94 CHAPTER 2. DESIGN IN THE CONTEXT OF SCIENCE & ENGINEERING

He explains that there are primarily two kinds of activities and therefore two
kinds of objects to be distinguished. What determines that distinction is whether
your own activity is or is not necessary for the object to occur. Let us consider
the following example. When you are handed a bunch of tulips and look at them,
you recognize the flowers and are happy. While you are actively ”present,” neither
the tulips nor the recognition nor the joy are brought about by your own activity.
Any object you encounter without needing your full activity to be there, he calls,
‘observation’, and the activity needed to have an observation, he calls ‘observing’.
Distinguished from this is an activity that not only gets to know its object, but
also makes it be. Normally, we can only ’make’ something by combining already
existing things. Steiner, however, discovers one exception. Suppose someone won-
ders what an ”organism” actually is, he can look at a tree or a cat. He can compare
those to a clock and reflect on the difference between a ”mechanism” and an ‘or-
ganism’. For example, he may come to the following insight: ’In the organs that
are the parts of a living whole, the same, lawfully evolving unity manifests itself all
the time.’ If you really think and understand this thought complex independently,
something special has happened. For understanding occurs only as soon as you
bring the thoughts to appearance in your consciousness and place them in their
interrelationship. You do this based on the content of the thoughts. However, the
connection of content is not observed like the tree or cat, but is produced. He
calls this the ’pure thinking’ that produces thoughts. With this, a fundamental
contradiction is found: observing contents (objects) that are already there, and
thinking as the activity that produces and connects understanding contents. This
contradiction of observing and thinking is crucial for the whole book. Moreover, it
allows you to unite empirics/science and introspection. The ‘observation content’
has become all-encompassing because of this contradiction. There is no longer any
principled difference between sensory, inner or mental phenomena. They all stand
as ’given’ observation contents against the concepts produced by your thinking.
What occurs in the observation without you producing it, can be connected to the
thought-content (concepts and ideas) that you produce yourself. This is how know-
ledge (and insight) arises. This way of producing knowledge is in some way similar
to and builds on the dialectical thinking developed by Hegel (2018, 2021), result-
ing in pure knowledge (epistime) generation (compare Hegel’s ‘highest’ dialect-
ical threefold categories: being/’sein’-essence/’wesen’-concept/’begriff’ or thesis-
antithesis- synthesis). Note: in Chapter 3, we will see this principle reflected in
the concept of the inner dialogue within the U model (purpose and presencing).
This introspective method excludes any metaphysics in advance (or using
Hegel’s words, “logic coincides with metaphysics”). There are only two kinds
of content: observation content and thought content, both connected with and ex-
perienced by a human subject. The only thing the experiencing subject can add to
both kinds of contents is the knowing process: the unification of both. Beyond that,
2.4. CON-SCIENCE, THE EXTENDED 4-QUADRANT MODEL 95

nothing can exist. The question then is whether this form of thinking is a purely
subjective or a super-sensible (universal) activity, and whether the knowing pro-
cess, i.e. the unification of observation/perception and thoughts/understanding,
brings us to reality? First, the common philosophical criticism of this approach is
that it enters an infinite regression. Does Steiner’s approach escape this infinite
series? Can this instrument play itself? According to Steiner, yes, because in
thinking the content and the activity coincide. The thought contents ( concepts
and ideas) are produced and this produced content, when thinking observes itself,
is produced by itself (i.e., thinking observed, or using Hegel’s words “the mind
comes to consciousness of itself”).
Another criticism comes from the natural sciences. Even if it were true that
thought has access to itself, it is in reality a brain activity that needs to be ex-
amined by neurophysiology. After all, our consciousness relying on brain processes
is the general ‘consensus’, and introspective self-reflection is in fact an illusion.
This materialistic view of consciousness has become commonplace in our time. At
the time of Steiner, brain research was already in full swing. Based on empir-
ical research, people were already linking psychological functions to parts of the
brain. Broca’s speech centre, for example, had already been discovered. Since
then, people have penetrated further and further into the workings of the brain.
For many, it is an obvious assumption that processes of consciousness are noth-
ing more than neuronal brain processes. However, Steiner rejects this explanation
in principle because it is not based on empiricism. Our thinking is primarily a
phenomenon of consciousness, which we learn introspectively. This experienced
thinking should be the starting point, not the brain that cognitive science invest-
igates. He was one of the first to analyse the unsolvable problem of any cognitive
science with razor-sharp clarity. In The Philosophy of Freedom, he demonstrates
the unbridgeable gap between brain research and introspection. Those who exam-
ine brains find no consciousness, no feelings, no thoughts. Those who contemplate
and observe their own thinking and/or their feelings do not find brains. All at-
tempts at explanation (such as the analogy with hardware and software) notwith-
standing, there is no escaping this problem. Doubt also occasionally surfaces in
modern science itself. The cognitive scientists Chalmers (2022), Gallagher (2013)
or Velmans (2017) amongst others, for example, argue that the ’hard problem’ of
how consciousness can arise from physical processes is fundamentally unsolvable
(cannot be reached at all, which legitimizes the inner phenomenological approach,
as described in the Philosophy of Freedom, actually even more).
Last but not least, fearful adherence to old paradigms has too often in history
stood in the way of the renewal of thought. The root of the problem seems to lie in
not allowing a speculative hypothesis. Its mere formulation is seen as a threat to
its own paradigm-based ‘authority’. A hypothesis nowadays seems to be able to be
stated only if the evidence is directly provided with it. Facts and observations are
96 CHAPTER 2. DESIGN IN THE CONTEXT OF SCIENCE & ENGINEERING

only accepted as such if they can be directly explained. We particularly recommend

this work by Steiner to scientists for today who are open to con-science as a whole,
and who do not want and dare to see science only as empirics. This can be seen
as a begin of an end, a period of ‘Idealismus-Romantik’ (Goethe, Hegel, Fichte)
or as an end of the beginning, a period of anthroposophy (Steiner). We end this
sction with the following notes:
(#1): Philosophy of Freedom (Steiner) - We become free by realising that we are
fully determined (apparent paradox). Or stated differently, according to a threefold
dialectical categories, the unity of fully determined complete randomness could be
freedom. Human beings are conditioned, goal-oriented, and their behaviour pushes
them in a certain direction. A person’s sense of ’self’, with the associated scheming
and possessing up to and including self-centeredness and greediness, constitutes
a powerful way of survival. People suffer when they cannot get what they desire
(or even when they have to give up concepts and ideas they already knew for new
truths). Much human suffering is caused by this. Indeed, that desire intended for
good survival can even turn against man. Man lives between desire (aspiration),
which is ’determined’ by the past, and freedom which opens designs towards the
future (purpose). This requires faith in yourself and the world around you (inner
and outer world), so that you can deal with the unknown (randomness).
(#2): Hegel’s scientific system (as a metamorphosis from Aristotle) - Hegel strove
to develop one overall concept, in which he wanted to unite science, aesthetics,
religion, and philosophy. He did not see reality as static, but as the outcome of
a continuous ongoing process, in which new contradictions are lifted each time.
The key word here is ‘to lift’ as well as to abolish and preserve (in Dutch ’ophef-
fen’, in German ‘aufheben’). During the so-called dialectical (thinking) process,
something (e.g. a moment) is first stated, then denied, to finally arrive at a higher
truth. Earlier, Fichte used the similar concepts of thesis, antithesis, and synthesis
for this purpose. This dialectical system, in which the so-called ’spirit’ is the
nature of all things and arrives at a new synthesis through the confrontation of
thesis and antithesis, should eventually lead to the ’Absolute Idea’ in which all
the individual elements of spirit merge and transcend themselves. There is no
real truth, but there is a truth that grows deeper and more mature. Yet in his
famous book The phenomenology of Spirit, Hegel saw his own philosophy as the
synthesis of the work of all his predecessors. Hegel thereby elaborates in part
on the conception of logic by Aristotle among others. According to Hegel, the
dialectical process also applies to individuals: first there is only the consciousness
of external truth, then a self-consciousness arises, which gradually reconciles with
consciousness. Hegel’s system comprises three major parts that are in dialectical
relationship to each other: the philosophy of logic, the philosophy of nature, and
the philosophy of mind: i.e, thesis, antithesis, and synthesis, respectively. Accord-
2.4. CON-SCIENCE, THE EXTENDED 4-QUADRANT MODEL 97

ing to Hegel’s philosophy, the development of all that exists was the development
of ’the Spirit’ itself. ’Everything that is a step in the development of the absolute
‘Idea” and ’Reason can do nothing without reality; and reality nothing without
reason’. The use of this dialectical thinking will be further utilized in Chapter 3
from a general people and management perspective and later in Chapter 4 from a
design perspective.

Figure 2.9: A viewpoint on a grain of wheat.

(#3): Steiner’s Q&As - Steiner gave many lectures also at Technical Universities
(e.g., in Delft or Stuttgart etc.). Students and professors had the opportunity to
dialogue with him. Notes were also made of these. Very worthwhile to investigate
these further see Steiner & Mulder (2022). Here we do not want to withhold at
least one of his answers from the reader:
”... imagine: the grain of wheat (see Figure 2.9) or the ear of wheat grows from the
roots and the culm. Then the plant-forming force manifests itself which from the
seed can produce a new plant which also seeds z and so on. We see that what works
as a formative force In the plant according to an inner law produces one form after
another, or as Goethe puts it, goes from metamorphosis to metamorphosis. Thus,
we try to follow In humanities rethinking that manifests In man as a formative
force. And we then come to the conclusion this thinking that In man is a formgiving
force also has a side effect and That is actually our normal core process. But If I
want to characterise the nature of thinking by virtue of that side effect I am doing
exactly the same as when I say Why should I concern myself with what shoots up In
the plant as a formgiving force through the root the culm to the nature. That does
not interest me. In fact, I take a nutritional approach and examine what appears
In the nature as nutrients. Of course, that is also a legitimate approach to the grain
of wheat. We can choose that view too. But If I do that, I am renouncing what
actually migrates through the plant as a continuous stream of development. So it
is with the core process. What is usually thought by practitioners of the theory
of knowledge by philosophers and all those who want to provide a foundation for
natural science with their reflections that are in fact processes that occur when
the thinking that actually wants to shape ourselves manifests outwardly in their
side-effects. That is the same as when we see what grows up in their wheat plant
alone as the basis for feeding another being. But it is not right to examine that
98 CHAPTER 2. DESIGN IN THE CONTEXT OF SCIENCE & ENGINEERING

wheat Only from that point of view. That has nothing to do with the essence of
the grain of wheat. In doing so, we are bringing in another point of view. . . ”

2.5. Open-ended Odesys research questions

With the following research questions (RQs), as developed by Wolfert , we en-
courage the reader to engage in a process of self-schooling and, in particular, to
investigate where Odesys stands within the context of the previous science and en-
gineering context, as explained within the different 4-Quadrant models questions.
One could therefore see this as an open-ended self-learning process.
(RQ #1) Is it a true statement when the smell of curry, an egg, and mayon-
naise relates to the flat of your grandmother? And if yes, how does this relate to
verification and reproducibility ? And if no, why is this statement not true or is
this a subjective? Is objectivity always something of retrospective verification, or
does objectivity mean that you don’t put biased subjectivity into it beforehand?
And don’t you automatically do that by starting from some paradigm? What is
actually the Odesys paradigm and is this ‘objective’ ? And what might be the
objective and subjective components of the Odesys methodology?
(RQ #2) What is the difference between belief and insight? An what is it for
you (belief or insight) when another scientist tells you that the Majorana particle
does exist? And what is it for you (belief or insight) when another scientist tells
you that the proper aggregation of preferences is a goal-seeking algorithm rather
than arithmetic calculation, see Barzilai (2022), or tells you that you should use the
D-decomposition method for complex root analysis to determine stability zones,
see Neimark (1978) or Wolfert (1998, 1999)? Having heard from scientists that
something works in a certain way, and ‘believing’ that so far, does not really make
much sense to talk about beliefs and science that way: would it not be much more
important to explore how do we relate ourselves to the source of that beliefs/
insights? And what does this mean to consider designing in the Odesys way?
(RQ #3) Is thinking a mental and invisible process? And if so, aren’t the
devised laws of nature actually in themselves also spiritual laws? Are these order
laws or are they prompted by something physical (e.g. think of gravity)? And do
these only become physical laws of nature when they become operative and visible
in the world? And what does this mean for the position of design ‘laws’ as part of
mind and matter sciences?
(RQ #4) When one says that a physicist is a good scientist one assumes that
he/she has been educated long enough and thus has acquired knowledge and de-
veloped skills to deal with physical matter What does this mean for a good scientist
of the mind and their particular education-path? And what would this mean for
(integrative) Odesys education?
(RQ #5) Suppose you are sitting on a bench with a friend and see a person
2.5. OPEN-ENDED ODESYS RESEARCH QUESTIONS 99

passing by. You observe this person X and identify all kinds of physical charac-
teristics (since you do not have no other characteristics or experience with this
person. You get no further. Next, a professor familiar to both of you passes by,
who taught you both. Now you (and your friend) identify both physical, but also
non-physical experience characteristics. You use your outer and inner senses and
experiences. Next, another friend of yours comes along and the three of you re-
peat these experiments/observations by letting person X and the professor pass by
again. What can you establish about the replicability (verification) of your exper-
iments and the ’truth’ of your results (verification)? How does this then translate
to verifying within an Odesys context?
(RQ #6) Could we admit and/or work with speculative hypotheses/paradigms/
axioma’s? And if no, what would this mean for the hypotheses as part of Millen-
nium Prize Problem, which are seven well-known complex mathematical problems
selected by the Clay Mathematics Institute in 2000 (e.g. the Riemann hypothesis
or the Poincare conjecture)? Even within the empirical sciences, we work for a
long time with predetermined hypotheses that are proven afterwards and thus by
definition. We also know that these are later overturned by a new theory (think
of gravitation force, for example). Could we also establish a new theory without a
presuppositional hypothesis? Within Odesys, do we establish hypotheses or goals
at the beginning of the design process, or is there just an idealized design to which
the designer is deeply committed?
(RQ #7) Can someone get a PhD and become a doctor without fully under-
standing one of the basic algorithms that underpins his theory? Or is belief in this
algorithm based on trust in the person who developed it fine and sufficient?
(RQ #8) We now know that a living worm cannot arise from nothing. Or in
other words, one could say life arises from life. What could this mean for the life
of a human being?
(RQ #9) Is the materialistic worldview a result of science or is today’s main-
stream science founded on a materialistic human and worldview?
(RQ #10) A dice behaves statistically? A pencil you can sharpen? Could you
call human’s behaviour statistically and to what extent can you influence or model
his behaviour (randomly)?
(RQ #11) Can you observe only outwardly or also inner beholding? And if
inner beholding involves a form of phenomenological thinking what is then the
difference between introspection and an inner experience?

The last overarching Odesys RQ is: what is the position of design in this present
discussion and where is it located in the quadrant figure? What is verification in
the design context. Is the concept of design sciences a paradox design philosophy.
Would introspection be not necessary and is inner experiences or are the inner
senses sufficient for Odesys?
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Chapter 3

Managing the service provider organiza-

tion

This Chapter decomposes into two main parts with corresponding purposes: 1)
describing organisation ordering principles of a service provider, that is a living
and dynamically enabling an ongoing quality of service through their engineering
assets and other organizational subsystems; 2) a prelude to the basic principles
for design and design making within an engineering asset management (EAM)
context.
For the first part, the purpose is threefold: (1) to provide a socio-eco perspective
on EAM within the context of a service provider, (2) to provide models through
which the social identity of such service organisations can be determined, analysed,
and/or improved; (3) to introduce a state-of-the-art U-model based management
system, through which fit-for-purpose open loops management and (re)design can
be completed (making well known management models obsolete).
For the second part, the purpose is also threefold: (1) to provide a pre-
lude/preview to the design and decision systems for the engineering assets within
an embedding EAM context (these systems will eventually be developed from
Chapter 4 onwards), (2) to provide the basic underlying social theory for collect-
ive well-being design/decision making (3) to introduce the Odesys’ U-model from
a socio-technical best fit for common purpose perspective. To this end, we will first
establish a vision based on different human and worldview paradigms. This vision
builds upon the principles of (1) human experience and a study of man, (2) social
threefolding, and (3) the theory-U and decision science. We argue that the proper
study of humankind within its living societal and organisational context is the
science of design and management. For us, the works of Brüll (2019; Endenburg
(1998); Glasl (1998, 2016); Kahneman (2013); Lievegoed (1991); Senge (2006);
Scharmer (2016); Simon (2019); Steiner (1995, 1996), are the key starting points
here (otherwise, other relevant literature will be widely cited where necessary).

101
102 CHAPTER 3. MANAGING THE SERVICE PROVIDER ORGANIZATION

We make the following notions:

(#1) The reader who is less interested in the socio-eco organisation and asset
management concepts offered here and/or in the fundamental basic principles un-
derlying human design and decision science, including the basic background of the
U-model, can continue with Chapters 4 and 5 respectively, as far as the Odesys
design approach and social identity are concerned;
(#2) This Chapter proposes a new system-oriented service provider organisation
model, including a human-centred EAM approach. This model is a quasi-static
organisation model at its core. For the reader who is also interested in dynamic
organisational development in this context, reference is made to Glasl & Lievegoed
(2016) and/or to Van Gunsteren & Vlas (2022) from a different perspective;
(#3) The socio-eco purpose threefold organization model, the Odesys’ U-models,
corporate social identity and group’s well-being design synthesis are all new models
and concepts as developed by Wolfert.
From Chapter 1, we will apply the concepts, viewpoints, and/or paradigms to
first structure an EAM service provider organisation and then to consider its organ-
isational development via open loop management (i.e., the learning & development
organisation). For the first organisational part, the principle of reflection with the
tripartite physical body is important. For the management and development part,
we will use the insights from the M-threefold of motive-momentum-management,
which forms the underlying human mechanism, to arrive at ’actions of response’
(see Section 1.6). The following sections will be increasingly summative in nature,
as they continuously build on what has been explained within Chapters 1 and 2.

3.1. Socio-eco purpose, the quality of service concept

This section summarizes the key aspects of a service provider organization, which
is an entity that enables a certain ongoing quality of service (QoS) through their
engineering assets and other subsystems. It will look at this organization from
a holistic systems-thinking-based perspective with the aim of modeling, diagnos-
ing (qualitatively and quantitatively), and improving and/or further developing
it. The content of this section is a reflection of Wolfert’s years of real-life service
provider practitioner experience, which he has integrated with scientific research
and insights from different backgrounds (social, engineering, organizational, bio-
logical etc.). Wolfert has also lectured and elaborated on this topic with MSc
students at TU Delft over the last decade. The direct (practical) experience of
Wolfert within this field is what makes it a unique state-of-the-art socio-technical
synthesis, which builds upon the organizational dynamics works of the aforemen-
tioned authors. This section is founded on the fundamental humanistic threefold
principles described in Chapter 1 while concurrently integrating a systems thinking
design and engineering approach. It constitutes a qualitative prelude that we can
3.1. SOCIO-ECO PURPOSE, THE QUALITY OF SERVICE CONCEPT 103

use and model quantitatively in the subsequent chapters. The aim of this section
is to provide the reader with insight into the overarching principles and concepts,
and invite them to apply them independently to improve the social identity of any
organization. Finally, this section has a summative character that can also be seen
as a portal to relevant reference material. An overarching starting point is that
an organization is a living organism which is not simply a sum of the individual
subsystems, but a synergetic organization emerging from a certain social quality
fit for purpose.
Before moving on to the innovative service provider organizational model, we
will first outline the context in which we will apply it. We will then briefly discuss
what the current state-of-the-art literature covers about Engineering Asset Man-
agement (EAM) within the context of a service provider. The context in which we
will consider our service provider organization from now on is that of infrastructure
and real estate assets within the built environment, see Figure 3.1.

Figure 3.1: Different service providers (infrastructure and real estate in the build environment) and
their engineering assets .

Typical physical, technical, or engineering assets in this context are thus rails,
roads, surge barriers, dams, water reservoirs, dams, stations, and/or government
buildings etc. These assets provide and ensure QoS, together with other relev-
ant subsystems of the service organisation, of the functional performance users
or customers experience when using this type of infrastructure or buildings. To
guarantee this QoS, a service provider provides both design, build, maintenance,
and operations activities (rather than just ‘painting and holding the handrail’).
This guarantee of QoS is what a systems thinking EAM approach requires. For
104 CHAPTER 3. MANAGING THE SERVICE PROVIDER ORGANIZATION

example, within a telecom service provider, we can think of a QoS that is the abil-
ity to provide different priorities to different applications, or to guarantee a certain
level of performance to a data flow (e.g., max and min bitrate speed and/or up
and downtime etc.). To do so, a telecom service provider makes use of and oper-
ates engineering assets such as antennas, routers, switching centres, facilities etc.
However, a telecom or internet service is not only provided by these assets, but
also by other realms of the organisation such as customer services, marketing and
sales, financial and billing support entities etc. Only through dynamic interplay
will a certain quality of service (QoS) be possible.

Incitement 3.1 Design and systems thinking

Consider the Dutch A15MaVa transport road infrastructure systems which comprises of the
Botlek and Thomassen tunnels, the movable Botlek bridge, and several other superstructures
and engineering assets. This road infrastructure is crossing the river which is an important
waterway as part of the Rotterdam harbor network. The MaVa Service provider, and in
particular the maintenance services contractor, have recently invested in detailed monitoring
per asset and can thus closely monitor degradation behavior (see for example Figure 5.3).
Because they can now monitor all sub-systems well, this contractor is sure to be able to
make an optimal service intervention plan. But is that really the case?

Are other drivers, such as given tunnel possessions, traffic hindrance, availability, and ac-
cessibility, much more important as they directly impact the Quality of Service (QoS) for
different stakeholders and/or users? Has this contractor integrated these QoS preferences
within the service operations plan together with the global engineering asset performance?
What would happen if the contractor dared to look beyond the MaVa road system bound-
aries and, together with the key stakeholders of the waterway system, arrived at a best fit
for common purpose service intervention plan in which all interests of different stakeholders
are optimised for effective and efficient decision making at multi-system QoS levels?

The overarching questions remain: how can we design an optimal service operations plan that
fits for common purpose, and what is the retained relevance of detailed asset degradation
curves per asset within such an multi-systems thinking approach? In other words, what is
the most effective approach; purely zooming in on system elements or zooming out on the
system as a whole?
3.1. SOCIO-ECO PURPOSE, THE QUALITY OF SERVICE CONCEPT 105

Much has been written about EAM in literature, see e.g. Balzer(2016); Dhillon
(2006); Hastings (2015); Haynes (2017); Uddin (2013); Slack (2010). Although all
these authors provide tools, processes, and other facilitating concepts to support
EAM in certain sub aspects, none of them act from a holistic point of view. Ex-
isting literature therefore fails to enable the integration of socio-technical systems,
lacking to provide real solutions for the real context of the service provider. In
short, these books offer some basic theoretical concepts to analyze parts of asset
management processes, but cannot solve future problems despite their claim to
work with meta-models, which is a misleading term because they do not follow a
meta or integration approach at all. Instead, they follow a one-sided technology
approach that mostly ignores the real socio-technical behavior of a service provi-
sioning system and its engineering assets. Since the principle of human reflection
is very often missing, many of the proposed models are instrumental in nature
and lose connection with the social context, identity, and purpose of the service
provider. We will not elaborate on these instrumental concepts here, but assume
the reader is familiar with the relevant EAM concepts or will become acquainted
with them through the references mentioned above. In conclusion, we can say
that something is needed to diagnose a socio-technical organization and make it
”healthy” in case of so-called ”disease”.
To make this well-needed translation into a new and pure socio-technical or-
ganizational systems integration, we start from the important tripartite/threefold
principles and paradigms from the previous section. We have seen that human
beings (and thus a living organization) consist of three important subsystems: (1)
the empiric subsystem which reflects the ‘eyes and ears’ of the organization, (2)
the metabolic subsystem which reflects the ‘organs’ (’engines’) of the organization,
and (3) the rhythmic system which reflects the (social) heart of the organization.
Let us take a closer look at these three subsystems. Firstly, the empiric sys-
tem perceives from inside to outside, looking into the world of customers, users,
and other stakeholders, and taking care of the ‘external housekeeping’ from a fair
service trading fraternity principle. We therefore call this system the economic
subsystem, with its purpose being ‘association to satisfy’. Secondly, the meta-
bolic system operates from within, providing monitors and cares for the life cycle
of the engineering assets (’capital’), and taking care of ’the internal household’
from the principle that these continue to function and/or be sustained in a free
and logically sound manner. We therefore call this system the ecologic subsystem,
with its purpose being ‘freedom to manifest’. Thirdly, the rhythmic subsystem
accommodates the internal dynamic balance and supports the other two subsys-
tems continuously from the principle of equality. We therefore call this system
the isonomic subsystem with its purpose being ‘equality to accommodate’. The
other qualitative characteristics of these three enabling subsystems are shown in
Figure 3.2. Last but not least, the symbiosis/synergy of these three subsystems
106 CHAPTER 3. MANAGING THE SERVICE PROVIDER ORGANIZATION

will result in the so-called socio-eco quality of service, or socio-eco fit for purpose
service (in short socio-eco purpose), and expresses the social identity of a service
provider or corporate social identity (CSI), as developed by Wolfert (including the
newly conceived terminology).

Figure 3.2: Qualitative characteristics of the different organisational realms.

Note that socio-eco is a concept amalgamation by putting together a number of let-

ters from the concepts economic, ecologic, isonomic, and social to form socio-eco.
It thus expresses the symbiosis of these four concepts. More specifically, these
three autonomous areas, economy, isonomy, and ecology, which each live and work
together from their individuality. This synergy leads (via the primal social phe-
nomenon) to a social force as a resultant. In the case of a service provider organ-
ization, the social force is the resulting quality of service (QoS) that is delivered.
This is the real service quality that a customer of this organisation experiences,
see also Van Gunsteren (2013). Ultimately, this resultant is also an expression of
the social identity of the organisation, i.e., the inherent value or identification of
the organization’s way of working. In other words, it expresses the purpose of the
organisation and is thus a measure of well-being. We call this concept from now on
the socio-eco purpose: i.e., corporate social identity (CSI), which characterises the
well-being of an organisation. In Chapter 5, we will look at how we can not only
express this qualitatively, but also quantitatively using preference function model-
ing/measurement and MCDA techniques (see Section 5.1, Example 4). This CSI
value of an organisation is a pure indicator that amongst at least two other organ-
izations will have to be determined, using the integrative socio-eco characteristics
3.1. SOCIO-ECO PURPOSE, THE QUALITY OF SERVICE CONCEPT 107

as proposed here. This identity is an expression of the emergent quality of service

which a service provider is able to deliver. This is the fundamental difference with
the traditional corporate social responsibility (CSR), which is determined only
from the own single view of organisation. This is fundamentally wrong and leaves
a large gap between their espoused theory and their theory in action. In short,
determining the CSI quantitatively requires a relative solver and preference-based
modeling approach between at least three different organizations. In this way,
we can make a real relative comparison and thus determine a CSI (note: within
MCDA, ’one is none’ applies, see Chapter 5). We have summarized the above in
a completely new representation of a socio-eco systems service provider organiza-
tional system model, as depicted below in Figure 3.3 and Figure 3.4. We make a
few extra notions:
(#1) Within the terminology used, eco stands for ’household’ (derived from
the Greek oikos), iso(s) for equivalent, nomos for laws and rules, socio(s) for com-
panion/company, and logos for word, idea, logic (isos, socios, nomos, and logos
can be traced directly from Greek).
(#2) These three subsystems can also be referred to as the commercial, the
technological, and the staff support parts of the organization. In other words,
these are the commercial & customer service department, the technological &
operations department, and/or the corporate support staff departments of the
service provider.

Figure 3.3: Part 1: Socio-eco service provider organizational model and its threefolding social identity
(well-being), as developed by Wolfert (including the related models derived from this below).
108 CHAPTER 3. MANAGING THE SERVICE PROVIDER ORGANIZATION

Figure 3.4: Part 2: Socio-eco service provider organizational model and its threefolding social identity
(well-being), as developed by Wolfert (including the related models derived from this below).

(#3) People and the possible natural resources, money, and data occupy a
specific role within the organization. You could characterize them as the blood
(people/resources, sort of continuous circulation) and/or the water and air (money/
data, sort of in-out flow) of the organization because they spread throughout the
organization, so to speak, via the rhythmic heart-lung system. They then also
take on the characteristics of the part where they are located, and can thus take
on multiple qualities. For example, we have already seen that money can take
on a gift- (ecological), a loan- (isonomical), and a buying form (economic), inde-
pendent of the organization, thus showing itself in the three different parts of the
organization.
(#4) The isonomic system is actually the true and enabling system from within.
It is the continuous enabling system of the other two subsystems which together
deliver the resulting QoS. You could say that the social fit for purpose service
emerges from the three synergetic enablers. The remarkable thing is that when
there is no longer a need to provide service, the isonomic heart stops ’beating’
just as the economic system closes its ’shutters’. The ecological system stops only
when the assets are ’exhausted’ or no longer receive a ‘supply of blood’.... Note:
this qualitative ’view of the organization’ is mainly to be used as a ’mirror’ from
which one can look to diagnose cause-and-effect relationships in the case of an
’energy-less’ non-functioning organization (don’t take the comparison too literally,
but as a supportive appraisal point of view).
3.1. SOCIO-ECO PURPOSE, THE QUALITY OF SERVICE CONCEPT 109

(#5) All the aforementioned principles and the corresponding organizational

model which breaks down into the economic, isonomic, and ecological trinity can
not only be used for the service provider organization but is a generic organizational
system model which can also be used for other organizations outside this specific
EAM context.

Figure 3.5: Part 1: Zooming out, the service provider and its embedding social threefold dimensions.

Figure 3.6: Part 2: Zooming out, the service provider and its embedding social threefold dimensions.
110 CHAPTER 3. MANAGING THE SERVICE PROVIDER ORGANIZATION

Let us continue with the socio-eco service provider organizational model. For
this, we zoom out and can see that the service provider organization, like any other
organization, is also embedded within a ’social-societal’ context. We saw earlier
that, according to social threefolding, this context can be divided into three types
of embedding system dimensions: the social-economic, social-political, and social-
cultural dimensions (see Chapter 1). The result of zooming out further is shown
in Figure 3.5 and Figure 3.6. Note: we clearly see here the self-similarity principle
of the service provider organization within its embedding system context.
We are now making one final step in the development of the socio-eco purpose
organization model, for which we will zoom in again. We then actually observe
two further points. First, we can see via the similarity principle that the technolo-
gical engineering asset management (EAM) organization, the ‘supply’ part of the
service provider, can be broken down into ‘internal’ subsystems as shown in the
picture below. We recognize again the familiar tripartite division: (1) a ‘demand’
interface to the commercial department (via service level agreements), (2) an ‘en-
abling’ part in which so-called functional control takes place, interfaced with the
corporate business support organization, and (3) the ‘supply’ part, reflecting the
EAM organization in which both project development plan (PDP) activities and
service operations plan (SOP) activities take place, see Figure 3.7.

Figure 3.7: Cyclical Service Operations Plan (SOP) and linear Project Development Plan (PDP),
which are both part of open EAM loops.

The PDP activities, including construction project management, together with

the SOP activities, including maintenance service management, form the so-called
SAMP (strategic asset management plan). Note the following hierarchy here:
EAM incorporates both project & construction management as well as operations
& maintenance management (in addition to preparative design management). All
of the SAMP activities directly contribute to safeguarding and/or expanding the
3.1. SOCIO-ECO PURPOSE, THE QUALITY OF SERVICE CONCEPT 111

quality of service of the service provider’s engineering assets. Secondly, by zooming

in closer, we can see that this EAM organization operates from an ecological control
and care viewpoint, sustaining and/or renewing the engineering assets over their
entire life-cycles. ’Control’ here implies the linear (one-off) process of project
development (PDP) by which we renew the assets or add new assets to the asset
base. ’Care’ here implies the cyclic process of service operations (SOP) supported
by effective and efficient maintenance management. These two processes of control
and care are recurring in itself and will be effected by both internal and external
(seen and unforeseen) changes.
To cope with these changes the EAM organisation has to (re)act in a resilient
open loops management approach, reflected by the open loops in Figure 3.8 and
Figure 3.9. Obviously, the other two parts of the organisation, the commercial
department and the corporate business support department, must also operate
according to an open loops management approach to jointly deliver the required
quality of service levels given these internal and external changes (typical disturb-
ances). In Section 3.4 we will elaborate on the concept of open loops management
and what this means for the developing organisation to deal with changes. We will
show that this requires a state of the art approach which goes beyond standard
management and organizational learning approaches (such as the PDCA and/or
MI/II single and double loop learning). Using the principles of theory-U, we will
introduce an innovative approach to open loops management within an organisa-
tion (and in the next Chapter to open designing of engineering assets).

Figure 3.8: Part 1: Zooming in, the open loops socio-eco service provider organizational model and
its EAM organizational threefold.
112 CHAPTER 3. MANAGING THE SERVICE PROVIDER ORGANIZATION

Figure 3.9: Part 2: Zooming in, the open loops socio-eco service provider organizational model and
its EAM organizational threefold.

Before we move on to open loops management, we will first extend the socio-
eco characteristics of the threefold organization with different ”social” laws that
apply to each part (social laws just like natural laws). We do this partially to
better understand the social identity of the company, but primarily to see how we
can improve or support collective decision-making processes, which is an essential
part of open loops management.
Incitement 3.2 Social sciences and design

(Dr. Rudolf Steiner, philosopher/ humanities scientist)

A radical viewpoint on social sciences and pedagogy, which could be used for socially re-
sponsible designing and learning?

”... When human beings meet together seeking the spirit with unity of purpose then they
will also find their way to each other.”
“. . . A healthy social life is found only, when in the mirror of each soul the whole community
finds its reflection, and when in the whole community the virtue of each one is living.”
“. . . In a community of human beings working together, the well-being of the community
will be the greater, the less the individual claims for himself the proceeds of the work he
has himself done; i.e., the more of these proceeds he makes over to his fellow workers, and
the more his own requirements are satisfied, not out of his own work done, but out of work
done by the others.”
“. . . When man faces man the one attempts to put the other to sleep and the other
continuously wants to maintain his uprightness. But this is, to speak in the Goethean sense,
3.2. SOCIAL LAWS & PRINCIPLES, A BASIS FOR ODESYS 113

the archetypal phenomenon of social science. This sleeping-into we may call the social
principle, the social impulse of the new era: we have to live over into the other; we have to
dissolve with our soul into the other.”
“. . . Our task is to educate the human being in such a way that he or she can bring to
expression in the right way that which is living in the whole human being, and on the other
side that which puts him/her into the world in the right way.”
“. . . If humanity is to live in the future in a socially responsible way, humanity must educate
its children in a socially responsible way.”

What might this mean for Odesys (and later for ODL)? At least let it be food-for-thoughts
during the conspection of socio-technical design synthesis solutions and the ODL concept
implementations....

3.2. Social laws & principles, a basis for Odesys

In addition to the qualitative characteristics of the threefold socio-technical (ser-
vice provisioning) organisation as described above, specific laws and principles
apply to each of the three organisational areas. These so-called social laws are
(like physical laws) generalities determined after a long study of man’s interaction
with his environment. Here, these laws and associated principles are derived on the
one hand from Steiner’s social/sociological laws, see Brüll (2019); Large (2010);
Selg (2011); Steiner (2013), and on the other hand from some philosophical prin-
ciples of Aristotle: i.e. man is by nature a social being and can find his perfection
and bliss only in a community, and the ’golden mean’ is the key here. These laws
again (like the aforementioned qualitative aspects per realm) are meant to estab-
lish the social identity or socio-eco purpose of an organisation, and/or come up
with suggestions for improvement. For an overview of the laws and principles as
used here, see Table 3.10. We make a few extra notions:
(#1) Social main law / Economic area - The well-being of a group of people
working/designing together is greater the less the individual claims the outcome
of their achievements. That is, the more he relinquishes it to his co-workers and/or
co-designers, the more of his needs are satisfied not by his own performance but
by the performance of others: i.e., a forward-looking development in which a link
between altruism and well-being is perceived.
(#2) Sociological basic law / Ecologic Area - The individual well-being of people
as part of a social organization is greater when the individual becomes (more) free
114 CHAPTER 3. MANAGING THE SERVICE PROVIDER ORGANIZATION

from the interests of the institutions and is free to develop their future needs and
personal abilities. At the beginning of its cultural status, humanity strives for the
emergence of social institutions where the interest of the individual is sacrificed
temporarily for the interest of the institutions.

Figure 3.10: The different social laws/principles related to different organisational realms, as de-
veloped by Wolfert from Brüll (2019); Endenburg (1998); Steiner (2013).

(#3) Different laws & principles / Isonomic area - Within this area, the following
laws and principles play an important role:
1. Solidarity principle - solidarity is the awareness that although individuals
have different roles, interests, and values, the order and coherence of society
depends on their being able to trust each other to carry out those specific
roles. It involves individuals recognizing that defending or encouraging the
interests of others is ultimately in their own best interests.
2. Sociocratic principle - sociocracy is a system of governance that seeks to
create socially safe environments and productive organizations. It draws on
the use of the consent principle or the preferendum, rather than majority
voting, in discussion and decision-making by people who have a shared goals
or work processes. It has been based on equal human dignity without stat-
ing that all people are exactly equal or fulfil an equal function. These are
elaborated within the sociocratic circle-organization method, as developed
by Endenburg (1998). Within this, two concepts play an important role.
• Consent principle - decisions are made when there are no remaining
”paramount objections”, that is, when there is informed consent from
all participants. Objections must be reasoned and argued and based on
the ability of the objector to work productively toward the goals of the
organization.
• Preferendum – a preferendum is a form of a-priori public decision-
making in which the gathering of information, consultation, and ex-
3.2. SOCIAL LAWS & PRINCIPLES, A BASIS FOR ODESYS 115

change of arguments and/or potential solutions are central (as opposed

to an a-posteriori vote for or against, e.g. referendum).
3. Common-natural laws - these laws arises from the normal interaction of
people with one another according to their nature and customs, which main-
tain peace and equity between themselves. This Common Law is the human
manifestation of the universal Natural Law, and creates no hierarchy or dom-
inating force over people with their human rights: e.g.,
• Recognition of the inherent dignity and equal and inalienable rights of
all members of the human family is the foundation of freedom, justice,
and peace in the world (Aristotle: ”the worst form of inequality is to
try to make unequal things equal. Humans are not equal, but they are
worth equivalent’).
• All human beings are born free and equal in dignity and rights. They
are endowed with reason and conscience and should act towards one
another in a spirit of brotherhood.
Specifically for these common-natural laws, it could be said ’in extreme’
that they go back to the 10 commandments (i.e., ’10 logoi’) given to mankind
through Moses. These later became part of the common-natural laws and the
preamble to the universal declaration of inherent/inalienable human rights.
Note that to the best of the author’s knowledge, we remark that here, for the
first time, an integrative link is made between the social threefolding mechan-
isms, the corresponding qualitative characteristics, and the specific relevant social
laws & principles per realm (social main / sociocratic-solidarity-common-natural
/ sociological). These have been developed by Wolfert and are Odesys’ basis for
collective and/or participative design and decision making.
Later in this book we will see how we further give substance to the introduction
and support of social threefold laws and principles. Or otherwise stated, this set
of social laws and principles forms the basis for Odesys’ Preferendus. Actually, the
Preferendus was inspired by and is a reference to the preferendum concept, and is
a composition of the words preferences and preferendum (as a name conceived by
Wolfert). The Preferendus was developed to accommodate early and transparent
participation within an a-priori group design/decision making process, see Zhilyaev
Binnekamp and Wolfert (2022) and/or Van Heukelum, Binnekamp and Wolfert
(2023). Through the cyclical running of the so-called social cycle, the participants
can eventually give their informed consent without objection (after perhaps many
iterations) and with fully transparent insight into the model/design outcome for
the best-fit for common purpose solution.
Regarding the concept of common purpose, Van den Doel (1993) has already
shown through his theory of collectivist utility (well-fare economics, a form of util-
itarianism) that the group optimum is greater than when the individual makes less
claim to his own individual outcome instead of strive for his own interest. Here
116 CHAPTER 3. MANAGING THE SERVICE PROVIDER ORGANIZATION

we take this (economic) collectivistic utility based decision-making a step further,

because we do not only look at utility in the economic sense but consider the ag-
gregated common purpose that is both economically, isonomically, and ecologically
determined. This aggregated property defines the group’s well-being optimum,
where the fundamental laws & principles of all three realms (social/sociological
law and sociocratic/solidarity principles) are maximally leveraged. How to re-
trieve this group’s well-being optimum is explained further in Chapters 6 -8, in
which the Preferendus (Odesys’ software ’engine’) is introduced in this book as
the ultimate participatory decision instrument to arrive at an optimum on max-
imising common socio-eco interests and the related aggregated preferences. In
other words, the a-priori and equitable (but not necessarily equal) inclusion of
all people’s preferences in the decision-making process will lead to best synthesis
solutions rather than compromises. These synthesis solutions minimise everyone’s
individual dissatisfaction, however the group outcome turns out to be sub-optimal.
Note here the subtle and important difference between the concepts of well-being
and well-fare within this context.

3.3. Open loops management, an act of U-ncovering

In this section, we will show an entirely new approach by which you can achieve
a redesign of, or within, an organization: i.e., open loops management of change.
Most change and learning methods are based on the Kolb Learning Cycle (some-
times also called PDCA), which suggests a version of the following sequence: ob-
serve, reflect, plan, act. By grounding the learning process this way, the learning
cycles are based on learning from the experiences of the past. Argyris & Schön’s
distinction between single-loop (model MI) and double-loop learning (model MII)
refers (still) to learning from past experiences. Single-loop learning is sufficient
where error correction can proceed by changing organisational strategies and as-
sumptions within a constant framework of values and norms for performance. It is
concerned with how to achieve existing goals and objectives, keeping organisational
performance within the range specified by existing values and norms (it has a so-
called self-sealing nature, as opposed to self-opening, see Argyris & Schön (1996).
In some cases, however, the correction of error requires an inquiry through which
organisational values and norms themselves are modified, which is the essence
of double-loop learning (model MII). In summary, one can say that single-loop
learning is reflected in the levels of reacting and restructuring, while re-framing is
an example of double-loop learning, which includes a reflection of one’s deepest
assumptions and governing variables).
However, the theory-U with its U-model goes beyond double-loop learning (see
Scharmer). It accesses a different stream of time, the future that wants to emerge.
It finds its basis in the act of design and decision making as described in section
3.3. OPEN LOOPS MANAGEMENT, AN ACT OF U-NCOVERING 117

3.1 (see the 3x3=9-fold of human being diagram and the theory of instinctive
versus intuitive thinking). Theory-U is more than just a theory (see e.g., the
U-lab and the presencing institute at MIT). It is a process model for renewal
and transformation of people, organisations, and systems from a threefold view
of human experiences (mind/soul/body). The U-model was originally developed
by Glasl and his colleagues Lemson and Lievegoed from the Dutch Institute for
Organisational Development (NPI), as an open socio-technical process model to
come from an organizational diagnosis of the present state to designs for the future
(see Chapter 1). The U-model (literally) goes deeper than the double-loop learning
process and gives concrete form to the double loop re-framing part. This is done
by consciously uncovering the common or individual open will via a process of
dialogue with the blind spot (or your ’silent self’). This U process involves a
deep movement, as in the letter U, hence the name (note that the letter U is the
most forward vocalisation, especially if one uses the German pronunciation: ’oe’,
which we also see reflected in the eurythmic movement of the letter U signifying its
forward and future orientation, see Steiner(2019). We will see that the U-model
will be developed into the ODL-U for education, enabling open design learning
from the future, rather than learning from the past .
This section presupposes basic knowledge about the main principles of the
U-model and/or theory-U, as described in Sections 1.5 and 1.6. Here in this
section, only the innovative tool(s) will be presented as an extension of the U-
model for the context of design and management of engineering assets. Moreover,
we use the work of Dijksterhuis (2011), Kahneman (2013) and Zajonc (2008) as
additional inspiration. This U-innovation emerged from the work of Wolfert and
can be seen as a unique complement to the existing theory-U. We will therefore
start this section with an interlude describing these basic extensions which can
later be used for management, learning, and design. After this interlude, this
section will continue with the open loops management within an organisation as
a first elucidative application of the renewed U-model by Wolfert. The other
applications and associated redeveloped U-models can be found in Chapters 4, 6
and 9: i.e, U-ncovering the best fit for common purpose design or U-nlocking open
design learning response respectively. A final introductory note: this section has
a summative character covering the models and diagrams for the purpose of this
book and, moreover, can also be seen as a portal to relevant reference material.

Interlude ’continuing U-model development’

The U-model was developed by Glasl (1998) and some colleagues (Lemson, Lieve-
goed) at the Dutch NPI institute for organizational development as an open socio-
technical process to come from an organizational diagnosis of the present state to
(re)designs or developments for the future. They described a process in a U pro-
118 CHAPTER 3. MANAGING THE SERVICE PROVIDER ORGANIZATION

cedure/formation consisting of three levels: (1) technical/ instrumental subsystem,

(2) social subsystem, and (3) cultural subsystem). Note: because design/plan/
management are actions from the mind to the matter, the actor of these activities
observes the engineering assets which are outside him. This explains that, accord-
ing to the inversion principle, in this case the engineering assets are the technical/
empiric system as opposed to the metabolic system as part of the technological
service provider organisation (viewed as an independent living organism). In gen-
eral, the U procedure transforms observations into intuitions (i.e., freely produced
‘thought-content’, her defined as intuitive ideas) and judgments about the present
state and redesign decisions about the future. The three stages represent explicitly
recursive reappraisals at progressively advanced levels of reflective, creative, and
intuitive insights, thereby enabling more radically open systems intervention and
redesign. The stages are a metamorphosis from: a) phenomena - picture (a qualit-
ative metaphoric visual representation), b) idea - purpose (the idealized design or
formative principle), and c) creation, judgment - validation (is this design synthesis
fit for purpose?). The first three (new idea→ new picture→ new phenomena) then
are reflexively replaced by better alternatives (new idea new image new phenom-
ena) to form the final design. In other words, we see that the U procedure goes
in two directions uncovering the common will : (1) from the instrumental matter
(i.e., technical subsystem) via the organizational context (i.e., social subsystem)
to the inner source (i.e., purpose or cultural subsystem) (2) from the new idea
working in or towards the new instrumental matter. In other words, you therefore
have to go through the process ’twice’ in opposite directions to unite the design
impulse (internally or externally driven) with its common motive so that social
interests can coincide with technical achievability: the essence of the U. Note that
this important translation to design and decsion making was first made here, see
Section 1.6.
Complementary to that earlier work on the U-model or procedure, which as-
sumes a set of three subsystems in the organization that need to be analysed
in a specific sequence, Scharmer’s theory-U starts from an epistemological view
(i.e., imagination, inspiration, intuition) that is grounded in Varela’s approach to
neurophenomenology, see Varela (1991), as opposed to the more ontological ap-
proach of Glasl’s U-procedure (i.e., picture, purpose prototype). It focuses on the
process of becoming aware and applies to all levels of systems change. Theory-U
contributed to advancing organizational learning and systems thinking tools to-
wards an awareness-based view of systems change that blends systems thinking
with systems sensing. On the left-hand side of the U the process is going through
the three main ”gestures” of becoming aware that Varela spelled out in his work
(suspension, redirection, letting-go). On the right-hand side of the U this process
extends towards actualizing the future that is wanting to emerge (letting come,
enacting, embodying). Scharmer expresses the theory-U as a process or journey,
3.3. OPEN LOOPS MANAGEMENT, AN ACT OF U-NCOVERING 119

which is also described as ’presencing’, as indicated in the diagram below (where

presencing integrates the words sensing + presence). Presencing (later seen as
dialoguing in the now) is connecting to the deepest source, from which the field of
the future begins to arise—viewing from source. Presencing is part of a U-journey
with three main movements: We move down one side of the U-’connecting us to
the world that is outside of our institutional bubble’, to the presencing bottom of
the U-’connecting us to the world that emerges from within’, and up the other side
of the U-’bringing forth the new into the world’. The sources of theory-U include
interviews with many innovators and thought leaders on organizational manage-
ment and change. Particularly the work of Brian Arthur, Francisco Varela, Peter
Senge, Ed Schein, Joseph Jaworski, Friedrich Glasl, Martin Buber, Rudolf Steiner
and Johan W. Goethe have been crucial for Scharmer, see Scharmer (2016).
For Odesys’ purpose, we will from now on adapt, extend, and convert these
basic U-diagrams (again Scharmer and Glasl were our starting point) in at least
three major directions: i.e., (1) an extension and particularisation to enable open
design systems and participatory decision making, including open source modeling
support, (2) the introduction of the concept of the living dialogue to give ‘hands
and feet’ to the purpose subsystem, and (3) a specific extension to an application
for the innovative ODL education concept. Note that giving ‘hands and feet’
(i.e., making it concrete and/or giving it substance) shows an interesting language
application which expresses that connecting the limbs to something is apparently
an expression of will.
Let us continue, and first present these three fundamentally extended U-diagrams
from an Odesys/ ODL purpose in the order of the extensions: i.e., (1) design pro-
cess, see Figure 3.11, (2) living dialogue, see Figure 3.12 and (3) learning process,
see Figure 3.13. These have been developed by Wolfert. We will later specialize
these basic schemes in the Chapters 4, 6 and 9, where the U-models are expanded
in more detail specifically for Odesys and ODL. After this interlude, we will elabor-
ate, detail, and link the design basis diagram to intuitive thinking (slow thinking)
and appropriate for open loops management as well. The common thread of these
diagrams is that when the actors goes through the U-model, they actually go
through an awareness process of consciously disclosing/unlocking their common
purpose or uncovering their common will (thinking slow combined with thinking
intuitive). The U-process goes from an open mind (imagination) via an open heart
(inspiration) to the open will (intuition), and then in reverse and ‘renewed’ to an
action of response via an inner dialogue. This action comes from the free will
where the ’contradiction’ or reversal of impulse and motive have coincided.
120 CHAPTER 3. MANAGING THE SERVICE PROVIDER ORGANIZATION

Figure 3.11: The new U-model for (re)designing within Odesys’ context, as developed by Wolfert
from Glasl (1998) and/or Scharmer (2016).

Figure 3.12: The extended U-model linked with the human ninefold and dialoguing with the ‘blind
spot’ (or ‘silent self’), as developed by Wolfert from Glasl (1998) and/or Scharmer (2016).
3.3. OPEN LOOPS MANAGEMENT, AN ACT OF U-NCOVERING 121

Figure 3.13: The new U-model for open design learning (ODL concept), as developed by Wolfert
from Glasl (1998) and/or Scharmer (2016).

We make a few extra notions:

(#1) The design U-model first of all shows three stages (compare Glasl’s meta-
morphosis stages) containing 3 P’s, 6 D’s, and 4 C’s, see Figure 3.11: i.e., (1)
’Picture’ - Devise / Download / Connect (2) ’Purpose’ - Consign / Dialogue / Dis-
close / Conceive (3) ’Prototype’ - Conciliate / Develop / Design. We will further
detail this social-technical-purpose design metamorphosis approach and use it in
more detail in the next Chapter 4.
(#2) The individual actor adds something of himself to the world, from outside
the ’ego range’ (from the inner self), which actually represents the deepest point of
the U ( a sort of ‘aha erlebnis’ or ‘gut sense/feeling’). To better understand what
takes place in this deepest U-point, (which Scharmer calls ‘presencing’, we will
introduce the following two concepts, see Figure 3.12: i.e., (1) the living (design)
dialogue, see Buber (2004); Bohm (2004), and (2) ’breathing’ with focused & open
attention, see Barendregt (2002); Palmer (2010); Van Lommel et al. (2009); Za-
jonc (2008). A living (design) dialogue assumes a conversation and a necessity to
listen to the other. Its creator/’father’ Martin Buber indicated that a real discov-
ery of a true ’I’ lies in the encounter with ’You’, and ’I’ does not exist without a
relation with ’You’. According to Buber (2004), a dialogue constitutes the basis of
philosophy in general due to the fact that it is the only effective form of communic-
ation in contrast to one-sided expressions of opinions. In other words, in the space
between one and the other (subject-object and/or subject-subject), a place can be
found where new ideas can emerge. From this principle arises the so-called design
dialogue as part of the U-model. A design dialogue is a way of ‘intuitive thinking’
via concentrative inter-sensing-acting on practice that brings together awareness
and insights as stepping stones towards the creation of new design. This living
design dialogue is an active ‘inner’ dialogue with yourself and/or an ‘outer’ dia-
logue with an open-source model that represents the design problem. To activate
this process the first step is consigning (you go as if you would say go to sleep or
to bed and you let go), and then continuing to ’breathe’ with your full attention.
122 CHAPTER 3. MANAGING THE SERVICE PROVIDER ORGANIZATION

You could see this as a kind of breathing between focused and open attention.
First we are intently focused on the object of design, but then the object is con-
signed and our open, non-focal, awareness is sustained and a redesign starts to be
conceived (note: for further exercises to support this process see Zajonc (2008)
and/or the eurythmic movement ‘Ich denke die Rede’. This can be seen as both
an introductory exercise and a basic exercise to train the soul, see Steiner (2019).
In addition, the movements of the vocals A-O-U are recommended to support the
U-metamorphosis from ‘I look around in the present world’ to ‘I contribute to a
future world’).
(#3) With regard to the U-model which was developed for the innovative ODL
education concept, some special characteristics have been added (see Figure 3.13).
First, it can be seen that the U actually consists of two parts in the learning process:
a so-called top down learning process and a bottom up learning process. In other
words, from top-head cognition to hands-on and from bottom-hands practicing
back to head, connected via the heart. This is called pure integrative education
(see Ackoff (2008); Biesta (2014); Wiechert (2012) amongst others), a path of
knowing (‘kennis’) and being competent (‘kunde’). The emergence resulting from
this knowledge/competence synthesis is the art of designing (‘kunst’), see further
on in Chapter 9 for the integrative ODL education concept. A second interesting
addition/ observation is that the heart means, in our case, the context (reflective
practice) of a so called self-chosen system of interest. This context is a stimulus
driven learning vehicle (see Chapter 9). The essence is that the student transforms
existing concepts via the self-chosen system of interest into a self-created learning
response (an appraisal or improvement proposal for its context). Last but not
least, the deepest U-point deserves some extra attention. It requires on the one
hand letting go but at the same time this letting go needs a kind of counter force
to play (practice, test) with the concepts and the new ideas in the self-chosen
context (playing like a young child that learns through playing). This ’playing’
or practising will become important when we add open source modeling to the
designing U-model (see Chapter 4).

The open loops management U-model

After this interlude we now can (finally) introduce the new Odesys U-model that
has been developed for the purpose of open loops management. Open loops man-
agement is about redesigning (control and change) the management system of an
organization, which is a set of strategies/structures and processes based on which
an organisation operates. Traditionally, most organizations focus mainly on their
technical or instrumental system (i.e., actual processes and tools), much less on
their socio-eco organizational context, and least on their own identity, purpose, or
cultural system (with crucial implications for their business performance, think of
3.3. OPEN LOOPS MANAGEMENT, AN ACT OF U-NCOVERING 123

Nokia as an imminent example, amongst others). Similarly, when it comes to new

solutions resulting from various sources of disturbances, the service provider tends
to think and act from its ‘familiar and visible instrument’. The service provider
thus neglects the context and often ignores its purpose (see Hastings (2015) and/or
the NEN-15288/ NEN-15504 process capability system for life cycle management).
Therefore, based on the renewed basic U-model as indicated in the previous inter-
lude, we have proposed here an integrated open loops management U-approach. In
Figure 3.15 we see this resulting open loops management U-model fully connected
with its Open Management System (OMS) diagram. The content and the details
of the figure speak for itself, however we make a few extra elucidative notions:
(#1) The OMS diagram contains three subsystems: i.e., the purpose-, the
social-, and the instrumental subsystem. Using the principle of reflection from the
threefold man and his senses, it is also seen as the open source (will), the open
heart (feeling), and the open mind (cognition). We see that the related U-model
goes in two directions: (1) from the instrumental processes to the organizational
identity/ socio-eco purpose (left U) (2) from the renewed purpose to the adapted
or renewed processes and tools. Actually, the OMS diagram is in itself thus only
a one-way view but not yet an integrative management approach. Only by go-
ing through the U will you arrive at such an approach, at an act of uncovering
or unlocking the organisation’s common will. To do so, you therefore have to go
through the process ’twice’ in opposite directions to unite the organizational man-
agement impulse (internally or externally driven) with its common motive: the
essence of the U (and therefore two opposing arrows have been added to the OMS
diagram). In other words, we have integrated the U-model with the MS diagram
via a bottom-up and top-down synthesis using the human ninefold of being for
open loops decision making. In doing so, the U-model goes beyond the one-sided
management approach of ‘structure follows strategy’.
(#2) If we zoom in a bit more to the middle axis (the mirror axis) of the U
we see the concepts of re-convert, re-concile, re-purpose, and re-generate linked
back to the open mind, the open heart, and the open will and the open source of
the U. This middle axis of the U expresses that this is a recursive, cyclical, and
open-ended process (see Lievegoed). In reality, then, the U will be cyclical and
open-ended in order to arrive at new intermediate results and (for then) best fit
for purpose solutions. That is why for us open loops management (and we will
also show this in Chapter 4 for open design systems) is so intimately connected
to the U. To reflect or unite this, the open-ended cyclical approach is depicted in
the middle of the U, see Figure 3.14. You could say that this symbolizes a ‘re-
Union’ process, since a re-uniting process takes places where a perfect (perhaps
temporary) emerge into U-nity or synthesis (note: re-, expresses a ”repetition of
an action” and unite expresses ”join together and make it into one” → re-Unite).
As a final detail we have just therefore the open loops that in the previous part
124 CHAPTER 3. MANAGING THE SERVICE PROVIDER ORGANIZATION

rotated clockwise here flipped and directed anticlockwise to even more symbol-
ize its cyclic and open-ended connection with the U (the symbol is actually a ‘U
on-the-run’). In the following Chapter 4 (and later in Chapter 9), we will see
that these open loops decompose into three cycles: the technical-cycle (configur-
ation/concreation), the social-cycle (context/conciliation) and the purpose-cycle
(synthesis consign/conceive) respectively.

Figure 3.14: Open loops Management and Open design, a re-Uniting approach.

Note that applying the U-model in practice also shows that you can complete a
sub-cycle faster on partial aspects than the whole (e.g. a sensitivity or impact
check of a single design parameter). You could call this, as it were, ”crossing over”
from the left side to the right side, and then continuing the entire U again. In
short, a dynamic design and decision-making process.
(#3) Last but not least, the new U-model leaves the possibility of extending
this with open-source computer model support. We will see this again in Chapter
4 as far as the elaboration for the open designing U. One could say that the meta-
morphosis process of ‘picture-purpose-prototype’ in those cases where humans can
use a computer system as a management decision support tool given the (too)
many combinations of new solutions. This supports the capturing of the proper-
ties of the socio-eco organizational context and provides support in the purpose
process by realizing, based on logical/mathematical reasoning, a combined inner-
outer source which together can arrive at new synthesis solutions. As an example
within the open loops management context, we developed the MitC tool, which
is a concurrent decision support tool for best fit for common purpose mitigation
measures for construction projects on-the-run, see Kammouh et al. (2021). Here
we have combined ”slow and intuitive thinking” with an actual representation of
how a project manager plans by applying an open design systems’ approach that
goes beyond basic PCDA or MI/MII cycles for dynamic planning and control.

In Chapter 4, we will show that this approach can also be made applicable to the
design process of physical/engineering assets with a link to an open source decision
support model, a mathematical optimisation model for maximising the common
purpose.
3.3. OPEN LOOPS MANAGEMENT, AN ACT OF U-NCOVERING 125

Figure 3.15: The new U-model for open loops management linked with the Open Management
System, developed by Wolfert from earlier ‘U-work’ by Glasl (1998) and Scharmer (2016).
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Chapter 4

Designing to best fit for common purpose

Having zoomed out considerably in the previous Chapter(s) from different per-
spectives and/or paradigms, other than just the technical and/or empirical sci-
ences, in this Chapter we will again zoom in on that part of the service provider
organization that is responsible for the design and/or management of their engin-
eering assets and the related quality of service (QoS) levels. We have seen that
these physical assets are one of the ‘supply’ subsystems through which this res-
ulting QoS is enabled. They are the suppliers thereof and they determine what
the system is capable of delivering in terms of performance. They thus give fulfil-
ment to the common ‘demand of all’ people involved, the common interest of all
stakeholders. Ultimately, it is a dynamic interplay between what the engineering
assets/objects are capable of, what the people/subjects collectively desire, and
how each individual subject’s preferences are represented.
The purpose of this Chapter is therefore to establish a perspective on how these
common interests can be structured and/or obtained to form the basis for designing
(managing and/or renewing) to a best- fit for common purpose engineering asset.
Here the new common socio-eco interests diagram, or design tY value framework,
holds an important place to reconcile the collective design input (here tY stands
for ‘design to Y values’, where the notion of Y will become clear later). This
diagram, as developed by Wolfert, is innovative in the following aspects: (a) it
is integrally linked to the socio-eco purpose embedding system dimensions and
the organization of which the engineering asset will be part, and (b) it indicates
an important clarification with regard to this in contrast to most similar existing
diagrams, like those from classical systems engineering (SE) books (see Blanchard
& Fabrycky (2011); Dyme (2004); Wasson (2015), amongst others).
Furthermore, we will see that the associated open design system considers three
subsystems or open design loops: (1) the technical-instrumental (’open config’),
(2) the social-contextual (’open space’), and (3) the purpose-idealized design (’open
source’) subsystems. This is in contrast to similar engineering design systems

127
128 CHAPTER 4. DESIGNING TO BEST FIT FOR COMMON PURPOSE

which often recognize less than three subsystems (see the aforementioned classical
SE books).
Within the second part of this Chapter the threefold open design system is
further linked to the U-model for designing, providing a refinement of the open
design U model, as developed for open loops management in Chapter 3. This is
also innovative in its character, as the U-approach connects the technical and social
human design process through a three-layer metamorphosis of picture-purpose and
prototype. This perspective culminates in an entirely renewed so-called Odesys U-
approach, as developed by Wolfert: i.e., the open config, the open space, and the
open source, which is an open-ended spiral design metamorphosis. In doing so, it
goes far beyond classical models such as the well-known SE V-model, with best fit
for common purpose socio-technical solutions as a result. As a whole, this Chapter
forms the basis for the (process) approach of the open design system methodology,
which will be further examined by zooming in after this Chapter. From there
we will focus on the modeling approach which is integrated into the open design
system methodology.
This Chapter assumes that the basic principles and concepts from previous
Chapters are known, because we will continue to work with these and zoom in
on the main parts from Chapter 3 (social threefold modeling, socio-eco purpose,
societal threefolding embedding system dimension, U-model etc.).

4.1. Common socio-eco interests, the design tY model

In view of the systems engineering design terminology that is often found confusing,
the logic of the Open Design Systems (Odesys) terminology is first summarised
here from the high level split between ‘common interests’ and ‘common purpose’
(at the end of this interlude, a note is placed with the subtle terminology differences
viewed from their etymological context).

Interlude open design system definitions

Stakeholders often start from vague needs of what is required to fulfill their pur-
pose. Sometimes these needs are already a lot more concrete and the stakeholder is
able to formulate more specific requirements. The set which combines the relevant
socio-eco concerns within a specific context is ‘at stake’ between all involved people
and is therefore called ‘common interests’. In other words, the stakeholder’s com-
mon socio-eco interests can generally be translated for a new engineering artifact
into design considerations consisting of needs and/or requirements. We will refer
regularly to ‘design for tY values’ as the common interests because they express a
common well-being and/or the intrinsic worth of an artifact. It will be seen later
that these values are always expressed in words ending in -ty, so design to or for
tY (which is a new concept within systems design and engineering context).
4.1. COMMON SOCIO-ECO INTERESTS, THE DESIGN TY MODEL 129

These common interests (or design for tY values) can be further translated
or converted by the designer into collective objective functions, constraints, indi-
vidual preference functions (and weights), design performance functions, endogen-
ous and/or exogenous design variables, and their specific bounds. It should be
noted here that the design variables are those over which the designer can still
exert his influence and which can be directly linked as a property to the object or
sub elements hereof (i.e., degrees of freedom). The objective functions are subject-
related goal programming functions which can be linked (in)directly to the design
performance functions (i.e., an expression of the degrees of capability). These ob-
jective functions may be of different importance to each individual stakeholder and
therefore should be expressed as a preference function per individual stakeholder
with associated weights (i.e., an expression of the degrees of desirability).
In summary, it is the designer’s challenge to convert these common interests
comprising of design considerations, such as needs/desires (e.g. ’a noise reduction
of xx dB must be realised’) and/or more concrete requirements (e.g. ’noise barriers
must have a minimum height of xx m’ ), into an integrative open design system
articulated through preference-, objective-, and design performance functions that
reflect these common interests (i.e., design conciliation input). With this integrat-
ive mapping of common interest into these type of functions, the designer can then
support the open-ended design process to arrive at a best fit for common purpose
design configuration. Note that the following etymologies of the main terms and
their subtle differences:
• interest - ‘from interesse’, ‘to concern, make a difference, be of importance’,
literally ‘to be between (people)’.
• value - ‘the intrinsic worth of a thing’, ‘degree to which something is fit for
purpose’, ‘social principle’ (supposedly taken from the art language).
• desire - ‘express a wish to obtain’, ‘from Latin de-siderare’ and therefore
closely related to consider.
• consider - ‘to fix the mind upon for careful examination’, from Latin con-
siderare. Probably literally ‘to observe the stars and convene/congregate
these’.
• require - ‘repeatedly’ (see re-), + quaerere (Latin) ‘ask, seek’ (see query); ‘to
need for some end or purpose’.
• need - ‘be required for some purpose’, ‘require, have need of’ / purpose:
‘originates from put forth for consideration’, ‘a thing proposed for a certain
intent/ interest’.
130 CHAPTER 4. DESIGNING TO BEST FIT FOR COMMON PURPOSE

Incitement 4.1 Desires, the parents of thought

Often not only is ‘desire the father of thought’, but all feelings and habits
of thought are actually the ‘parents’ of thought itself. From experience
we know that one can rarely convince someone by using only logical argu-
ments. Something, which lies much ”deeper” in man than logical points of
view, often prevails over one’s decision or action for response. Could this
have something to do with our motives, intentions, and impulses? And,
might these in turn arise only from these ’parents’ of thought? Or could
they also arise from an interplay between with the living (thoughts) world
around us? To answer this question, we could first ask ourselves if water
can be drunk from a glass without water? In other words, can thoughts
be extracted from a world around us where there are no thoughts?
Finally, what would the ”parents of our thoughts” and the ”thoughts
around us” mean for our common interests as inputs to the new world to
be designed and created around us?

Within the Odesys methodology, we take the more commonly used collectivistic
utility based design and decision-making theory/practice a step further because
we look not only at utility in the economic sense, but consider the social system’s
identity that is both economically, isonomically, and ecologically determined. From
this aggregated property we can determine the group’s well-being optimum. We
know that this aggregated property is by definition the group’s well-being optimum
since the fundamental laws & principles of all three social threefold realms, which
are the social/sociological laws and sociocratic/solidarity principles, are maximally
leveraged (see Chapter 3). In other words, an a-priori and equitable (but not
necessarily equal) inclusion of all people’s interests in the design decision-making
process will lead to synthesis solutions to best-fit for common purpose rather than
to compromise solutions, where everyone’s individual dissatisfaction is minimised
and where the group outcome turns out to be sub-optimal. Later in this book
(from Chapter 6 onwards), we will see how to give further content to generating
these design synthesis solutions supported by mathematical optimisation modeling.
For this, we have developed the Odesys methodology, introducing the Preferendus
as the ultimate participatory decision-making tool to arrive at an optimum on
maximising common societal goals (inspired by and a reference to the preferendum
concept and the word preference, see Incitement 4.2).
Note: (1) the fit/fitness for common purpose concept expresses the intrinsic
quality of service (QoS) or real service quality of an artefact which we will refer
to as the aggregated social system’s identity (see Chapter 3); (2) synthesis is
part of Hegel’s dialectical threefold categories: thesis- antithesis- synthesis (see
Chapter 1). In other words, fit for purpose is the feasible synthesis or unity within
the threefold of social-interests-desires/ technical-behavior-capabilities/ purpose-
quality-feasibility; (3) With regard to the modeling, we can state that this is seen
4.1. COMMON SOCIO-ECO INTERESTS, THE DESIGN TY MODEL 131

as a logical act of reasoning to unlock the outer environment (‘common will’),

also called thinking slow: deliberation. However, intuitive thinking is an act of
uncovering the inner will: a dialogue. When the two coincide and are unified, by
definition a synthesis solution as a free thinking result is reached (see Chapter 3).

Incitement 4.2 Referendum or Preferendum?

Wouldn’t it be great if we lived in a society that decides together, for each other, and with
each other? Is a society possible where citizens have equal rights, where the scientist can
inform freely, where business takes place associatively with money not as a goal but a means,
and where the government acts as a ‘true civil servant’ (social threefolding). A value-wish
dialogue between citizens of all ‘sorts and sizes’ seems to be the brittle cement of a free
society. Or as Lucebert (Dutch artist/poet) famously said, ”everything of value is defense-
less”. The question now is how can we extract desires, wishes, and values from people and
society to use them for participatory (deliberative) decision making. A process that starts
from these common interests of everyone to design an a-priori synthesis, in which values
and wishes are maximised, rather than to appraise an a-posteriori sub-optimal compromise
(‘after the fact’). Does this mean that we would have to go for a Preferendum, rather than
a Referendum? If so, how might we give substance to this and what type of support tools
could be of interest covering both the social desirability and the technical capability of a
system?

Notes: (a) ’The preferendum brings citizens and politicians closer’ according to Dr. David
van Reybrouck in De Standaard’ on Oct. 30, 2021 (vReybrouck is a Belgian cultural his-
torian, archaeologist and author and also a well-known advocate of the Preferendum); (b)
Already in 2007, the WRR indicated that the results of a referendum do not sufficiently
reflect the wishes of the citizens. At the same time, it indicated that this would be clearer
with a preferendum (WRR is the Scientific Council for Government Policy making, an in-
dependent think tank and advisory body to the Dutch government).

Best fit for common interests & purpose

Here, we will describe the best-fit for common interests and purpose design con-
cepts, which are the key elements of Odesys. The aim is to support a design process
describing how to get from common socio-eco interests (needs/requirements/ de-
sires) to a design proposition or synthesis that best fits the common purpose. This
process can be seen as the search for a dynamic interplay and/or optimal equi-
librium between what people want (demand) and what technical assets can offer
(supply). Design is thus a process of finding the leeway within the design vari-
ables: i.e., the design degrees of freedom, to best achieve this equilibrium between
132 CHAPTER 4. DESIGNING TO BEST FIT FOR COMMON PURPOSE

common desirabilities and possible capabilities, given all individual preferences of

those involved and all physical constraints or other limitations. We call the results
of this the synthesis equilibrium, which is a measure of the degree of satisfaction
(for the individuals) and the degree of capability (for assets). When we have ob-
tained such a result, we call it a ‘best’ feasible design configuration or prototype
given all the common interests and purposes.
Let us now introduce the new state of the art open design systems that is
explained by the following three parts, shown in Figure 4.1, Figure 4.2, and Fig-
ure 4.3 (as developed by Wolfert). The content and the details of the above three
figures speak for themselves, however we make a few extra notions:
(#1) Since design is primarily an activity that goes from human being (mind-
the inner) towards materialisation (matter- the outer), we must congregate the
common interest, the human inner sources of design, as input. We do this by
zooming out to the embedding system’s context in which the new engineering
asset will find its place and to the organisation where it will be managed and/or
maintained. Combined with the user needs/wishes/requirements, these form the
collective design input of the relevant stakeholders for the new engineering asset. In
Chapter 3, we saw a threefold, seen from the inside of the organisation (economic-
isonomic-ecologic). Here, seen from the inside of the designer, we see a threefold
of a technical subsystems (empiric), a social subsystem (context), and a purpose
subsystem (idealised design).

Figure 4.1: Part 1 - The congregate of common interests.

4.1. COMMON SOCIO-ECO INTERESTS, THE DESIGN TY MODEL 133

Figure 4.2: Part 2 - Common socio-eco interests diagram, a participatory design tY framework,
derived from societal and organizational needs/wishes/requirements. The design tY values, to be
translated into preference-, objective-, and performance functions and/or constraints (not limitative).
** Adaptability, Capacity, Comfortability, Connectivity, Creativity, Distributivity, Flexibility, Fraternity,
Immunity, Integrability, Integrity, Invest-propensity, Liberty, Liveability, Operatorability, Profitability,
Reliability, Resistivity, Reconfigurability, Predictability, Recyclability, Security, Solidarity, Supportabil-
ity, Simplicity, Servicability, Scarcity, Transportability, Testability, and Vulnerability.

In other words, from the design/decision process that arises from the inner, man
will strive to put a technical/physical system in the outer world which tries to find
the best match with his/her idealised design, the purpose system, via a collective
social system (the system of common interests).
(#2) Depending on the system’s context, a multicoloured pallet of interests can be
desired by the human stakeholder. We often express these in terms of objectives
(goals) such as availability, affordability, dignity, etc., the so-called design to Y
(tY) values. All these design tY values are purposive and thus solely linked to
134 CHAPTER 4. DESIGNING TO BEST FIT FOR COMMON PURPOSE

the human/subject and not to the artefact/object. The system can only assume a
state because it is connected and/or controlled to these human objectives. There
always underlies an (inner) goal-oriented human phenomenon to the final system-
state and/or performance behaviour of the (outer)object. This system-state can
only be achieved by the input of the designer and after the designer has synthesised
and configured the design variables accordingly.

Figure 4.3: Part 3 - The best fit for common purpose model.

(#3) Following from the social laws (see Chapter 3), we argue that a best design
is that design which suffices the collective group’s well-being that is defined as the
maximum of the aggregated individual preferences for the different design object-
ive functions (i.e., for the different design tY values) given the design performance
functions and constraints. The ‘technical’ result is a set of degree of capability
values, which describes the prototype configuration and its dimensioning. The
‘social’ result is a set of degree of capability values (i.e., common objective func-
tion values) and set of degree of satisfaction values (i.e., preference functions per
individual).
4.2. OPEN DESIGNING, AN ACT OF U-NCOVERING 135

4.2. Open designing, an act of U-ncovering

We take here the starting point of the design U-model showing its three meta-
morphic stages, comprising 3 Ps, 6 Ds and 4 Cs: (1) ’Picture’ - Devise/ Download/
Connect (2) ’Purpose’ - Consign/ Dialogue/ Disclose/ Conceive (3) ’Prototype’ -
Conciliate/ Develop/ Design (see the basic design U-diagram in Chapter 3). To
make the basic U-diagram more specific for the open design system (Odesys) ap-
proach, in addition to the starting points from the basic diagram, we draw up
some important key points for this specific purpose.

Incitement 4.3 Design to consider

Consider the decision making process regarding former military

airbase Valkenburg, near Leiden in the Netherlands. The politi-
cians decided that the best thing to do was to close this military
airbase to solve Leiden’s housing problem. The mayor of Valken-
burg was unsure whether this was indeed the best choice. Proper
modeling of this multi-criteria decision making problem, taking
into account all stakeholders’ interest, showed that closing the
airbase was far from being the most preferred alternative. Only
after removing the criteria of safety, costs, and nature preserva-
tion did closing the airbase become the most preferred option.
Clearly not all interests were part of this decision making process.
Unfortunately, it is common that political manipulation leads to
single purpose poor decision making. How can we then ensure
open and transparent integration of all stakeholders’ interests to
be considered when modeling the design/decision problem?

A professor gave a balloon to every student, who had to inflate

it, write their name on it, and throw it in the hallway. The
professors then mixed all the balloons. The students were given
five minutes to find their own balloon. Despite a hectic search,
no one found their balloon. At that point the professors told the
students to take the first balloon that they found and hand it
to the person whose name was written on it. Within 5 minutes
everyone had their own balloon. Wouldn’t design also be better
when considered a participatory process?

The engineer’s ideal of Caesar’s war chariot is that which never

fails but at the end of its lifetime disappears completely into dust.
If one bolt would still remain, then that bolt would have been
constructed too conservatively and that would have had adverse
weight implications. Unnecessary weight impairs the effective-
ness of the chariot, which Caesar would never have accepted.
How can we then incorporate the right design performance con-
siderations so that it does not lead to over-dimensioning, while
satisfying the user needs during the operation phase?
136 CHAPTER 4. DESIGNING TO BEST FIT FOR COMMON PURPOSE

First let us take a closer look at the etymology of the word design (’de-sign, and
in Dutch: ’ont-werpen’). The prefix de- (in Dutch ont-) has a special meaning here:
(1) spontaneously starting (e.g. decaying, or another Dutch example word ‘ont-
branding’); (2) removal of something or even put away (e.g., defoliate or decode,
or another Dutch example word ‘ontcijferen’). Note that especially in Dutch the
prefix ‘ont-’ can also mean ’uit’ (out of someone/ out of something, think of the
word ‘ontvangen’). In short, and if we combine these etymologic starting points,
the word design can mean both the starting process ‘signing’ and/or the opposite
(direction) of ‘signing’, which can mean not signing but read and gather (as an
act of gathering towards your mind: ’intel-ligence’). Clearly, the latter also means
an opposite movement towards itself (from ’another’), expressed with the word
conceive (or in Dutch ‘ont-vangen’). In short, these notions play a role (perhaps
mirrored) in the process of designing. This would allow us to see that the U, and in
particular its deepest point, encompasses actually two opposing movements, which
meet interactively withing a living ’dialogue’. From Chapter 3 we know that these
two movements of ’the common will’ become visible from the technical matter.
The inner source then works towards/ in the ’technical matter’ to unite the design
impulse (internally or externally driven) with its motive.
A living design dialogue is an active ‘inner’ dialogue with yourself. To activate
this process the first thing to do is consigning (you go as if you would say to
sleep, allowing yourself to let go), and then continuing to ’breathe’ with your
full attention. You could even see this as a kind of breathing between focused and
open attention. Actually, a supportive open-source model exists within the Odesys
approach. This is a second source that represents the design problem reflecting
both the human desirability and the engineering artefact’s capability. The designer
can therefore have a second support tool within the dialogue which is the ‘outer’
dialogue via or with this model. To activate this process the first thing to do is
just to play and practice a bit with the model, and then reflect on the ‘proposed’
high level design synthesis outcome (let it be generated and try to recognize). In
other words, the consigning process requires on the one hand letting go but at the
same time this letting go needs a kind of counterforce to ‘play’ with the model
(practice, test, appraise) within the design context (playing like a young child that
learns through playing).
With all the aforementioned designing specifics we can convert the basic U-
diagram from Chapter 3 into the fundamental Odesys U-model that will be the
basis for the open designing process, see Figure 4.4. The central thread of this U-
process-diagram is that when the designer goes through the U-model, he actually
go through an awareness process of consciously disclosing the common purpose
or unlocking (i.e., uncovering) the common will which is a form of thinking slow
and intuitive thinking. The ’thinking slow part’ here can be fulfilled by a sup-
porting computer model. This action comes from the free will where the ’contra-
4.2. OPEN DESIGNING, AN ACT OF U-NCOVERING 137

diction’ or reversal of impulse and motive have coincided (here common interests
and desires, see Chapter 3). The U-process moves from an open configuration
(mind-imagination) through an open space (heart-inspiration) to the open source
(will-intuition), and then through an inner dialogue proceeds in the opposite and
’renewed’ direction to an action of response. This action of response is the real-
isation of a prototype configuration. This unification covers a new and extended
Odesys’ U which provides the foundation for a socio-technical design process with
a best fit for common purpose result (it will be made even more specific for math-
ematical modeling in Chapter 6).

Figure 4.4: Odesys’ basic U-model, as developed by Wolfert from Glasl (1998) and/or Scharmer
(2016) (extended from the starting U-models in Chapter 3).

We make a few extra notions:

(#1) The model under discussion here is a mathematical optimisation model, sup-
portive to the integrative unlocking process (note, this model neither descriptive
nor predictive in character). It is a mathematical representation of the design pro-
cess, where all human-oriented preference and objective functions and all object
performance functions and constraints are brought together to find the maximum
aggregated preference of all stakeholders involved (i.e., a re-purpose process). Be-
cause this re-purpose involves a complex set of relationships and possibilities, a
138 CHAPTER 4. DESIGNING TO BEST FIT FOR COMMON PURPOSE

search algorithm is called in to find this best fit for common purpose. The best fit
for common purpose is the synthesis or golden mean, a design point which unites
all the open source input (system capability and human desirability) the best. We
will elaborate on this in Chapter 6 and beyond, zooming in one step further (from
embedding systems dimension Chapter 3, to the threefold of preference, objective
and performance functions as of Chapter 6). Note that here another form of uni-
fication takes places as the logical act of reasoning (outer deliberation and open
source input) coincides with intuitive an act of intuitive thinking (inner dialogue
and open source output) resulting in a synthesis solution as a free design modeling
result.
(#2) It is also important here to put the new open design U-model next to the
more common SE V-model. First of all, we see that both models fit together
seamlessly. The U-model is used when there is still substantial design freedom and
has a human-driven focus with a new prototype configuration as its response. The
V-model is used when a prototype is being engineered into a lower level of detail
for subsequent construction with a new artifact as its response. What is further
noticeable is that both models form a ’mirror image’ of each other in every tone.
In the case of the U-model, there is even a particular ‘crossover’ visible (devise
vs. ’envise’/envisage, consign vs. design), see Figure 4.5. All of this gives the new
Odesys’ U high added value in designing what people want and the engineering
artifact can deliver, reflecting a pure socio-technical modeling approach. Once the
most desired socio-technical solution has been configured, the Odesys designer can
pass on his response to a structural engineer to further detail this response and
realize it according to the engineering development V-model. Note that the U-
and V-model go hand in hand, with the U-model in the lead, representing a joint
W-model approach (see also Chapter 9 for the ’double-U’ principle).

Figure 4.5: The mirror image and connection of the U (open design) and V (engineering development)
model.
4.2. OPEN DESIGNING, AN ACT OF U-NCOVERING 139

We can now (finally) introduce the full new Odesys’ U-model that has been de-
veloped for the purpose of open design systems, see Figure 4.7. ’The common will
becomes visible from the technical matter, the inner source and the open source
work towards/ in the technical matter’ via a threefold of open config re-converting,
open space re-validating and open source re-purposing/ re-synthesizing. Tradition-
ally, most designers (or engineers especially) focus mainly on their technical design
configuration by parametric engineering, much less on the socio-eco organizational
context, and least on an idealized design or purpose system. This is why, so
often, we build what no one wants. Moreover, this is why, so often, engineers
optimise their solution only for the technical subsystem properties, disregarding
stakeholder’s preferences (i.e., only a technical driven parametric design approach).
Therefore, based on the fundamental basic design U-model as indicated above (see
Figure 4.4), we have proposed here the integrated new Odesys’s U-approach. In
Figure 4.7 we see this resulting Odesys’ U-model fully connected with its open
design system diagram. The content and the details of the figure speak for itself,
however we make a few extra and final notions:
(#1) The Odesys U-diagram consists of three subsystems: i.e., the purpose (ideal-
ized or best-fitting design), the social (common socio-eco interests) (the social
context), and the technical (design performance configuration) subsystem. Using
the principle of reflection from the threefold man and his senses, it is also seen as
the open will/open source, the open heart/open space, and the open mind/ open
config subsystems. Here, we see that the related U-model goes in two directions:
(1) from the technical system (in light of a high level ‘picture’, e.g. a bridge instead
of a tunnel as a fit for a connection) to its intended purpose, which is the left of
the U, and (2) from a renewed purpose to the adapted or renewed (engineering)
configuration, which is the right of the U. Actually, the Odesys U-diagram is in
itself a top-down view only, but not yet an integrative approach. Only by going
through the U will you arrive at such an approach, at an act of unlocking the stake-
holders common will. To do so, you must therefore go through the process ’twice’
in opposite directions to unite the design impulse (in- or externally driven) with
its common motive, so that social interests can coincide with technical achievab-
ility : the essence of the Odesys-U (and therefore two opposing arrows have been
added to the Odesys’ U). In other words, we have integrated the U-model with the
Odesys’ U via a bottom-up and top-down synthesis using the human nine-fold of
being for open designing (see for the U-basics Chapter 3). In doing so, the Odesys
U-model goes beyond the one-sided design approach of ‘detailed implementation
design follows strategic sketch design’.
(#2) If we zoom in a bit more on the middle axis (the mirror axis) of the U, we see
the concepts of re-convert, re-validate, re-purpose, and re-generate/re-synthesize
linked back to the open mind, the open heart, and the open will and the open
source of the U. This middle axis of the U expresses that this is a recursive,
140 CHAPTER 4. DESIGNING TO BEST FIT FOR COMMON PURPOSE

cyclical, and open-ended process (see e.g., Lievegoed). In reality, then, the U
will be cyclical and open-ended to arrive at new intermediate results and (for
then) best fit for purpose solutions. That is why for us open design loops are
so intimately connected to the Odesys U. To reflect or unite this, the open-ended
approach is depicted in the middle of the U, see Figure 4.6. You could say that this
symbolizes a ‘re-Union’ process, since a re-uniting process occurs where a perfect
solution (perhaps temporary) emerges in unity or synthesis (note: re-, expresses a
”repetition of an action” and unite expresses ”join together and make it into one”
re-Unite). Here we then see the unique in its sort and state of the art threefold
Odesys U-model incorporating three open-ended design loops: i.e., a spiral of: (1)
Open config – technical cycle, (2) Open space -social cycle, and (3) Open source -
the purpose cycle (in contrast to similar classical engineering design systems which
often recognize only less than three subsystems loops). More generally formulated
so that these three cycles also apply to open loops management (see Chapter3)
and to open design learning (see Chapter 9), we can describe the three cycles as:
i.e., (1) the technical - configuration/concept, (2) the social - conciliation/context,
and (3) the purpose - consign/conceive cycle respectively.
(#3) Lastly, we make the following open-ended note (see Simon, 2019): ’A para-
doxical, but perhaps realistic, view of design goals is that their function is to
motivate activity which in turn will generate new goals.’

Figure 4.6: The three open-ended U-cycles (open design loops): purpose, social and technical: an
open-ended spiral design metamorphosis, as developed by Wolfert.
4.2. OPEN DESIGNING, AN ACT OF U-NCOVERING 141

Figure 4.7: The full new Odesys U-model with the Open Design System, developed by Wolfert from
earlier ‘U-work’ by Glasl (1998) and Scharmer (2016).
This page intentionally left blank
Chapter 5

Mathematical modeling design & decision

problems
Engineers commonly use optimisation and decision making models to select/choose
between design alternatives or to generate/configure design solutions: i.e., decision
analysis/evaluation versus design optimisation models. The novel Odesys method-
ology described in this book (see Chapter 6 and onwards) combines these models
into one overarching design and decision making methodology. Before combining
these models we need to formally define both and uncover the limitations of each.
We distinguish a-posteriori decision analysis models and a-priori design optimisa-
tion models. In the case of a-posteriori decision models the alternatives to choose
from are known and can be evaluated by means of a Multi Criteria Decision Ana-
lysis (MCDA) evaluation. Conversely, in the case of a-priori design optimisation
models the alternatives are not yet known up front and need to be generated by
means of Single and/or Multi Objective Design Optimisation (SODO/MODO)
search.
Note that as mentioned prior, for elucidation purposes, the MCDA and MODO
are discussed separately in Sections 5.1 and 5.2 respectively. In practice, this
’separate use’ may also be the case when we work from ’coarse to fine’ during
a planning and design process. An MCDA-approach can then first be applied
during a variants study (see e.g. Example 2 in Section 5.1) to determine a best
variant. A MODO-approach (see Section 5.2) can then be used to come to a best-fit
configuration within the chosen variant, given the design constraints, the different
stakeholder objectives, and given the design degrees of freedom (this rather than
just ’out-engineering’ a variant as is often done without true conflict of interest
dissolution, resulting in an artifact no one wants).
We now show how to apply these MCDA and MODO approaches in a math-
ematically correct way using preference function modeling (PFM).

143
144 CHAPTER 5. MATHEMATICAL MODELING DESIGN & DECISION PROBLEMS

Incitement 5.1 The principle of reflection

Q(1): An event took place in 2005 and another one in 2007.

What’s wrong with 2005 + 2007 = 4012?
Answer:
The sum of two times, as opposed to time differences, is
undefined because time scales are affine scales and the oper-
ation of addition is undefined for points on an affine straight line.

Q(2): 3:00 p.m. is 15:00 and 2:00 p.m. is 14:00. Is their ratio
3/2=1.5 or 15/14=1.071428571?
Answer:
The ratio of two times is undefined because time scales are af-
fine scales. The operation of division is undefined for points on
an affine straight line. In this context the number 1.071428571
has no meaning despite its scientific appearance. For the same
reason, the ratio of two potential energies is undefined.
The literature of classical decision theory and measurement the-
ory offers neither insight as to why “2005+2007=4012” nor these
aforementioned ratios are meaningless. For more inspiring incite-
ments visit: scientificmetrics.com.

5.1. Multi-criteria decision analysis & preference function

modeling
Multi-criteria decision analysis (MCDA) is integral to engineering design and man-
agement processes and is an important element in nearly all of their phases. View-
ing engineering design and management as a decision making processes recognizes
the substantial role that decision theory can play in their activities. Decision
theory articulates the three key elements of decision-making processes as:
1. Identification of options or choices.
2. Development of expectations on the outcomes of each choice.
3. Formulation of an evaluation system of values for rating outcomes to provide
an effective ranking to obtain the preferred choice.
The foundations of decision and measurement theory, however require major cor-
rections. Below we show, based on a new PFM theory developed by Barzilai, that
von Neumann and Morgenstern’s utility theory, which is at the basis of decision
theory, contains fundamental mathematical flaws with regards to modeling the
measurement and aggregation of preferences. We introduce this new theory of
measurement which provides mathematically well founded scales for the measure-
ment of preference.
5.1. MULTI-CRITERIA DECISION ANALYSIS & PREFERENCE FUNCTION MODELING 145

Incitement 5.2 When being good is not good enough

Consider a reputable construction company that used to address

their customers with a yearly survey to measure their perception
of delivered service quality. Respondents were requested to give
a grade on the following criteria: Communication; Reliability;
Delivery times; Eye for customer’s interests; Quality control;
Image. On all criteria the company scored well above seven,
so everything seemed to be in order. Until, that is, someone
raised the question: What do these seven actually represent and
what is the best and what is the worst you can score? And
above all and more importantly, how do you know that your
major competitors don’t score an eight and what is the scale of?
After all, to be selected in a bidding procedure, to be ‘good’ is
not good enough. One has to be perceived as better than the
competing candidates.
So the question remains: how can we properly measure the firm’s
performance on all criteria that reflects the firm’s relative posi-
tion using properly defined scales?

The issues with classical preference measurement theories

Classical measurement and evaluation theories, including utility theory, cannot
serve as the mathematical foundation of decision theory, game theory, economics,
or other scientific disciplines because their models do not satisfy the conditions
that must be satisfied to enable the application of the mathematical operations
of linear algebra and calculus. For modeling psychological variables, where the
existence of an absolute zero is not established, the only possibility for addition,
multiplication, order, and limits to be applicable is the model where the measured
objects correspond to points in a one-dimensional affine space over the ordered real
numbers. In such a space the ratio of two points is undefined while their difference
is a vector and the ratio of two vectors is a scalar. Note: psychological variables
relate to a human subject as opposed to physical variables that relate to a physical
object. For instance, the room that you are sitting in has a temperature that can
be objectively determined, whether or not you find the room to be warm or cold
is up to you to determine.
Ratios of variables for which the existence of an absolute zero has not been
established are undefined. For example, ratios T1 /T2 of temperature have been
undefined until it was established that temperature has an absolute zero, see e.g.
Von Neumann et al. (1955). In the case of time, the ratio t1 /t2 , where t1 and t2 are
two points in time, is undefined while the ratio ∆t1 /∆t2 of two time differences, i.e.
time periods or time intervals, is well-defined. It follows that the ratio is undefined
for any psychological variable since the existence of an absolute zero has not been
established for psychological variables.
146 CHAPTER 5. MATHEMATICAL MODELING DESIGN & DECISION PROBLEMS

Incitement 5.3 Job decision

Consider a person having to decide between two job positions with characteristics as shown
in first the Table below. Using the arithmetic mean (weighted sum) to determine the overall
rating of each position shows that position 1 is preferred over position 2.

Criterion
Opportunities Salary ($/Yr) Weighted sum
Position 1 15 50 000 20 009
Position 2 20 45 000 18 012
Weight 0.6 0.4
However, if we change the unit, which the weighted sum allows, for the salary criterion
from /Y r to k/Y r the order is reversed and position 2 is preferred over position 1, see the
following Table below.

Criterion
Opportunities Salary ($k/Yr) Weighted sum
Position 1 15 50 29
Position 2 20 45 30
Weight 0.6 0.4
How should this person now come to a well-supported job decision? Seemingly the weighed
sum produces an infinite number of non-equivalent ‘absolute’ outcomes which should be
relative... So the question remains: how can we mathematically correct determine and
aggregate scores on different criteria.?

Proper preference measurement scales

The expression a−b
c−d
= k where a, b, c and d are points on an affine straight line
and k is a scalar is used in the construction of proper scales. The number of points
in the left hand side of this expression can be reduced from four to three (e.g. if
b = d), but it cannot be reduced to two and this implies that pairwise comparisons
cannot be used to construct preference scales where the operations of addition and
multiplication are enabled.
A scale s is a mapping of the objects in an empirical system E into the ob-
jects in a mathematical model of that system M that reflects the structure of E
into M , see Figure 5.1. The construction of measurement scales requires that the
property-specific empirical operations be identified and reflected in the mathemat-
ical model. Moreover, the operations should be chosen so as to achieve the goal of
this construction which is the application of mathematical operations in the math-
ematical model. Note that the property (length, mass, etc.) of the objects must
be specified in order for the mathematical operations to be applicable and that
addition and multiplication are applied on lengths and masses of objects. It is not
5.1. MULTI-CRITERIA DECISION ANALYSIS & PREFERENCE FUNCTION MODELING 147

possible to “add objects” without knowing whether what is being added is their
mass, length, temperature, etc. In this context, preference is the only property of
relevance in the context of the mathematical foundations of game theory.
In conclusion, to create preference scales that enable the mathematical oper-
ations of addition and multiplication we need to map at least three alternatives
within the empirical system E to three objects in the mathematical system E.
Of these three alternatives the worst and best performing alternatives are used to
define the scale on which the third alternative is scored.

Figure 5.1: A scale is a mapping of the object in an empirical system into the objects in a mathematical
model of that system.

Aggregating preferences
Having determined how to create proper preference scales we also need to aggreg-
ate preference scores on different criteria given a set of weights. The weighted
arithmetic mean is commonly used to yield an overall preference scale:
m
X
V (a) = wi vi (a) (5.1)
i=1

Where V (a) is the overall value, performance or preference score of alternative a,

vi the value or preference score reflecting alternative a’s performance on criterion
i and wi the weight assigned to reflect the importance of criterion i. Employing
the weighted arithmetic mean to yield an overall preference scale is, however, a
mathematical modeling error for the following three reasons:
1. It produces an infinite number of non-equivalent outcomes.
2. Mathematical operations are applied where they are not defined.
3. It produces absolute outcomes without regard to other alternatives being
considered.
148 CHAPTER 5. MATHEMATICAL MODELING DESIGN & DECISION PROBLEMS

Ad. 1 - The output of this procedure depends on the units by which the scales
are measured. As a consequence, we can produce an infinite number of non-
equivalent outcomes, just by changing the units. This is an unacceptable property
of any mapping used to aggregate scales. Note that the definition of the weighted
arithmetic mean does not prerequisite having only normalized numbers.
Ad. 2 - The overall performance of an alternative is determined by multiplying
preference scores by weights assigned to criteria. As explained above, the math-
ematical operations of division and its inverse are undefined for any psychological
variable since the existence of an absolute zero has not been established for psy-
chological variables. Applying multiplication implies that the empirical system is
modeled by a vector space where addition and multiplication are defined. This is
however the incorrect mathematical model for representing psychological variables.
Ad. 3 - The weighted arithmetic mean is a mathematical formula that does not
take into account how other alternatives under consideration score. Instead we
need an algorithm for finding the aggregated preference score: i.e., the ‘best’ fit
of all weighted (relative) scores for all the stakeholders’ criteria, of an alternative
that minimises the least-squares difference between this overall preference score
and each of the normalized individual scores of this alternative on all criteria in
the affine space, by computing its closest counterpart, see Barzilai (2022).

Figure 5.2: Visualisation of PFM-based preference aggregation.

Figure 5.2 can be used to illustrate how preference scores can be properly
aggregated. The first variant scores highest on the criterion with the highest
weight attached to it, scores average on a less important criterion and scores low
on the least important criterion. As a result the aggregated preference score ends
up close to the score on the criterion with the highest weight. The opposite holds
for the second variant which scores lowest on the criterion with the highest weight
attached to it, scores also low on the less important criterion and highest on the
least important criterion. As a result the aggregated preference score ends up close
5.1. MULTI-CRITERIA DECISION ANALYSIS & PREFERENCE FUNCTION MODELING 149

to the score on the criterion with the highest weight. Finally, the third variant
scores low on the criterion with the highest weight attached to it, scores highest
on the less important criterion and lowest on the least important criterion. As an
overall result the first variant has the highest overall preference score in relation
to the remaining variants. The second variant has the lowest overall preference
score and the third variant ends up in between both other variants. Note that
scores of variants are not ’isolated’ but always relative to the other variants. This
is highlighted by the dashed lines.
To determine the (overall) aggregated preference score we use a software pack-
age called Tetra which is a solver rather than a ‘calculator’, and is based on
the aforementioned algorithm. For more information on the Tetra solver, see:
scientificmetrics.com or choicerobot.com. Those that want to know more
about the mathematical modeling errors of classical measurement theories are
referred to the book “Pure Economics” by Barzilai (2022). A more detailed de-
scription of the errors relating to preference modeling in engineering design can be
found in Barzilai (2006).
To explain the previous PFM-based preference aggregation and to show how
to apply these for multi-criteria evaluation purposes, four illustrative MCDA ex-
amples will be given below.

Example 1: Phone selection (MCDA)

We illustrate proper preference measurement and aggregation into one overall pref-
erence scale using two examples of two decision makers A and B facing a multi-
criteria phone selection problem. The first decision maker has a different set of
criteria, weights and ratings compared to the second decision maker. This under-
lines the subjective nature of preference measurement where ‘beauty is in the eye
of the beholder’.
We note that choice is synonymous to preference as we choose those objects
that we prefer. This is why in the following tables physical properties like screen
size and weight are translated into preference scores by means of linear interpol-
ation (i.e., to arrive at a common denominator which is preference). Recall that
preference is the only property of relevance in the context of utility theory and
game theory. Note that ‘unit price’ is not a physical property of an object but
rather relating to economics which is part of the social sciences (demand versus
supply as driving factors for the pricing of goods). In other words, price is not
related to the object but to affordability as determined by the human subject.
Also note that by linear interpolation we assume a linear relationship between an
object’s physical properties and relating preference scores. It is up to the decision
maker to decide whether or not this is a valid assumption or needs adjustment.
Tables 5.1 and 5.2 show the input for decision maker A and B respectively and
150 CHAPTER 5. MATHEMATICAL MODELING DESIGN & DECISION PROBLEMS

the output obtained by the Tetra solver. Decision maker B is not interested in
brand but is interested in privacy instead and also has a different set of weights
and ratings. Not surprisingly the output is different for decision maker A and B.

Table 5.1: Criteria, scores and weights for different phones (decision maker A).

Looks Price Brand Size Mass Overall

Criterion weight 40% 5% 45% 5% 5% score
Iphone 100 0 (€900) 100 50 (6.1”) 50 (175g) 100
Samsung 70 15 (€80) 30 100 (6.2”) 100 (160g) 56
Pinephone 0 100 (€200) 0 0 (5.95”) 0 (190g) 0

Table 5.2: Criteria, scores and weights for different phones (decision maker B).

Looks Cost Privacy Size Mass Overall

Criterion weight 5 % 40% 45% 5% 5% score
Iphone 100 0 (€900) 20 50 (6.1”) 50 (175g) 0
Samsung 70 15 (€80) 0 100 (6.2”) 100 (160g) 1
Pinephone 0 100 (€200) 100 0 (5.95”) 0 (190g) 100

Example 2: Parking garage (MCDA single-stakeholder)

We can illustrate the problems with aggregation of preference ratings using a
simple engineering decision making problem concerning four design variants of a
parking garage:
• Variant 1: two level, where the parking spaces are at a 45°angle.
• Variant 2: two level, where the parking spaces are at a 70°angle.
• Variant 3: three level, where the parking spaces are at a 45°angle.
• Variant 4: three level, where the parking spaces are at a 70°angle.
The decision maker takes three criteria into consideration: 1) functionality, 2)
environmental impact, and 3) cost. The scores for each alternative and the weights
attached to the criteria are shown in Table 5.3 including the overall preference score
according to Tetra and the weighted arithmetic mean. Note that on each criterion
the worst (0) and best (100) alternatives have been determined to define the scale
on which the remaining alternatives are scored and that the scores obtained by the
arithmetic mean are also re-scaled to the range 0-100 to make them comparable
to the Tetra outcomes.
As can be seen the aggregated scores according to the weighted arithmetic mean
are different from the scores obtained by the Tetra solver. We now remove variant
4 to illustrate the problem with the arithmetic mean. As this variant has the
5.1. MULTI-CRITERIA DECISION ANALYSIS & PREFERENCE FUNCTION MODELING 151

Table 5.3: Scores and weights for the parking garage decision making problem.

Variant 1 Variant 2 Variant 3 Variant 4 Weight

Functionality 100 0 20 85 40%
Environmental impact 0 100 45 60 10%
Cost 90 100 55 0 50%
Overall rating Tetra 100 52 4 0
Overall rating arithmetic mean 85 60 40 40
Overall rating re-scaled 100 44 0 0

same score as variant 3 according to the use of the arithmetic mean the scores
obtained by the arithmetic mean will remain unchanged. The removal of variant
4 does, however, have an effect on the overall preference ratings of the alternatives
according to the Tetra solver as can be seen in the revised Table 5.4. This is
because Tetra takes into account the relative position of each alternative to find
the aggregated preference score that reflects this new position.

Table 5.4: Scores and weights for the parking garage decision making problem.

Variant 1 Variant 2 Variant 3 Weight

Functionality 100 0 20 40%
Environmental impact 0 100 45 10%
Cost 90 100 55 50%
Overall rating Tetra 100 73 0
Overall rating arithmetic mean 85 60 40
Overall rating re-scaled 100 44 0

Example 3: Parking garage (MCDA multi-stakeholder)

In the previous example we considered a single decision maker taking three cri-
teria in consideration when making their decision. A rudimentary way of solving
multi-stakeholder problems is by considering each of the main criteria to belong
to a single stakeholder. In the case of the parking garage it could be that we
have three stakeholders where one is interested in the functionality, another in
the environmental impact and the third one in the cost. Note that the weight
distribution now expresses the power of each stakeholder. A more elegant way
of defining multi-stakeholder decision making problems is by using the top level
criteria for representing decision maker weights. The sub-criteria can then be used
by each decision maker to express their individual set of criteria (and sub-criteria).
This also allows for multiple decision makers expressing their preference for the
same criterion. We converted the parking garage problem to a multi-stakeholder
152 CHAPTER 5. MATHEMATICAL MODELING DESIGN & DECISION PROBLEMS

decision problem as shown in Table 5.5. For the sake of simplicity we use the same
set of criteria for each stakeholder and set the criteria weight to zero for those
criteria that a given stakeholder is not interested in.

Table 5.5: Scores and weights for the parking garage multi-stakeholder decision making problem.

Decision maker weight Criterion weight Variant 1 Variant 2 Variant 3

Functionality (20%) 100 0 20
Stakeholder A (50%) Env. impact (0%) 0 0 0
Cost (80%) 0 100 45
Functionality (15%) 100 0 55
Stakeholder B (50%) Env. impact (80%) 100 90 0
Cost (5%) 0 10 100
Overall rating Tetra 58 100 0

Example 4: Supermarket’s CSI (MCDA)

Multi-criteria decision making tools can also be used for determining an organiz-
ation’s socio-eco identity: i.e., corporate social identity (CSI) as an expression for
the socially responsible quality of service delivery that is perceived by a user/client
(see also Chapters 3 and 4). According to PFM principles, to mathematically cor-
rectly determine the CSI indicator of an organization mathematically correctly,
we must consider at least three organizations (see PFM principles in Section 5.1).
Moreover, these three organisations should be experienced through the eyes of at
least one and the same customer. Note that the traditional Corporate Social Re-
sponsibility (CSR) indicator is in most cases only viewed from the perspective of
one’s own organisation which is fundamentally flawed and thus these results are
meaningless showing no reliable results at all. It results in a large delta between
espoused theory and theory in action. The CSI indicator and its quantitative
approach, has been developed by Binnekamp and Wolfert (see Chapter 3 for the
qualitative origin of the CSI indicator).
As an CSI example, consider a customer that wants to identify the identity
of three Dutch ’brands’ of supermarkets: Odin, Jumbo and Ekoplaza. The cli-
ent distinguishes the overall socio-eco purpose threefold main criteria: economic,
isonomic and ecological performance indicators which are weighted equally. Each
criterion is subdivided into further sub-criteria which are also weighted equally. Fi-
nally each supermarket is scored on each sub-criterion. For the process of scoring
the client visited each supermarket to sample the products & services on offer and
also assessed how each organization operates in a broader societal perspective. All
in- and output scores are summarized in Tables 5.6 and 5.7, which also shows the
overall score of each brand of supermarket (as a result from Tetra). Note that both
5.1. MULTI-CRITERIA DECISION ANALYSIS & PREFERENCE FUNCTION MODELING 153

the sub-criteria, the values and the weights are indicative, generic and static to
show only the operation of the method here (in reality, a more thorough customer
study is needed as input accompanied by a dynamic sensitivity analysis).

Table 5.6: Supermarket socio-eco purpose input

Scores
Main criteria Weights Sub-criteria Weights
Odin Jumbo Ekoplaza
Quality experience by cus- 25% 80 0 100
tomer / customer satisfac-
Economic 33%
tion
Associative / co-maker re- 25% 100 0 50
lations that share pain
and gain
Future proof / fair pricing 25% 100 0 70
Lowest market price 25% 0 100 20
Collective decision making 25% 100 0 50
/ involvement employees
Isonomic 33%
and/or clients
Contracts from equality 25% 100 0 60
instead of self-interest
Equitable / fair working 25% 100 0 100
conditions
Vulnerability due to mar- 25% 0 100 50
ket / contract changes
Liberty / free develop- 25% 100 0 10
ment potential employees
Ecological 33%
Unconditional re-invest- 25% 100 0 50
ments / neutralize capital
True care for earth / (so- 25% 100 0 90
cial) environment
Relieving nature by using 25% 0 100 0
artificial organisms / ways
of farming

Table 5.7: Relative CSI ranking outcome for the supermarket’s CSI.

Alternative Solution
Odin 100
Jumbo 0
Ekoplaza 60
154 CHAPTER 5. MATHEMATICAL MODELING DESIGN & DECISION PROBLEMS

5.2. Single- & multi-objective design optimisation

In the previous section we viewed engineering design and management as an a-
posteriori decision-making processes to show that decision theory can play an
important role in their activities. Engineering design and management can also be
viewed as a process of a-priori modeling a single or multi-objective design or plan
resulting in a mapping from the design variant space to the design performance
attribute space. Note that a design is defined as a plan or scheme in the mind.
Subsequently, a utility function is constructed that reflects the designer’s (acting on
behalf of the decision maker) preference while considering trade-offs among system
attributes. As such the design process is goal-oriented aimed at maximising utility:
i.e., aggregated preference.
Where in the previous section a selection needed to be made between a set of
given (a-posteriori) design alternatives, the goal here is to generate these alternat-
ives (a-priori) and to be able to select the most optimal one. For the generation of
alternatives the utility function is used. Think of a designer going for the most sus-
tainable (minimise raw material use) or most profitable (maximise yield) design.
Note that the utility is inversely proportional to the material use (the less material
use, the higher the utility).
While a designer can manually (or sometimes ‘intuitively’) search for an op-
timal design, optimisation techniques could be used to make this search process
more efficient and effective. Mathematical single- or multi-objective design optim-
isation (SODO/MODO) models can be used for representing engineering design
problems because such problems can be modeled using goal-oriented systems. We
first introduce the general formulation of mathematical optimisation models, which
can be stated as follows:
U = f (X, Y ) (5.2)

The utility or value (U ) is a function of two types of variables: controlled (endogen-

ous) (X) and uncontrolled (exogenous) variables (Y ). An optimisation algorithm
is used to select the configuration of controllable (design) variables X that yields
the highest utility U whilst not violating the constraints. The system’s utility U is
defined by an objective function which needs to be maximised. This model can be
solved using different mathematical optimisation techniques. A gentle introduc-
tion into the application of the above optimisation framework for solving generic
making problems can be found in the work of Ackoff (1999).
The technique of linear programming, among other techniques, can be used for
solving mathematical optimisation problems. For the description of the general
mathematical model of linear programming, we will use the nomenclature and
the standard form adopted in the Operations Research (OR) textbook of Hillier,
Lieberman et al. (2006). This model is to select the values for the (design) variables
5.2. SINGLE- & MULTI-OBJECTIVE DESIGN OPTIMISATION 155

x1 , x2 , . . . , xn , so as to:

Maximise U = c1 x1 + c2 x2 + . . . + cn xn (5.3)

Subject to the inequality constraints:

a1,1 x1 + a1,2 x2 + & . . . + a1,n xn ≤ b1 . . . am,1 x1 + am,2 x2 + & . . . + am,n xn ≤ bm (5.4)

x1 ≥ 0; x2 ≥ 0; . . . xn ≥ 0 (5.5)
In this model the variables x1 , x2 , . . . , xn represent the controlled variables. To-
gether with coefficients c1 , c2 , . . . , cn they represent the objective function. Coef-
ficients a1,1 , . . . , am,n and b1 , b2 , . . . , bm represent the uncontrolled variables. Vari-
ables x1 , x2 , . . . , xn , coefficients a1,1 , . . . , am,n and b1 , b2 , . . . , bm together represent
the different constraints. Note that this is a linear problem setup since both the
objective and the constraint functions are linear. For an overview of how to apply
(non)-linear programming to other types of managerial optimisation problems we
suggest the work of Balakrishnan et al. (2017).
To explain the previous general mathematical formulation for a design problem
and show how to solve it, four illustrative example problems will be given below,
which can also be found on the Odesys Github, using the different standard Python
based solver types as explained in Appendix E. The first example is still a single-
objective managerial optimisation problem (SODO). However, the second problem
is a multi-objective design optimisation problem (MODO). The third example is
again a multi-objective design optimisation problem (MODO), but then applied
to a planning problem. For illustrative purposes, these first three problems have
been kept linear. Finally, the fourth problem is a non-linear multi-objective design
optimisation example.

Example 1: Computer production (SODO linear)

We start with a simple example of a company that produces two types of com-
puters, a basic computer and a more advanced computer. The basic computer
type requires one hard drive, the advanced type requires two hard drives. Each
produced basic computer type has a profit of $300 whereas an advanced computer
has a profit of $500. There are 60 cases in stock for the basic computer and 50
cases in stock for the advanced computer type. Finally there are 120 hard drives
in stock. The company wants to know how many computers of each type it should
produce to maximise profit (a typical managerial decision making problem).
The controllable variables x1 , x2 , . . . , xn are the numbers of computers produced
of each type. The profit needs to be maximised so the single objective function
becomes:
Maximise U = 300x1 + 500x2 (5.6)
156 CHAPTER 5. MATHEMATICAL MODELING DESIGN & DECISION PROBLEMS

The constraints relate to the number of cases and hard drives in stock:

x1 ≤ 60; x2 ≤ 50; x1 + 2x2 ≤ 120 (5.7)

Solving this single objective optimisation problem shows that producing 60

basic computers and 30 advanced computers results in the highest profit of $33
000. Note that within Python one can use the ‘Minimise’ algorithm to solve this
type of optimisation problems (both for linear and non-linear continuous object-
ive/constraint functions, where the Mixed Integer Linear Programming (MILP)
algorithm can be used for mixed integer and linear objective/constraint functions
extended with a Genetic Algorithm (GA) modification for non-linear mixed integer
cases).
We can also solve simple linear optimisation problems graphically. Figure 5.3
shows how the computer production problem can be solved in this way.

Figure 5.3: Graphical representation of the computer production problem.

Blue lines represent the constraints relating to the amount of cases and hard drives
in stock. All constraints determine the solution (or design) space which is the gray
area in the figure. If the constraints indeed define a solution space (no conflicting
constraints) the objective function is used to find the optimum. The lower orange
line shows the of the objective function. Its slope is determined by the coefficients
of the objective function (300/500). Depending on the nature of the problem the
objective function can be either maximised (e.g. profit) or minimised (e.g. costs).
In this case the profit needs to be maximised which means that the orange line
needs to be shifted up along the Y axis. The optimum is reached when the ob-
jective function can no longer be shifted upwards without violating the constraints
5.2. SINGLE- & MULTI-OBJECTIVE DESIGN OPTIMISATION 157

(leaving the solution space). The optimal solution is the coordinate i.e. combin-
ation of controllable variables found which is in this case 60 basic computers and
30 advanced computers. Note that a problem having three controllable variables
(x1 , x2 , x3 ) can still be represented graphically. In that case the constraints are
represented by planes that in turn define a 3-dimensional solution space. The ob-
jective function is also represented as a plane that needs to be shifted towards one
of the solution space’s corner points. A problem having more than three control-
lable variables can no longer be represented and solved graphically but still can be
solved mathematically.
This simple example shows how a managerial decision problem can be modeled
and solved using mathematical optimisation techniques. Mathematical optimisa-
tion models thus allow searching for the optimal solution to a decision making
problem. It relies on defining the controllable variables, the objective function
and the constraints that define the solution space. Should the solution space be
empty, then no solution can be found. All solutions within the solution space are
feasible, however, given the objective function, the most desirable solution can be
identified. The main question is whether the technique of mathematical optimisa-
tion using linear programming is also applicable to solving real life (engineering)
design problems.

Example 2: Bridge design (MODO linear & PDP)

Consider a bridge design where we limit the design problem to the determination of
the optimal span and the clearance height of the bridge. Assume that there are two
decision makers (stakeholders) that have conflicting interests: 1) the municipality
interested in the costs of the bridge, and 2) the waterway users interested in the
waiting time when the bridge is closed for waterway users. This can thus be
transformed in a so-called 2x2 MODO problem statement (i.e., 2 design variables
and 2 objectives). It serves as a base for a project delivery plan (PDP).
For this problem we have two controllable design variables, the bridge span x1
and the clearance height of the bridge x2 . The municipality wants the cost to be
minimised which becomes the first objective function. The costs are a function of
the material use which in turn is a function of both the span and clearance height.
We assume a linear relationship between costs O1 and material use F1 defined by
coefficient c1 = 3.

O1 = c1 F1 (5.8)
We also assume a linear relationship between material use and both span and
clearance height defined by coefficients c2 = 4 and c3 = 7 respectively.

F1 = c2 x1 + c3 x2 (5.9)
158 CHAPTER 5. MATHEMATICAL MODELING DESIGN & DECISION PROBLEMS

The first objective to be minimised then becomes:

O1 = c1 F1 = c1 c2 x1 + c1 c3 x2 (5.10)
The waterway users want the waiting time to be minimised which becomes the
second objective function. The waiting time is a function of the traffic flow which
in turn is a function of both the span and clearance height. We again assume a
linear relationship between waiting time O2 and traffic flow F2 , and reads as:
O2 = −c4 F2 + w0 ; F2 = c5 x1 + c6 x2 (5.11)
where w0 > 0. We also assume: (a) the coefficient c4 = 1.2 (b) a maximal waiting
time of w0 = 100 (for a traffic flow that is ’nearly’ zero); (c) a linear relationship
between traffic flow and both span and clearance height defined by coefficients
c5 = 1.7 and c6 = 1.9 respectively. The second objective to be minimised then
becomes:
O2 = −c4 F2 + w0 = −c4 c5 x1 − c4 c6 x2 + w0 (5.12)
The constraints relate to the minimum and maximum span and the minimum and
and maximum clearance height:
1 ≤ x1 ≤ 5; 3 ≤ x2 ≤ 8 (5.13)
An overall conceptual model of this linear 2x2 problem is shown in Figure 5.5.
The graphical representation of this problem and both optimal solutions are shown
in Figure 5.4. The SODO design points are (x1 , x2 ) = (1, 3) and (5, 8) for costs and
waiting time respectively. Note the difference with the previous example where we
now have two objective functions and two optima.

Figure 5.4: Graphical representation of the bridge design problem.

5.2. SINGLE- & MULTI-OBJECTIVE DESIGN OPTIMISATION 159

Figure 5.5: Conceptual model of the bridge design problem.

Example 3: Railroad maintenance plan (MODO linear &

SOP)
Consider a level crossing railroad maintenance problem where regular tamping
activities need to be carried out . We limit the problem to the determination of the
optimal number of tamping activities per service interval and the tamping length
(measured from the crossing). Assume that there are two decision makers (stake-
holders) that have conflicting interests: 1) the railroad user interested in travel
comfort when passing the crossing, i.e., the smoothness of the railroad track, and
2) the railroad operator interested in the crossing’s availability, i.e., the railroad’s
operational service time. This can thus be transformed in a so-called 2x2 MODO
problem statement (i.e., 2 design variables and 2 objectives). It serves as a base
for a service operations plan (SOP)

For this problem we have two controllable design variables, the number of
tamping activities x1 and the tamping length x2 . The railroad user wants the
comfort to be maximised which becomes the first objective function. The comfort
160 CHAPTER 5. MATHEMATICAL MODELING DESIGN & DECISION PROBLEMS

is a function of the number of tamping activities per service interval and the
tamping length. We assume a linear relationship between travel comfort O1 and
both the number of tamping activities and tamping length defined by coefficients
c1 = 50
21
and c2 = −5
21
respectively.
The first objective to be maximised then becomes:

O1 = c1 x1 + c2 x2 (5.14)

The railroad operator wants the availability to be maximised which becomes the
second objective function. The availability is also a function of the number of
tamping activities per service interval and tamping length. We again assume
a linear relationship between availability O2 and both the number of tamping
activities and tamping length defined by coefficients c3 and c4 . We also assume
a maximal availability of a0 = 80 (in case there are no tamping activities or
disturbances). The second objective to be maximised then becomes:

O2 = a0 − c3 x1 − c4 x2 (5.15)

The constraints relate to the minimal safety level tc = 70 and minimal availability
level av = 100:
O1 ≥ tc ; O2 ≥ av (5.16)
An overall conceptual model of this linear 2x2 problem is shown in Figure 5.7. The
graphical representation of this problem and both optimal solutions (for comfort
and availability) are shown as cornerpoint solutions in Figure 5.6.

Figure 5.6: Graphical representation of the railroad maintenance problem.

5.2. SINGLE- & MULTI-OBJECTIVE DESIGN OPTIMISATION 161

Figure 5.7: Conceptual model of the railroad maintenance problem.

The previous three examples contained only linear equations. In reality also
non-linear equations will apply. In the following example we show how to solve
problems containing non-linear equations.

Example 4: Building design (MODO non-linear)

Consider the design problem for a new building. We limit the problem to the
determination of the optimal dimensions of the building (length, breadth, height).
Let us assume that there are two decision makers (stakeholders) that have con-
flicting interests: 1) the project developer interested in the project’s profit, and 2)
the user interested in the building’s energy use. This project development prob-
lem can thus be transformed in a so-called 3x2 MODO problem statement (i.e.,
3 design variables and 2 objectives). For this problem we have three controllable
design variables, the building’s length x1 , breadth x2 and height x3 . The pro-
ject developer wants the profit to be maximised which becomes the first objective
function O1 . The profit is defined as the difference between the revenues and the
construction costs. The revenues are a function of the floor area while the costs are
162 CHAPTER 5. MATHEMATICAL MODELING DESIGN & DECISION PROBLEMS

a function of both the floor area and facade area. We assume a linear relationship
between revenues and floor area defined by a coefficient c1 = 55. We also assume
a linear relationship between facade area and costs defined by coefficient c2 = 3.5.
Finally we assume a linear relationship between floor area and costs defined by
coefficient c3 = 1.5. We assume the floor height to be 3 meter.
The first non-linear objective to be maximised then becomes:

O1 = c1 x1 x2 − 2c2 (x1 + x2 )x3 − c3 x1 x2 (5.17)

The user wants the energy use to be minimised which becomes the second objective
function O2 . The energy use is a function of the building’s volume. We assume
a linear relationship between the volume and energy use defined by a coefficient
c4 = 0.32.
The second non-linear objective to be minimised then becomes:

O2 = c4 x1 x2 x3 (5.18)

The building’s footprint cannot exceed 35 000 square meters (constraint):

x1 x2 ≤ 35000 (5.19)

The profit needs to be no less than 30 000 Euros (constraint):

c1 x1 x2 − 2c2 (x1 + x2 )x3 − c3 x1 x2 ≥ 30000 (5.20)

Solving this model shows that the design configuration x1 = 176.52, x2 =

198.27 and x3 = 3 yields the highest profit. Conversely, the design configuration
x1 = 80.0, x2 = 75.0 and x3 = 3.0 yields the lowest energy use. Note that by
definition non-linear optimisation can only arrive at a local optimum where linear
optimisation will arrive at a global optimum.

5.3. Conspection & curiosity

These MODO examples illustrate how mathematical optimisation techniques can
be used to solve engineering design problems. There are major limitations, how-
ever, that prevent optimisation to be used for solving group design optimisation
problems.
An important feature of optimisation models is that there is only one objective
function. In other words: it can only produce solutions optimised on one objective
to fully satisfy no more than one of only a single decision maker’s interests. There-
fore, this technique does not extend naturally to group decision making. When
there are multiple objectives or multiple decision-makers, each objective or de-
cision maker is associated with its own objective function. In that case, there are
5.3. CONSPECTION & CURIOSITY 163

as many optimisation models as there are objective functions. So, although this
technique helps decision-makers to find feasible design solutions, it does not help
them to select the most preferred solution from these. For this decision-makers
have to rely on negotiation. In other words, the math is lost. Moreover, the nego-
tiations will only involve compromise solutions as each solution fully satisfies only
one objective of one decision-maker (multiple single-criterion design solutions).
An approach to overcome this problem is to use the so-called constraint method
which operates by optimising one objective while all of the others are constrained
to some value. The use of the constraint method, however, is completely arbitrary
and still relies on unstructured negotiation.
Another common approach to address this problem is employ methods from
the domain of decision theory as described in the previous section to select from
the different design solutions the most preferred one. To elucidate this, we return
to the MODO example 2, the bridge design problem. In this case we now perform
an Multi Criteria Decision Analysis (MCDA) using the different single-objective
design optima (corner point solutions) as alternatives. Looking at Figure 5.4 we
can distinguish four corner points that represent different design configurations.
Each has its own properties and again using linear interpolation as used in the
phone selection problem we can determine preference scores for each. Using this
information we can use Tetra to determine the overall preference rating of each
corner point design solution. We assume that both objectives are equally weighted.
The resulting information is summarized in Table 5.8. As can be seen the corner
point solutions found by optimising on costs (corner point A) and the one found
by optimising on waiting time (corner point D) are outperformed by the design
configuration represented by corner point C. This makes sense as this design con-
figuration performs reasonably well on both costs and waiting time. Figure 5.4
also supports this conclusion as it graphically ’meets both stakeholders somewhere
in the middle’.
Table 5.8: Design configurations (corner points), objectives and overall scores for the bridge design
problem.

x1 x2 O 1 O2 Overall score
Corner point A 1 3 100 (€75k) 0 (91 seconds) 37
Corner point B 1 8 31 (€180k) 58 (80 seconds) 0
Corner point C 5 3 69 (€123k) 42 (83 seconds) 100
Corner point D 5 8 0 (€228k) 100 (72 seconds) 63

We can conclude that multi-criteria decision making can be performed math-

ematically correct using the new Preference Function Modeling (PFM) theory of
Barzilai. This is, however, limited to a-posteriori evaluation of defined (engineer-
ing design) alternatives. For supporting the a-priori multi-objective design process
164 CHAPTER 5. MATHEMATICAL MODELING DESIGN & DECISION PROBLEMS

we can make use of optimisation models that are, however, limited to producing
only compromise solutions that fully satisfy no more than one objective of one
stakeholder (limitations as seen in the examples of the previous sections). What
is needed is an a-priori methodology for finding the most preferred and feasible
design solution that represents the synthesis of all stakeholders’ interests instead of
having to choose between compromise solutions a-posteriori. This is the key topic
of the next Chapter where we construct this new Open Design Systems meth-
odology which we call Odesys and a new integrative maximisation of aggregated
preferences (IMAP) method, implemented in the Preferendus tool.
 Part II

Odesys methodology and

applications
This page intentionally left blank
Chapter 6

Socio-technical systems design & integra-

tion

Current systems design optimisation methodologies are one-sided, as these ignore

the dynamic interplay between people’s preferences (demand) and engineering as-
sets’ physical performance (supply). Moreover, classical multi-objective optimisa-
tion methods contain fundamental (aggregation) modeling errors and are not able
to reflect various socio-eco interests in one common preference domain. Therefore,
we introduce a new and state of the art open design systems (Odesys) methodology
in this Chapter. First, a sharp motivation for the need, or development gap, for
such a new integrative socio-technical design methodology will be described. This
will be followed by the Odesys’ development statement, its mathematical descrip-
tion, and a new threefold modeling framework linked with the open-ended Odesys
U-diagram.
As part of this Odesys methodology, a new IMAP (Integrative Maximised
Aggregated Preference) optimisation method is introduced for maximising aggreg-
ated preferences. This IMAP method forms the basis of a software tool called
the Preferendus and combines the state-of-the-art PFM principles with an inter-
generational genetic algorithm (GA) solver developed specifically for this purpose.
Odesys’ added value and use are demonstrated in Chapters hereafter (Chapters 7
and 8), both for specific components within formative examples and for summative
exemplars of real infrastructure design applications, showing how to achieve ”best
fit” for common design points. To make a comparison between Preferendus/IMAP
results and a conventional optimisation method which is among the few that does
not violate PFM principles, the Min-max goal attainment method is introduced
here as a comparative mathematical formulation.
The state-of-the art Odesys methodology and the new IMAP multi-objective
optimisation method, implemented in the Preferendus tool, have been developed
by Binnekamp, Van Heukelum, and Wolfert. Parts of the text are taken verbatim

167
168 CHAPTER 6. SOCIO-TECHNICAL SYSTEMS DESIGN & INTEGRATION

from the scientific paper Van Heukelum, Binnekamp and Wolfert (2023). It is noted
that the Preferendus in its primary form was published in Zhilyaev, Binnekamp
and Wolfert (2022).
Incitement 6.1 The impossible car design

Consider a person that wants to design: i.e., configure using existing knowledge, a new car
and is interested in the car’s fuel consumption and top speed. The person has stated their
(added) value or preference for two typical car design variables: i.e., their preference criteria
for fuel consumption and top speed of their future car. The person was asked to determine
the relation between these variables as depicted in the figure below.

This means that a car having a fuel consumption of 6 l/100km and a top speed of 250 km/h
is most preferred by this person (subject). Although such a car design is most desirable,
in real-life such a car is simply infeasible/incapable if one takes into account the physical
engineering properties of the car (object). This is because the laws of nature (natural
sciences) dictate that the fuel consumption and top speed are related to the engine size, see
the next figure.
The question now remains: how can this person arrive at a feasible design solution while
maximising their individual preferences? In this book we will show how such design/decision
problems can be solved using mathematical optimisation modeling where capability (physical
object behavior) and desirability (human subject values) are interconnected into an overarch-
ing design/decision support system to find the best fit for common purpose design/decision
solution.
6.1. ODESYS’ METHODOLOGY & SIGNIFICANCE 169

6.1. Odesys’ methodology & significance

Why, so often, do we build what nobody wants? Why, so often, do engineers
optimise their solutions based only on physical capabilities and fail to consider
the stakeholders’ desires? Why, so often, do policy makers and/ or infrastruc-
ture managers so often keep the design/decision-making process non-transparent
and non-participatory? Why, so often, do conflicts stem from failed attempts to
constructively design? The answer to these questions is that engineering design
and decision-making are often solved from a one-sided point of view, without
considering the fact that the problem is complex and multifaceted. Therefore, a
participatory process that does justice to both the ’hard’ technical and ’soft’ social
aspects of infrastructure systems development is needed. It is thus crucial to truly
connect and bridge the gap between human preference interests and the engineer-
ing assets performances using transparent models for complex systems design and
integration. The goal of such an Open Design Systems (Odesys) approach is to
promote the use of the civil infrastructures that surround us every day through
a multi-system level socio-technical approach, supported by sound mathematical
open-glass box models as means for observation and perception during collaborat-
ive decision-making.
Above all, zooming in on the design challenge of our contemporary (civil) infra-
structures, it can be noted that this challenge is becoming increasingly complex due
to the environmental demands, new transport modes, and other transitions. This
rapidly changing infrastructure context requires an optimal life-cycle value design
within the framework of infrastructure asset management (see e.g. Balzer (2016),
Hastings (2015) and/or the NEN/ISO 15288 systems life-cycle standard). Multi-
objective optimisation is key to supporting informed decisions in infrastructure
asset management (for an extensive literature review and overview of optimisation
methods, see Chen & Bai (2019)). Increasing stakeholder involvement, combined
with the multidisciplinary nature of infrastructure design challenges, further neces-
sitates a more effective and efficient participatory and supportive decision-making
process (see also Van Heukelum et al. (2023) for more references). In this con-
text of asset management decision-making, the focus of this book is therefore on
socio-technical design optimisation, where both the various stakeholder prefer-
ence interests (or societal values) and the technical system life-cycle capabilities
are unified in a best-fit for common purpose design configuration. To this end,
a new so-called Odesys methodology is introduced, with a new preference-based
multi-objective design optimisation method. This is required because the current
design optimisation methods have intrinsic problems and/or shortcomings that
make them unsuitable to provide the required unique and best-fit design solutions.
In the following sub-section we will summarise these in five fundamental prob-
lems and shortcomings which together constitute the Odesys’ development gap.
170 CHAPTER 6. SOCIO-TECHNICAL SYSTEMS DESIGN & INTEGRATION

Development gaps
The first problem with the current multi-objective design optimisation methodolo-
gies is the disconnect between the domain of human preferences (subject desirab-
ility) and the domain of the physical performance behaviour of the engineering
asset (object capability). Moreover, when applied in the classical systems engin-
eering context, design optimisation is usually limited to a single objective design
approach and/or to an a-posteriori evaluation of design alternatives Dym (2004),
Blanchard (2011) and/or Cross (2021). However, in a-posteriori evaluation, there
is no guarantee that the optimal design point has been found and a choice has to
be made between sub-optimal compromise solutions (even when optimisation and
a-posteriori evaluation are combined, see Mueller and Ochsendorf (2015)). Espe-
cially in complex engineering projects, the number of possible design alternatives
is too large to evaluate them all and the optimal solution may thus be ignored.
Secondly, most multi-objective optimisation methodologies introduce funda-
mental mathematical operation and aggregation flaws because they: 1) use un-
defined measurement scales and apply mathematical operations where these are
not defined (e.g. for variables that have neither an absolute zero nor one, such
as time/potential energy/preference, the mathematical operations of addition and
multiplication are not defined in the corresponding mathematical model which is
the one-dimensional affine space); 2) produce an infinite number of non-equivalent
‘optimal’ outcomes (e.g. the definition of the aggregation algorithm does not pre-
requisite having only normalised numbers); 3) outcomes do not take into account
the relative scoring impact of other design alternatives (e.g. in reality, the score
of one alternative depends on the performance of all the other alternatives; the
score is obtained by finding the best balance between the normalised and weighted
scores for all sub-criteria given the set of alternatives). As a result, the outcomes
of decision-making in engineering design may lead to sub-optimal design configur-
ations. The foundations of this second shortcoming are found from the principles
of Barziali’s Preference Function Modeling (PFM) and its associated preference
measurement theory, see Barzilai (2022, 2006 and/or 2005).
A third problem with many of the classical multi-objective design optimisation
methods is that they do not have a consistent way of translating the different ob-
jective functions into a common domain to find a best-fitting aggregated optimum.
To get around this problem, these multi-objective design methods often use mon-
etisation. In other words, all objective functions are expressed in terms of money.
However, according to classical decision/utility theory, decisions are not based on
money, but on value or preference (where minimising expenditure or maximising
profit can be one of the objectives). Here, preference is an expression of the de-
gree of ’satisfaction’, and it describes the utility or value that something provides.
Although some researchers have incorporated preference modeling into their multi-
6.1. ODESYS’ METHODOLOGY & SIGNIFICANCE 171

objective optimisation frameworks (see, for example, Lee et al. (2011) or Messac
(1996)), none of them use strong (preference) measurement scales or individually
weighted preference functions (i.e., continuous functions linking an individually
weighted preference to a specific objective). In addition, these approaches do not
lead to a single optimal design point and also contain the aggregation modeling
errors mentioned above.
A fourth shortcoming of classical multi-objective design optimisation methods
is that many of them consider the so-called Pareto front as a valid outcome Marler
(2004). Apart from the fact that the Pareto front is often obtained in a math-
ematically incorrect way (see the aforementioned second point), it also generates
an infinite set of possible, and supposedly equally desirable, design points, see e.g.
Farran (2015), Furuta et al. (2006) and Saad et al. (2018). However, this is
inconsistent with the fundamental basis of an engineering design process, where
each design point is (subjectively) interpreted by people in terms of preference
(i.e., a statement of their individual interest) and where a search is performed to
find a single optimal design solution. These Pareto shortcomings are also noted
by e.g. Kim et al. (2022), Lee et al. (2011), Bai et al. (2015), Golany et al.
(2006) and Bakhshipour et al. (2021), amongst others. However, their proposed
(hybrid) solutions still rely on the Pareto front (with its mathematical flaws) and
some form of a-posteriori evaluation. Their modeling approaches therefore fail to
provide a pure integrative design approach and are not able to obtain a-priori a
single best configuration.
A fifth shortcoming is that current multi-objective optimisation processes are
rather disconnected from systems design practices, as they lack deep involvement
of decision-making stakeholders, see e.g. Guo (2022). In addition, the dynamic
nature and the socio-technical interaction between stakeholder preferences (‘what
a human wants’) and the performance of technical assets (‘what a system can’)
are often not considered in service life design.

Development statement
To overcome the aforementioned shortcomings and problems, and to enable pure
human preference and asset performance systems design integration, the socio-
technical Open Design Systems (Odesys) design methodology is introduced in this
Chapter. In other words, the above summarizes the development gap that motiv-
ates Odesys’ development statement which reads as follows:

“There is a need for an open design methodology enabling socio-technical systems

integration on all relevant levels using a human centered preference based design
performance approach supported by a pure mathematical optimisation modeling.”
172 CHAPTER 6. SOCIO-TECHNICAL SYSTEMS DESIGN & INTEGRATION

As part of this development statement, we formulate the following Odesys prin-

ciples which follow from the perspective Chapters within Part I ‘Setting the scene’:
(#1) The maximum aggregate preference reflects the group’s well-being optimum,
since the fundamental laws & principles of all three social threefold realms are
maximally leveraged, and where aggregation is a solver algorithm rather than an
arithmetic operator (see Section 3.5);
(#2) Best fit for common purpose: i.e., the group’s well-being optimum, is the
best feasible synthesis or unity within the threefold of social-interests-desires/
technical-behavior-capabilities/ purpose-well-being-feasibility (see Section 2.4);
(#3) The design synthesis solution that is best uniting system capability and
human desirability is to be obtained by a supporting preference function based
model (PFM) searching for the group’s well-being optimum, involving a complex
set of relationships and feasible solutions, and (see Chapters 4 and 5);
(#4) Synthesize is a unification of re-generate and re-purpose, which is an in-
tegration of a logical act of reasoning/modeling to unlock the outer environment
(‘thinking slow’, deliberation), and intuitive thinking (dialogue) is an act of un-
covering the inner will (see Chapters 3 and 4);
(#5) Design is an open-ended systems integrating U-model based approach where
the technical, social and purpose cycles are incorporated (see Chapter 4).
Note: using typical expressions (at least following from the Dutch language), these
points can be summarized as a movement from ”every man for himself and God
bless the grip” towards ”common interest for the greater good based on individual
commitment and support for dynamic and social design”.

Methodology
Continuing, Odesys builds further on the multi-stakeholder design optimisation
methodology proposed by Zhilyaev, Binnekamp and Wolfert (2022), who showed
that the unambiguous solution to a multi-objective engineering design/decision
problem is to translate each of the objective functions, as a function of the design
variables, into an overarching preference domain. This can be done using stake-
holder preference functions: i.e., the relationship between an individual preference
and a specific objective, which then allow for the maximisation of the aggregated
group preference, leveraging Barzilai’s PFM theory (see Binnekamp (2010) where
this concept originated in its initial form, and Arkesteijn et al. (2017) for its early
social validation). However, all these aforementioned developments in the field of
preference-based design, which so far only were applied in the context of real estate
planning, still have three methodological deficiencies, and lack the following:
(#a) a generalised mathematical framework for multi-objective socio-technical
design optimisation: i.e., a threefold modeling framework of integrative perform-
ance, objective and preference functions;
6.1. ODESYS’ METHODOLOGY & SIGNIFICANCE 173

(#b) a connection between common socio-eco interests and the related subject
preferences, and the physical/mechanical object behaviour: i.e., a pure integration
of technical design performance, social objective and preference functions;
(#c) a PFM-based solver: i.e., a search algorithm to find the optimal solution with
the maximum aggregated preference;
(#d) an open-ended socio-technical process model that reflects a human centered
best fit for common purpose design: i.e., an open-ended spiral design system with
different open loops/cycles, expanding the U-model to achieve a design meta-
morphosis from picture via purpose to prototype.
Finally, the Odesys methodology will allow for the full integration between
subject (un)desirability: ‘what a stakeholder wants/does not want’, expressed via
preference functions, and object (in)capability: ‘what a system can/can not’, ex-
pressed via design performance functions. This integration is schematically depic-
ted in Figure 6.1. It is being achieved by constructing preference functions that
are a direct function of both the stakeholder objective and the engineering asset
design performance functions, which depend on the design and physical variables
and their constraints. In other words, this unified set of preference functions, which
at the lowest level is a function of the engineering design variables and the phys-
ical constraints, is a translation (a mapping) of the socio-technical system under
consideration. Next, an automated algorithm is needed that searches for a feasible
and optimal design synthesis solution where the aggregated group preference score
is maximal. In reality, this search is an open-ended approach. This means that an
iterative process of technical-, social-, and purpose-cycles will have to take place.
This implies that a best-fit for common purpose design configuration can only
be achieved through an iterative socio-technical process given the final ‘idealised’
desires, objectives, interests, and requirements of the stakeholders.
This makes Odesys a pure socio-technical systems integration methodology
where human preference-based design and engineering physics/mechanics con-
verge, offering a wide range of potential applications within the context of (in-
fra)structure systems engineering design. As part of this Odesys methodology, a
new Integrative Maximised Aggregated Preference (IMAP) optimisation method
for maximising aggregated preferences is introduced. This IMAP method forms
the basis of a new software tool called the Preferendus and combines the state-of-
the-art PFM principles with an inter-generational Genetic Algorithm (GA) solver
developed specifically for this purpose. Note: the Preferendus (inspired by and a
reference to the preferendum concept, a composition of the words preferences and
preferendum, and conceived by Wolfert) was developed to accommodate early and
transparent participation within an a-priori group design/decision making process,
see Zhilyaev et al. (2022) or Van Heukelum et al. (2023). The basis for group de-
cision making according to fundamental laws and principles of social threefolding
theory is found in Chapter 3.
174 CHAPTER 6. SOCIO-TECHNICAL SYSTEMS DESIGN & INTEGRATION

Figure 6.1: Socio-technical interplay between (un)desirability and (in)capability

Now we can continue to formulate a general mathematical statement of the

open design systems integration methodology. Next, a flow chart (or concept dia-
gram) of the Preferendus software tool is described in which the Odesys methodo-
logy is implemented. Finally, the use and added value of the Odesys methodology,
the IMAP optimisation method, and the Preferendus tool are demonstrated in
Chapters 7 and 8.

6.2. Odesys’ mathematical formulation

As described in the motivation, there is currently no optimisation framework that
allows for pure integration of the human preference domain (subject desirability)
and the engineering asset physical performance behavior domain (object capab-
ility). This disconnection will limit optimisation to sub-optimal results, as the
interaction between these two levels is not considered. To overcome this, the fol-
lowing mathematical statement is introduced, which integrates subject desirability
and object capability and searches for a feasible solution with the maximised ag-
gregated group preference which is the core of the Odesys methodology:
′
 
M aximise U = T Pk,i (Oi (F1 (x, y), F2 (x, y), ..., FJ (x, y))) , wk,i (6.1)
x

for:
k = 1, 2, ..., K
(6.2)
i = 1, 2, ..., I
6.2. ODESYS’ MATHEMATICAL FORMULATION 175

and subject to:

gp (Oi (F1,2,...,J (x, y)), F1,2,...,J (x, y)) ≤ 0 for p = 1, 2, ..., P (6.3)

hq (Oi (F1,2,...,J (x, y)), F1,2,...,J (x, y)) = 0 for q = 1, 2, ..., Q (6.4)

and with:
• T : The aggregated preference score determined using the PFM theory prin-
ciples (see Barzilai (2022)).
• Pk,i (Oi (F1,2,...,J (x, y))): Preference functions that describe the preference
stakeholder k has towards objective functions, which are functions of dif-
ferent design performance functions and dependent on design and physical
variables.
• Oi (F1,2,...,J (x, y)): Objective functions that describes the objective i, func-
tions of different design performance functions and dependent on design and
physical variables.
• F1,2,...,J (x, y): Design performance functions that describe the object, de-
pending on one or multiple design variables x (i.e., controllable endogenous
variables) and one or multiple physical variables y (i.e., uncontrollable exo-
genous variables).
• x: A vector containing the (controllable) design variables x1 , x2 , ..., xN . These
variables are bounded such that lbn ≤ xn ≤ ubn , where lbn is the lower bound,
ubn is the upper bound, and n = 1, 2, ..., N .
• y: A vector containing the (uncontrollable) physical variables y1 , y2 , ..., yM .
• wk,i
′
: Weights for each of the preference functions. These weights can be
broken down into weights for the stakeholders and weights for the objectives:
– wk : weights for stakeholders k = 1, 2, ..., K. These weights represent
the relative importance of stakeholders.
– wk,i : these weights represent the weight stakeholder k gives to objective
i.
′ ′
The
P ′ final P weights wP k,i can be constructed via wk,i = wk ∗ wk,i , given that
wk,i = wk,i = wk = 1
• gp (Oi (F1,2,...,J (x, y)), F1,2,...,J (x, y)): Inequality constraint functions, which
can be either objective function and/or design performance function con-
straints.
• hq (Oi (F1,2,...,J (x, y)), F1,2,...,J (x, y)): Equality constraint functions, which can
be either objective function and/or design performance function constraints.
To further elaborate on this formulation, several important remarks are made
which are discussed below.
176 CHAPTER 6. SOCIO-TECHNICAL SYSTEMS DESIGN & INTEGRATION

Remark 1: preference aggregation

Here, the aggregated preference scores are determined based on the principles of
PFM, expressed by the mathematical operator T . This operator is a solving al-
gorithm that is based on finding/synthesising the aggregated preference score (i.e.,
the ‘best’ fit of all weighted (relative) scores for all the decision-making stakehold-
ers’ objectives) that minimises the least-squares difference between this overall
preference score and each of the normalised individual scores (on all criteria) by
computing its closest counterpart, see Barzilai (2022) and/or Van Heukelum et al.
(2023). In this, preference is a statement of an individual stakeholder’s interest
and a measure of satisfaction, which is a score that is expressed as a real number
(scalar or bare quantity) on a defined scale, e.g. 0 to 100, where 0 corresponds
to the ‘worst’ performing alternative and 100 to the ‘best’ performing alternative.
For the applications shown in this book (Chapters 5-8), Tetra is used as this pref-
erence aggregation solver (i.e., operator T ). For more information on the Tetra
solver, see: scientificmetrics.com or choicerobot.com.
Remark 2: preference functions
Preference functions describe the relationship between an individual stakeholder’s
preference and a specific objective (where a stakeholder is defined as one of the
participants in the design/decision-making process). The theory of preference
functions (often also called utility functions) for a-posteriori multi-criteria de-
cision evaluation is a branch of the social science in itself. However, the preference
functions are needed as input to the design/decision system to enable a-priori
multi-objective design optimisation. Here, the elicitation of the preference func-
tions and associated weights is handled pragmatically using ‘static’ expert judge-
ment, whereas in practice this is inherently a dynamic and iterative process that
helps stakeholders better understand the impact of their input on the optimisation
outcome (see see Arkesteijn et al. (2017) for the specifics of this elicitation as part
of the design cycle). Finally, note that an objective Oi can be associated with mul-
tiple stakeholder preference functions Pk,i (as k ≥ i). However, it is not required
that a stakeholder expresses a preference for all objectives. This is modelled by
giving a stakeholder’s objective a weight of zero, which means that some elements
of the wk,i matrix can be zero.
Remark 3: bound preference scores
Here, a preference score is bounded by 0 ≤ Pk,i ≤ 100. A constraint can be added
to the objective functions to prevent preference scores which lay outside these
bounds.
Remark 4: design variables in objective functions
A design variable x can be directly linked to an objective function O. In this case,
the design performance function F is just equal to the design variable x. Moreover,
6.3. THREEFOLD MODELING FRAMEWORK & THE ODESYS’ U 177

these design performance functions F can also only relate to an exogenous physical
variable y.
Remark 5: rewrite equality constraints
Equality constraints are quite common in the object behaviour domain. However,
as the Preferendus uses a genetic algorithm (GA), equality constraints can com-
plicate the convergence of the optimisation, as especially the simpler constraint
handlers for GAs have problems with handling equality constraints, see e.g. Ho-
maifar et al. (1994). Therefore, when modeling a system of interest, the equality
constraints can be rewritten as inequality constraints, as is often done in literature,
see e.g. Coello (2002) and/or Kramer (2017). This is often done in the form of
Equation (6.5). For the proposed Odesys methodology, it is possible to rewrite
most equality constraints directly into inequality constraints, as the methodology
aims to reduce ‘waste’ in the result. For example, the length of a beam supporting
a floor will usually have a fixed length: the length of the span. Since a length
greater than the length of the span will result in more costs, material consump-
tion, carbon emissions, etc., this equality constraint can safely be rewritten as an
inequality constraint. This makes modeling easier, since the tolerance ϵ does not
have to be set and tuned for each problem.

|h1,2,...,M (O1,2,...,I (F1,2,...,J (x)), F1,2,...,J (x))| − ϵ ≤ 0 (6.5)

Remark 6: soft and hard constraints

Finally, a distinction can be made between soft and hard constraints. The former
result from the sociological aspect of a design process and are negotiable. They
can be adapted during the process based on discussions with other stakeholders or
new insights. The latter are fixed and non-negotiable. They are given by, among
others, laws of nature, material composition, environmental conditions, etc.

6.3. Threefold modeling framework & the Odesys’ U

The mathematical statement with the aforementioned remarks provides a general
framework in which it is possible to connect the subject desirability level (prefer-
ence functions) with the object capability level (design performance functions) via
the integrative subject-object conciliation level (objective functions). Note that
there will be three types of functional values and/or outcomes of interest: (1)
degrees of capability – design performances (technical) (2) degrees of freedom –
design variables (technical) (3) degrees of satisfaction – preferences and objectives
(social).
178 CHAPTER 6. SOCIO-TECHNICAL SYSTEMS DESIGN & INTEGRATION

Figure 6.2: Conceptual threefold modeling framework of the Odesys mathematical statement, where
desirability-subject (preference functions) and the capability-object (design performance functions)
are integrated subject-object (objective functions).
6.3. THREEFOLD MODELING FRAMEWORK & THE ODESYS’ U 179

To better understand and further detail this specific social-technical systems

integration, the different functions as part of the mathematical formulation are
conceptualised in a threefold modeling framework, as shown in Figure 6.2. Note
that the different functions are linked (an ordering principle) and that maximisa-
tion is not yet part of this threefold.
Now that we know the relationships between the different functions (prefer-
ence, objective and performance functions), we can set up the complete design
problem statement using the U-approach. The U-approach connects the technical
and social human design process, through a three-layer metamorphosis of picture-
purpose and prototype, see Chapter 4. In other words, the mathematical problem
formulation, including the maximisation of aggregated group preference, is now
translated into the U model, see Figure 6.3. Note that the cyclical activities re-
convert/re-validate/re-purpose are not present here (see Chapter 4 for the entire
Odesys U).

Figure 6.3: Odesys U-model representing the mathematical design/decision support modeling (as a
simplification of the U-model of Chapter 4, developed by Wolfert).

So here we then see the unique in its sort and state of the art threefold Odesys
U-model for the incorporating three open-ended design loops: (1) Open config –
technical cycle, (2) Open space - social cycle and (3) Open source - the purpose
180 CHAPTER 6. SOCIO-TECHNICAL SYSTEMS DESIGN & INTEGRATION

cycle (in contrast to similar classical engineering design systems, such as the V-
model, which often recognize only less than three subsystems without open loops).
Finally, using the full Odesys U and the open-ended spiral diagram from Chapter 4,
the three open design loops can be run cyclically to achieve a design metamorphosis
from picture via purpose to prototype. Note here the combination of both the
epistemological and the ontological origins of the U-modeling theory (see Chapter
1 and/or 3).

6.4. IMAP & the Preferendus

In this section, the so-called Preferendus tool and its optimization method, named
IMAP (Integrative Maximised Aggregated Preferences), are described as part
of the Odesys methodology. This IMAP method forms the basis of a software
tool Preferendus and combines the state-of-the-art PFM principles with an inter-
generational genetic algorithm (GA) solver developed specifically for this purpose.
Here the conceptual functioning of the Preferendus will be introduced, as an ex-
tension and further advancement of the Preferendus as first described by Zhilyaev,
Binnekamp and Wolfert (2022). As part of this Preferendus, a new IMAP op-
timization method is introduced for maximising aggregated preferences within a
socio-technical system, as schematically depicted in Figure 4.3 in Chapter 4. The
Preferendus combines proper preference aggregation with preference maximisation,
as described by the mathematical formulation of the Odesys problem statement of
the previous section. The state-of-the-art Preferendus applications will be demon-
strated using formative and summative design applications in the Chapters 7, 8.

Preference aggregation (IMAP part 1)

Following the Odesys methodology, it is argued that the overarching goal of multi-
objective design optimisation is to find the highest overall group preference score
that represents the design synthesis. However, for these design syntheses to be
possible, the individual preference scores first need to be aggregated.
Since preference scores are defined in an affine space, aggregation should also take
place in this space. This means that, according to the basic principles of PFM
theory, the correct way of aggregating preference scores is to find the aggregated
preference score that provides the ‘best’ fit to all the weighted (relative) scores
of the different preference functions (Pk,i ). Here, the preference functions are the
integration of objective functions and design performance functions. The final
preference score aggregation is performed by the aforementioned PFM-based solv-
ing approach (see remark 1 of the previous section), as an integral part of the
overall design optimisation algorithm.
6.4. IMAP & THE PREFERENDUS 181

Preference maximisation (IMAP part 2)

To finally find the design configuration that reflects the maximum group preference
aggregation, it is also necessary to use a maximisation algorithm. To do this, a GA
is used that is specifically adapted to work with Tetra. This is necessary because
it is not possible to directly compare one generation of the GA with another, as
the aggregated preference scores contain only information about the alternatives
of a single generation. To overcome this, a GA is developed that combines widely
available elements and is extended with a so-called inter-generational solver. The
details and the operation of this GA solver are given in Appendix C.

Figure 6.4: The workflow of the Preferendus, presented as a concept diagram.

The final result is an Odesys-based design optimisation tool, the Preferendus,

which incorporates the IMAP method. The concept diagram of the Preferendus
is shown in Figure 6.4. This is an open-source tool available via GitHub (see data
availability statement).

IMAP validation (Min-max goal attainment and SODO)

To compare and validate the results and added value of the IMAP multi-objective
optimisation method, the following section first compares the results with those
of the single-objective optimisation. In addition, a comparison is made with the
classical Min-max goal attainment multi-objective optimisation method (Marler
and Arora (2004)). This method does not generate group results based on overall
aggregation, but rather optimises: i.e., equalises, each individual result so that it
is as close as possible to a ‘utopian’ design point. In other words, the Min-max
method tries to minimise the maximum dissatisfaction for all individual scores
182 CHAPTER 6. SOCIO-TECHNICAL SYSTEMS DESIGN & INTEGRATION

(expressed by the distance to this utopia point). The result of this method, which
does not conflict with the fundamental PFM principles, is a solution that gratifies
each stakeholder equally.
In order to make a like-for-like comparison between IMAP and Min-max, the
mathematical formulation of the Odesys problem statement needs to be modified
(i.e., Equation (6.1) needs to be changed). First, this means that in this case the
Min-max method will try to minimise the distance to a score of 100 for all different
preference scores Pk,i (i.e., the best-scoring utopian point has been defined as 100).
Then, the preference score Pk,i with the greatest (weighted) dissatisfaction must
be found and minimised, which mathematically can be read as Equation (6.6).
 ′ 
M inimise U = max wk,i × {100 − Pk,i (Oi (F1 (x, y), F2 (x, y), ..., FJ (x, y)))}
x k,i
(6.6)
for:
k = 1, 2, ..., K
(6.7)
i = 1, 2, ..., I
It should be noted that the Min-max goal attainment method, as part of a larger
group of multi-objective optimisation methods, does not violate the PFM prin-
ciples. However, this method treats the scores of all design alternatives as abso-
lute values, ignoring the dynamic interplay between them. In other words, this
method focuses on making each stakeholder as ‘happy’ as possible, even though
this may not be beneficial for the group as a whole. This is why this optimisation
is called a compromise method, because it finds a design configuration based on a
compromise between stakeholders rather than a synthesis.
Chapter 7

Formative Odesys examples

In this Chapter, by means of a number of examples, the reader will be introduced

into the basic working Odesys methodology, the IMAP optimisation method, and
the Preferendus. These examples highlight the limitations of classical design/decision
making approaches that use mathematical optimisation models. The examples
show the novelty of the Odesys methodology and its possibilities versus the limit-
ations of the more traditional multi-objective approach from Chapter 5. We will
therefore start with a ”rerun” of one of the problems from Chapter 5, namely the
bridge design problem (revisited). Now, the integral IMAP/Preferendus is used
to show that it can arrive at the same solution, but a-priori and all at once. The
other examples, a shopping mall and a supermarket design, zoom in on the use
of different types of preference functions (non-linear and non-monotonic), showing
that the design points are able to move away from the corner points and can even
lie within the design space. The latter is new, especially compared to the clas-
sical methods that search the edges (and/or Pareto front) of the design space for
the best design point. In some of the examples a comparison with the Min-max
MODO (multi-objective design optimisation) or SODO (single-objective design
optimisation) methods, the corner point solutions of Chapter 5, is also made. In
Chapter 8, we will consistently make the comparison between SODO, Min-max,
and IMAP MODO methods. This Chapter however focuses on the location of the
optimal design point and the use of the preference functions.
The examples used in this chapter are educational in nature rather than an
actual representation of real-life design problems. We therefore call them formative
examples. However, the design applications in the following Chapter 8 demonstrate
the real-life value of the Odesys design/decision methodology and its application in
systems engineering design and management. Therefore, we call these summative
design applications. All of these can also be found on the Odesys Github.

183
184 CHAPTER 7. FORMATIVE ODESYS EXAMPLES

7.1. Bridge design (revisited)

The first example is the bridge design problem, which we already have seen in
Section 5.2, see Example 2 (MODO). This problem showed that the corner point
solution found by optimising on costs-only and the one found by optimising on
waiting time-only were outperformed by the design configuration represented by
another ‘intermediate’ corner point, see the conspection section 5.3 and Table 5.8.
This made sense as this design configuration performs reasonably well on both
costs and waiting time. When we use the Odesys methodology we expect to find
this most preferred and feasible design solution without the need for a-posterior
evaluation of corner point solutions (as done in Chapter 5).
As discussed in the previous Chapter 6, for proper a-priori optimisation, all
objectives should be translated to the preference (functions) domain. This allows
it to connect an outcome of the objective function (e.g. Objective costs of €5) to
a preference function score (e.g. 60). Let us first recall the objective functions for
this example:
O1 = c1 F1 = c1 c2 x1 + c1 c3 x2 (7.1)
O2 = −c4 F2 + w0 = −c4 c5 x1 − c4 c6 x2 + w0 (7.2)

where 1 ≤ x1 ≤ 5; 3 ≤ x2 ≤ 8; c1 = 3; c2 = 4; c3 = 7; c4 = 1.2; c5 = 1.7; c6 = 1.9.

For now, we limit ourselves to the construction of linear preference curves. This
means, the outcome of the objective that is most favourable will get a preference
score of 100, and the outcome of the objective that is least favourable will get
a preference score of 0. In between, the preference score will linear increase or
decrease.
Table 7.1: Minimum and maximum values for the two objectives from the bridge example.

Objective Minimum Maximum

Objective 1 75 228
Objective 2 71.5 108.9

Given the objective functions, we can find the minimum and maximum of them
by using a simple minimisation and maximisation algorithm (see Appendix E for
an overview of these algorithms). This will result in the outcomes as shown in
Table 7.1. For the construction of linear functions, one can use the equation as
shown in Equation 7.3. Here x1 and x2 are the minimum and maximum outcome
of the objective, and y1 and y2 are the corresponding preference scores. Now we
can construct the preference functions resulting in Equations 7.4 and 7.5.
y2 − y1
y = y1 + (x − x1 ) (7.3)
x2 − x1
7.1. BRIDGE DESIGN (REVISITED) 185

0 − 100 7600 100O1

P1 = 100 + (x − 75) = − (7.4)
228 − 75 51 153
0 − 100
P2 = 100 + (x − 71.56) = 465.849 − 5.11247O2 (7.5)
108.88 − 71.56

These preference functions are also plotted in Figure 7.1.

Figure 7.1: Preference functions for the bridge example.

Figure 7.2: Design space of the bridge problem, including IMAP design point

Finally, the new multi-objective optimisation method IMAP that uses the Prefer-
endus algorithm (instead of the minimise algorithm as in Chapter 5) indeed also
186 CHAPTER 7. FORMATIVE ODESYS EXAMPLES

finds this best solution (‘corner point C’) and in one time without the need for a-
posteriori evaluation. The result is shown in Figure 7.2 (here is the MODO design
point again equals point C: (x1,x2)=(5,3)). Note that we assume both objectives
equally weighted.

7.2. Shopping mall (linear & non-linear)

Consider a design/decision making problem for a new shopping mall. An investor
and municipality can choose between two types of shops with different properties
with respect to the profit, CO2 emissions and shopping potential (attractiveness),
see Table 7.2.

Table 7.2: Types of shops and their characteristics.

Shop type x1 Shop type x2

Profit [euro/m2] 160 80
CO2 emission [kg/m2] 120 30
Shopping potential [ppl/m2] 15 45

The total shopping area is limited by the municipality to 10 000 square meters.
No more than 5 000 square meters of shop type A are allowed and no more than
7 000 square meters of shop type B are allowed. Finally, the total amount of shops
needs to be at least 3 000 square meters. The investor wants to make as much
profit as possible whereas the municipality wants to minimise the CO2 emissions.
The existing shop owners want to make sure that the potential of the shopping
mall is maximised.

Design performance functions

In this case, the performance functions relate to the amount of shops type A
(variable x1 ) and type B (variable x2 ). Note that there are no physical performance
functions in this case, but only the design variables x1 and x2 (which are directly
related to the objectives). From consistency point of view we always set the design
performance functions first, then the objective functions and finally the preference
functions (see Chapter 8 for this rationale).

Objective functions
For the investor, the objective function is defined by the total profit given the
amount of shops type A and B. The investor wants the profit to be maximised.
The objective function for profit reads as:

O1 = 160x1 + 80x2 (7.6)

7.2. SHOPPING MALL (LINEAR & NON-LINEAR) 187

For the municipality, the objective function is defined by the total CO2 emission
given the amount of shops type A and B. The municipality wants the CO2 emissions
to be minimised. The objective function for CO2 emission reads as:

O2 = 120x1 + 30x2 (7.7)

For the existing shop owners, the objective function is defined by the total attract-
iveness (i.e., shopping potential) given the amount of shops type A and B. The
shop owners want the shopping potential to be maximised. The objective function
for shopping potential reads as:

O3 = 15x1 + 45x2 (7.8)

The constraints are the restrictions given by the municipality:

x1 ≤ 5 000; x2 ≤ 7 000; x1 + x2 ≤ 10 000; x1 + x2 ≥ 3 000 (7.9)

Preference functions (linear)

As explained in Chapter 6 and shown in the previous example, we need to translate
the objective functions into the preference domain. For this example, we will
limit ourselves to linear preference curves. To construct these, we first determine
the minimum and maximum outcomes of each objective. These can be found
in Table 7.3. By utilizing the linearisation Equation 7.3, we can construct the
preference functions, resulting in the following equations (which are also visualized
in Figure 7.4):

O1 − 240 000
P1 = (7.10)
9 600
O2 − 90 000
P2 = 100 − (7.11)
6 600
O3 − 45 000
P3 = (7.12)
3 150

Table 7.3: Minimum and maximum values for the three objectives from the shopping mall example.

Objective Minimum Maximum

Objective 1 240 000 1 200 000
Objective 2 90 000 750 000
Objective 3 45 000 360 000
188 CHAPTER 7. FORMATIVE ODESYS EXAMPLES

IMAP optimisation and design results (linear)

Now that we have the preference functions, we can optimise the shopping mall
problem a-priori using the methodology introduced in Chapter 6. This is done
here with both the MODO IMAP and the MODO Min-max methods. To properly
evaluate and compare the results of these two MODO methods, a third design point
should be added (as explained in Chapter 5). Therefore, a corner point is added
as a third (trivial) design option. Note that we calculated the relative aggregated
preference scores, which were used to determine the overall relative ranking, via
the PFM-based MCDA tool Tetra, and where the resulting aggregated preference
scores are re-scaled between scores of 0 and 100 (where 0 reflects the ‘worst’ scoring
configuration/alternative and 100 the ‘best’, see Appendix C for further details).

Table 7.4: Evaluation of design points and their relative ranking (based on aggregated preferences).

Method Variable 1 Variable 2 Relative preference score

IMAP 3 000 7 000 100
Min-max 1 400 7 000 55
Corner point (5 000, 5 000) 5 000 5 000 0

The relative ranking of the design points (the optimisation and/or corner point res-
ults) are shown in the Table 7.4. The last column contains the relative preference
scores of the three alternatives, showing that the IMAP method gives the solution
with the highest overall preference score, followed by the Min-max method, and
finally the selected corner point. The results of the optimisations are also shown
in the Figures 7.3 and 7.4. Note that the weights of the three preference functions
are all equal (i.e., wi = 1/3).
The IMAP design point moves toward the corner point (3 000, 7 000) due to the
linearity of the preference curves. Especially since both P1 and P3 (with a combined
weight of 2/3) prefer larger x1 and x2 , it is likely that the overall best-fit solution
lies at one of the corners of the solution space, since here the values for x1 and x2
are often highest. This is what we observe here. This is in contrast to the Min-
max design point. By its nature, the Min-max method searches for the design
point where each stakeholder’s dissatisfaction is minimal. When two preferences
are in conflict, as is the case here for the profit and CO 2 preferences, this will
lead to sub-optimal results for both preferences, rather than preferring one over
the other (where the IMAP method will prefer one over the other if it is better for
the group).
7.2. SHOPPING MALL (LINEAR & NON-LINEAR) 189

Figure 7.3: Design space for the linear shopping mall example, showing the MODO design points.

Figure 7.4: Preference curves for the lienar shopping mall example, including the MODO results.
190 CHAPTER 7. FORMATIVE ODESYS EXAMPLES

Preference functions (non-linear)

To show the difference between linear and nonlinear preference functions, we can
transform the functions of the previous section into nonlinear functions. It is
possible to do this transformation by hand, but to make it easier and more user-
friendly, we will use an interpolation function here (more specifically, we will use
the common PCHIP interpolation method). We can feed this function the combin-
ations of preference scores and objective function outcomes, and it will generate
all the intermediate points itself. The input for the interpolation function for the
shopping mall example is shown in Table 7.5. The resulting preference curves are
shown in Figure 7.6.

Table 7.5: Objective outcomes and there preference scores, as input for the interpolation function.

Objective 1 Objective 2 Objective 3

Outcome O1 Related P1 Outcome O2 Related P2 Outcome O3 Related P3
240 000 0 90 000 100 45 000 0
450 000 65 200 000 30 200 000 80
1 200 000 100 750 000 0 360 000 100

IMAP optimisation and design results (non-linear)

The results of the nonlinear preference curve optimisation are shown in the Figures
7.5 and 7.6. The weights of the preference functions were w1 = 0.25, w2 = 0.5,
and w3 = 0.25. When the preference curves are no longer linear and/or conflicting
objectives are more balanced in the weight distribution, it is no longer guaranteed
that the best-fit design solution lies at a corner point. This can be seen in this
example where the design point moved from a corner solution in the linear example
to a solution on the edge of the design space in the nonlinear example. Since the
preference functions are still monotonically decreasing or increasing, it is expected
that the solution will still be on one of the edges of the design space (as is the
case here). To move into the design space, non-monotonic preference curves are
needed. This will be shown in the next example.
7.2. SHOPPING MALL (LINEAR & NON-LINEAR) 191

Figure 7.5: Design space for the non-linear shopping mall example, showing the MODO IMAP design
point.

Figure 7.6: Preference curves for the non-linear shopping mall example, showing the MODO IMAP
result.
192 CHAPTER 7. FORMATIVE ODESYS EXAMPLES

7.3. Supermarket (non-linear & non-monotonic)

As shown in Chapter 5, the solution to a linear optimisation problem can be found
graphically. The objective function is a straight line and if you move this line
through the solution space, it will eventually reach a corner point where the value
for the objective function is at the highest point. For non-linear objective functions,
this is not that straight forward anymore. Here, the best possible outcome is not
necessary in a corner of the solution space but could even lay somewhere within
the solution space.
So far we only used monotonic preference functions. In real-life also ‘U’ and
inverted ‘U’ shapes might be a representation of the stakeholder’s preferences,
which are typical non-monotonic curves. Design/decision problems that only con-
tain monotonic preference curves are quite certain to result in optimal solution
points that are on one of the edges of the solution space. However, as soon as
non-monotonic functions are part of the problem this is no longer a certainty. As
we will see in this example, the introduction of inverted ‘U’ shapes indeed results
in solution points within the solution space.

Design performance functions

In this example, we investigate the configuration of a new supermarket. This
supermarket is located at a distance from the center of a neighborhood (design
variable x1 ) and has a certain size of the assortment (design variable x2 ). The
optimal distance and size of the assortment must be determined. Note that there
are no physical performance functions in this case, but only the design variables
x1 and x2 (which are directly related to the objectives). Based on a questionnaire,
the minimal distance to the supermarket must be 100 meters (to prevent noise
hindrance) but cannot be higher than 1 000 meters. The size of the assortment
of a supermarket can range from 800 different items for a very small and local
supermarket to 30 000 for a very large supermarket.

Objective functions
For this example, we consider two objective functions: shopping added value and
sustainability. The former is of interest of the owner of the supermarket, as it will
give an insight in the number of customers he can expect. The latter is of interest
for the municipality. The two objective functions are discussed separately below.
7.3. SUPERMARKET (NON-LINEAR & NON-MONOTONIC) 193

Shopping added value (location vs. assortment size) The first objective
is the shopping potential of the supermarket. This is depending on the relative
effort people must do to reach the supermarket in relation to the assortment size.
For this we first normalize the distance and assortment size:

x1 − 100
x1,norm = 1 − (7.13)
1 000 − 100
x2 − 800
x2,norm = (7.14)
30 000 − 800
For these normalized values we can construct a function that represents the in-
centive that people have to go to the shop. This incentive will not increase linearly
with the normalized scores, because there is an interaction between the two. To
reflect this, the normalized scores are combined via the root sum squared:
q
OSP = x21,norm + x22,norm (7.15)

Transport sustainability and waste There is an increasing demand for sus-

tainable shopping facilities. Sustainability relates to transportation, waste and
emission issues as follows:
1. The assortment is supplied by trucks. The higher the size, the more efficient
this transport can be, the lower the emissions are per item.
2. A high number of items in the assortment can cause waste. More items will
be thrown away and people buy stuff they might not need.
3. The larger the distance, the more interesting it is to take the car or scooter
instead of walking or cycling. This also contributes to emissions.
Based on this, we can construct an index function to express the relative sustain-
ability:
x2
OS = x1 (7.16)
20 000 − 400

Here, the assumption is that an assortment size of 20,000 items is most favorable.
This number is however influenced by the distance, as discussed above.

Preference functions
To find the proper balance for the shopping added value, the extreme outcomes
must be ’constrained’ (i.e. high and low x1,norm and low x2,norm respectively), be-
cause they have little incentive. For this, the interpolation function is used with
the values as displayed in Table 7.6.
194 CHAPTER 7. FORMATIVE ODESYS EXAMPLES

For the second objective, we again use the interpolation function to get to the
preference curve (see Table 7.6). Note that for both functions the resulting curves
are non-monotonic inverted ‘U’ functions.

Table 7.6: Objective outcomes and their preference scores, as input for the interpolation function.

Objective Added value Objective Sustainability

Outcome OSP Related PSP Outcome OS Related PS
0 0 0 0
1
√ 100 4 100
2 60 6 60

Plots of the resulting preference functions are shown in Figure 7.8.

IMAP optimisation and design results

For this decision-making problem, the weights are given in Table 7.7. We have
chosen to make shopping potential more important than sustainability. This is be-
cause without shopping sustainability matters not at all. The ratio is an arbitrary
one in this case.

Table 7.7: Weights per stakeholder for the supermarket example.

Stakeholder Weight
Shopping added value 0.65
Transport sustainability & wasted 0.35

Using the Preferendus and its a-priori IMAP optimisation method, the shop con-
figuration has a distance of 147 meters and an assortment size of around 11 370.
In Figure 7.7, this result is plotted in the solution space. For comparison, both op-
timal solutions via the IMAP and via the Min-max methods are plotted. Figure 7.8
shows the preference curves including the results. As can be seen, the solution lies
neither in a corner point nor on any of the edges of the design (solution) space.
This emphasizes the need for the application of non-linear solver algorithms, which
not only searches the edges but also within the design space.
Now we will evaluate the results of the different MODO methods, following the
same steps as in the previous shopping mall example (see Section 7.2). To prop-
erly evaluate and compare the results the number of design alternatives must be
increased to ≥ 3, and thus we added two corner point solutions for the relative
ranked design evaluation (again we use the PFM-based MCDA tool Tetra, where
100 and 0 reflect ’best’ and ’worst’ respectively). The additionally chosen corner
7.3. SUPERMARKET (NON-LINEAR & NON-MONOTONIC) 195

Figure 7.7: Design space for the supermarket example, showing the design points for the two MODO
methods.

Table 7.8: Evaluation of design points and their relative ranking (based on aggregated preferences).

Method x1 x2 Relative preference score

IMAP 147 11 370 100
Min-max 104 14 972 0
Corner point (100, 800) 100 800 51
Corner point (1 000, 30 000) 1 000 30 000 70

points are promising design configurations since they will result at least in a ’single’
preference score of 100 for P1 .
The final evaluation is shown in Table 7.8. We see that the IMAP method gives
the solution with the highest overall preference score, followed by the corner point
solutions. The Min-max design point gives the lowest overall preference score. We
conclude that the IMAP method gives the best-fit for common purpose design
point, which in this case lies within the design space (as also Min-max lies within
this space, but close to the edge).
Note that the results may differ for specific other preference curve and/or
weights distributions. This may result in an optimal design point that lies even
further within the design space (on a ’line’ between the ’best’ corner points). For
an example where both the Min-max and IMAP design points are both ’well’
within the design space, see the design application in Section 8.4.
196 CHAPTER 7. FORMATIVE ODESYS EXAMPLES

Figure 7.8: The preference curves for the supermarket example, including the results of the two
MODO methods.
Chapter 8

Summative Odesys applications

This chapter builds upon the Odesys methodology (see Chapter 6) from an engin-
eering asset management (EAM) viewpoint within a multi-stakeholder context. In
other words, this chapter deals with typical problems in which an engineering asset
manager has either needed to add new functionality or capacity to the engineering
asset base (to extend existing systems), or to operate his in-service engineering
assets including small/large maintenance, upgrades/renovations, and/or renewals.
These types of design applications are part of the so-called strategic asset
management plan (SAMP) of a service provider, an organization that must ensure
that the quality of service (QoS) is continuously guaranteed (see also Chapter 3).
It does this by developing new assets: i.e.,, new one-off service delivery, and by
maintaining and/or upgrading existing ones: i.e., ongoing service operations on-
the-run. The ‘rolling’ SAMP for a certain planning horizon therefore consists of a
so-called project development plan (PDP) and a service operations plan (SOP) (see
also Chapter 5 for a basic PDP bridge design problem and a SOP rail maintenance
problem). Within these plans, either optimal development or best operational
strategies are determined. The design applications that we examine in this chapter
will provide EAM examples for both existing and yet to be developed assets.
These SAMP activities require optimal decision-making, taking into account
the different interests of various stakeholders (e.g. project manager, maintenance
manager, environmental manager, user/client, etc.). These so-called design tY
aspects, such as availability, maintainability, comfort, etc. (see Chapter 4), form
the basis of such multi-objective design optimisation applications (MODO, see
Chapter 5) and necessitate a pure socio-technical design methodology where sub-
ject desirability (’what a stakeholder wants’) and object capability (’what a system
can do’) will meet. The Odesys methodology can be used to arrive at these best
fit for common purpose strategies while making use of the application potential of
the IMAP/Preferendus.

197
198 CHAPTER 8. SUMMATIVE ODESYS APPLICATIONS

To exemplify all of the aforementioned in an integral manner, four differ-

ent summative design applications (in contrast to the formative examples from
Chapter 7) will be demonstrated on the basis of the following real-life (civil) en-
gineering systems of interest:
DA-1 A capacity extension of a Norwegian light rail system.
DA-2 A new German high voltage power line.
DA-3 A service-life renewal of a Dutch rail level crossing.
DA-4 A new South Korean offshore floating wind farm.

All of these design applications (DAs) can be found on the Odesys Github. Before
we start dealing with these DAs in Sections 8.2-8.5, we first make some introduct-
ory notes here.

(# DA-1) Note that the first light rail application/system of interest is still without
integrating physical/mechanical design performance behavior (as opposed to the
other three examples, here only design variables and constraints are considered).
However, the focus is on a project management application in which some of
the stakeholder preference functions are non-monotonic. Therefore, a traditional
corner point method is not sufficient and the IMAP/Preferendus is applied. A
comparison is made with the as-built configuration and a classic corner point
solution, showing that the Preferendus solution is best fit for common purpose.
(# DA-2) Note that the design configuration for this power line application/ sys-
tem of interest is initially determined using the a-posteriori corner points method.
The integral IMAP/Preferendus is then used to show that it can arrive at the same
solution, but a-priori and all at once. This is also demonstrated within the frame-
work using a special type of design space which is discontinuous (i.e., lines instead
of a plane). The MODO Preferendus results are compared with both SODO- and
MODO Min-max methods and show that all methods arrive at the same best fit
for common purpose design.
(# DA-3) Note that the service life design model of the rail level crossing ap-
plication is an integral Odesys model in which the physical/mechanical perform-
ance functions are directly linked to the preference functions. A comparison of
the results of the IMAP/Preferendus with classical SODO and MODO Min-max
methods shows primarily that the Preferendus best fits a common purpose design.
This comparison is shown both graphically in the design space (two-dimensional)
as well as numerically using the preference scores. In addition, this example shows
that using the so-called Pareto front does not automatically lead to an optimal
socio-technical design.
(# DA-4) Note that the design planning model of the offshore wind farm applic-
ation is an integral Odesys model in which the physical/mechanical performance
functions are linked to the preference functions via, amongst others, a technical
 199

design constraint. A comparison of the results of the IMAP/Preferendus with

classical SODO and MODO Min-max methods shows that the Preferendus best
fits a common purpose design. This comparison can not be shown graphically
anymore since the design space is multi-dimensional. For this design application,
moving towards even better real-life modeling by zooming in further on the design
performance functions is the closest step compared to DA1-3 (see Van Heukelum
et al., 2023).

(# DA-1..4) We will structure the problem for all DAs by ‘running’ the Odesys
threefold diagram integrally. This will be achieved by constructing preference func-
tions that are a direct function of both the stakeholder objective and the engineer-
ing asset design performance functions, which depend on the design and physical
variables and their constraints. In other words, this unified set of preference func-
tions, which at the lowest level is a function of the engineering design variables and
the physical constraints, is a translation (a mapping) of the socio-technical system
under consideration. Next, an automated algorithm is needed that searches for a
feasible and optimal design synthesis solution where the aggregated group prefer-
ence score is maximal. Last but not least, to show the true potential of Odesys,
the IMAP/ Preferendus results are conspected within a broader design context.

Finally, for all DAs in this chapter, we only do this run for one social technical
cycle to arrive at a best fit for the common purpose solution. In real-life design
practices, as well as even within the educational context (see the ODL response
in Chapter 9), this quest is an open-ended approach (see Chapter 4). This means
that an iterative process of technical-, social-, and purpose-cycles will have to take
place, implying that a best fit for common purpose design configuration can only
be achieved through an iterative socio-technical process given the final ‘idealised’
desires, objectives, interests, and requirements of the participating stakeholders.
This culminates in the so-called Odesys U-modeling, as developed by Wolfert: i.e.,
the open config, open space, and open source design metamorphosis (see Chapters
4 and 6). We will therefore first describe this U-modeling approach and its open
design loops in general terms in the following Section 8.1, before showing only one
such cycle/loop for each DA in Sections 8.2-8.6. In this way, we will refer back
to and/or integrally connect to the main elementary principles from the previous
Chapters 3-6. We invite the reader to convert the current one-off DA solutions in
Sections 8.2-8.5 to an open-ended U-modeling approach.

A final note: parts of the text (i.e., design applications 3 and 4 in sections 8.4 and
8.5) are taken verbatim from the scientific paper Van Heukelum, Binnekamp and
Wolfert (2023).
200 CHAPTER 8. SUMMATIVE ODESYS APPLICATIONS

8.1. Open-ended Odesys’ U

The Odesys methodology, the associated IMAP optimisation method and the use
of the Preferendus are demonstrated in four real-life infrastructure design applic-
ations (DA): (1) a railway level-crossing life-cycle design and (2) a floating wind
turbine installation design. Both of these design cases were conducted within a
real-life infrastructure design context. Alle of these have been developed in col-
laboration with reflective practitioners and relevant stakeholders from (1) Skyss,
a Bergen light rail operator, (2) Tennet, a German transmission system oper-
ator (TSO), (3) ProRail, a Dutch railway infrastructure service provider, and
(4) Boskalis, an internationally operating maritime contractor. Especially within
Boskalis, several socio-technical cycles were carried out to validate the Odesys’
best fit for common purpose results and the added value of the Preferendus with
various stakeholders involved. In addition, here the Preferendus has also been
validated for dredging applications with promising validation results (but beyond
the scope of the current edition of this book).
Although all DAs are a somewhat simplified for illustrative purposes, they still
provide insight into the added value and principles of the Odesys methodology,
the IMAP method and the use of the Preferendus tool. For further substantial
extensions of two of the four design applications, as presented here, see Shang et.
al. (2023) and/or Van Heukelum et al. (2023). For all the DAs, the mathematical
threefold diagram of design performance, objective, and preference functions is
presented using the Odesys diagram. Here are the preference functions ultimately
a direct function of both stakeholder objective and engineering asset design per-
formance functions, which in turn are related to the design variables and their
constraints. These functions are derived from an idealised design configuration
(i.e., a tangible design representation) and from the common preference interests
of the stakeholders involved. The goal is then to find, within the feasibility space,
the candidate solution with the highest aggregated group preference score. In
reality, this quest for optimisation is actually open-ended.
This means that within an actual design there is an iterative process of tech-
nical, social and purpose cycles: i.e., from an idealized design, a best configura-
tion can be achieved through an iterative socio-technical process, given the final
idealised desires, goals, interests and requirements of the stakeholders, and given
the technical or physical constraints of the system. This is reflected in the form
of the open-ended Odesys’ U, incorporating three open-ended design loops: i.e., a
spiral design metamorphosis that contains three cycles: (1) Open config - technical
concept/concreation, (2) Open space -social context/conciliation and (3) Open
source - common purpose/synthesis. In this book, for demonstration purposes,
only one socio-technical best fit for purpose cycle per DA is included. It should
be noted that in the real-life DA-4 (’floating wind installation’), as carried out
8.2. NORWEGIAN LIGHT RAIL 201

within Boskalis, the stakeholders were asked to re-adjust both their open-config
and open-space parameters (from the social context) to achieve a better result.
This open-ended process was repeated several times for ‘idealised’ purposes. The
Preferendus has shown its added value here to arrive at the best-fit-for-common-
purpose design configuration, especially within multi-objective dredging and off-
shore planning applications, in combination with discrete event simulation (DES).
The Odesys combination of intuitive ‘U-thinking’ and deliberative ‘thinking-slow’
made the Preferendus an effective and transparent design/decision support tool
within Boskalis. Note that applying the U model in practice also shows that you
can complete a sub-cycle faster on partial aspects than the whole (e.g. a sensitiv-
ity or impact check of a single design parameter). You could call this, as it were,
”crossing over” from the left side to the right side, and then continuing the entire
U again. In short, a dynamic design and decision-making process.
Moreover, the social-technical cycle was also gone through and validated in a
real-life design application called Waelpolder, an area development project in NL
where the Preferndus was used. Together with the municipality of Naaldwijk, the
urban planning consultancy firm Planmaat and students from TU Delft, this pro-
ject was carried out (see van Eijck & Nannes TU Delft repository, 2022 , and section
8.6). With regards to the goal the stakeholders expressed that they preferred the
design obtained using the Preferendus method. The Min-nmax method optimisa-
tion results were deemed less satisfactory for the group as a whole. Although there
was a differentiation in stakeholder satisfaction when using the Preferendus, the
optimisation result was more diverse and attractive.
The open-ended Odesys methodology and modeling approach described above
is summarized below with the help of four essential diagrams, all described and
explained in more detail in the previous Chapters 4-6: (1) the full Odesys U-model
Figure 4.7, (2) the open design cycles/loops Figure 4.6, (3) the Odesys threefold
mathematical framework Figure 6.2, and (4) the mathematical design support U-
model Figure 6.3. We will use these four auxiliary diagrams to structure, model
and work through the DAs in the following sections.

8.2. Norwegian light rail

’Technical’ context: The Norwegian city Bergen wants to add a new section to its
light rail network. The hope is that the extension of the light rail will facilitate
more jobs and houses, making it an interesting investment for the municipality.
We assume that the decision for exact design of the route is not made yet and
that we want to model the design process. The route actually selected by the
municipality is shown in Figure 8.1. As a preliminary design exercise, a simplified
decision making problem was formulated first in which performance functions are
not included for the time being since the objective functions are directly linked to
202 CHAPTER 8. SUMMATIVE ODESYS APPLICATIONS

the design/decision variables (compare the supermarket/ shopping mall examples

from Chapter 7). This type of design problems are sometimes called managerial
decision making problem because technology and/or physics are not really included
(that is why it is actually social design rather than socio-technical one). However, it
is an interesting real-world application for a typical engineering asset management
(EAM) department. We will see the potential of IMAP multi-objective design
optimisation as preparation for the project development plan (PDP).
Social context: In this design application, a MODO approach for the decision
on the number of stations along the route and the number of trains per hour is
demonstrated based on different conflicting interests from multiple stakeholders:
i.e., the municipality is interested in the project’s development potential, the users
and inhabitants are interested in the travel time, the light rail operator is interested
in the operational costs and the project development organization is interested in
the construction time. These potentially conflicting interests, make this problem
a multi-stakeholder design problem containing, for example, the number of train
stops and the number of trains as important design variables.

Figure 8.1: Light Rail trace as realized.

We will first describe the integral problem by constructing the design perform-
ance, objective, and preference functions (we follow this order as motivated in the
previous section 8.1.)

Design performance functions

There are two main design/decision variables: the number of stops/stations along
the route (design variable x1 ) and the number of trains per hour (design variable
x2 ). Note that there are no physical performance functions in this application, but
only the design variables x1 and x2 , which are directly related to the objectives.
8.2. NORWEGIAN LIGHT RAIL 203

Objective functions
We start by formally defining (conflicting) objectives, bounds and constraints for
each relevant stakeholder. The objective functions read as follows.

Development potential is a key driver for the municipality which it wants to

maximise. This potential can be expressed in two ways:
1. The value of a property increases if it lies in the vicinity of a station. The
increase in value also means an increase in tax incomes for the municipality.
Secondly, this will lead to an increase in economic activity. Both will lead
to an assumed added value of €500 000 per station.
2. The number of trains per hour will influence the economic activity in the
surroundings. Less than 10 trains per hour will influence this negatively,
and more than 10 will be positive.
Both objectives are captured in the following objective:

OM = 500 000x1 − 25 000(−x2 + 10) (8.1)

Besides this, the municipality demands that the number of stops is at least 1, ex-
cluding the beginning and end stops. This stop is at Haukeland Hospital. Secondly,
to assert a minimal level of economic activity, the number of trains per hour should
be at least 2.
G1 = x1 ≥ 1; G2 = x2 ≥ 2 (8.2)
Travel time is a key value parameter for the light rail users. The shorter the
travel time is the better. The travel time is depending on a lot of variables (distance
between stops, acceleration length, deceleration length, minimal wait time), but
for simplicity, we assume here an average 1.5-minute travel time between stops.
The average travel time will secondly decrease fast by adding more trains on the
route.
1
OU = 1.5x1 + 60 (8.3)
x2
To assert good accessibility of the line, the minimal number of stations should be
3 according to the users. However, there must be no more than 10 stations, to
prevent excessive disturbance due to noise and vibrations.

G3 = 3 ≤ x1 ≤ 10 (8.4)
Maintenance costs will be the determining objective for the light rail operator.
From reflective practice, we can extract the costs per station, which are around
120 000 euros per year. This value is influenced by the number of trains per hour
since the track will wear more with an increased number of movements. The
120 000 euros is for 10 trains per hour and expected is that this number will
204 CHAPTER 8. SUMMATIVE ODESYS APPLICATIONS

decrease by a maximum of 10% for a decreasing number of trains per hour, and
vice versa.
x2 − 10
OO = 120 000x1 + 0.1 × 120 000 (8.5)
10
For profitability, the minimum number of stops must be 2. Otherwise, the line will
attract too few users. The number of trains cannot be higher than 20 per hour,
to allow for safe operation.
G4 = x1 ≥ 2; G5 = x2 ≤ 20 (8.6)
Construction time is the leading objective for the project organization. From
reflective practice, we can make a reasonable assumption for the time needed to
construct one station which is around half a year.
OP = 0.5x1 (8.7)
Since the route needs to be finished within 5 years, the number of stops cannot
exceed 10, assuming no parallel construction can take place.
G6 = x1 ≤ 10 (8.8)
Note that all constraints given in this section are determining the bounds of the
solution space:

3 ≤ x1 ≤ 10; 2 ≤ x2 ≤ 20 (8.9)

Preference functions
As discussed in Chapters 5 and 7, we distinguish two approaches for determining
preference functions. The first approach searches for the range of decision variable
values for each objective by means of maximisation and minimisation. Within
the second approach each stakeholder is asked for this range, regardless of the
feasibility of attaining this range. In this design application, we apply the latter
approach combined with curve fitting. The values used for this curve fitting are
given in Table 8.1. The resulting functions (i.e., relations between different values
P1..4,1..4 and O1..4 ) are shown as blue curves in Figure 8.2.
Note that the preference function for the income of the municipality is non-
monotonic. The municipality’s objective is not to profit on a project, but to
facilitate new projects. Making money is still important, but not the highest goal,
hence this preference function shape.
Similarly, the preference for the operational costs is non-monotonic. The given
objective function is a simplification, but in the underlying functions, quality of
material must be considered. The lower the quality, the higher the risk of sudden
breakdowns; i.e., low costs of replacement if failure is foreseen, but a large risk of
when breakages are not foreseen. Hence the lowest operational cost does not have
the highest preference.
8.2. NORWEGIAN LIGHT RAIL 205

Table 8.1: Preference points Bergen Light Rail.

Dev. potential Travel Time

P = 60 €5.25 × 106 P = 100 7.5 min
P = 100 €4.00 x 106 P = 80 20 min
P=0 €1.30 x 106 P = 10 35 min
P=0 40 min
Operational Costs Construction Time
P = 60 €350,400 P = 100 1.5 years
P = 100 €750,000 P = 95 2 years
P=0 €1.212,000 P=0 5 years

Design optimisation results & conspection

For this decision-making problem, and to generate the design points (i.e., design
configuration results) for this multi-objective optimisation problem, the weights
for each objective must first be determined. For this decision-making problem, the
weights are taken as w1,M = 0.2, w2,U = 0.4, w3,O = 0.3, w4,P = 0.1. Note that
for this example the weight distribution represents each decision maker’s power in
the design/decision making process.
We now first perform a-posteriori evaluation to determine what is the best
relative solution of the corner points using the Tetra software. If we evaluate these
alternatives, we get the outcome as displayed in Table 8.2.

Table 8.2: Alternatives for evaluation.

Alternative P1,M P2,U P3,O P4,P Score Rank

x1 = 3; x2 = 2 0.00 11.00 60.00 60.00 21 3
x1 = 3; x2 = 20 33.73 100.00 65.00 65.00 100 1
x1 = 10; x2 = 2 81.74 0.00 8.00 8.00 0 4
x1 = 10; x2 = 20 60.00 85.00 0.00 0.00 40 2

The multi-objective design optimisation problem can also be solved via the a-priori
IMAP optimisation method, using the Preferendus. The result of this optimisa-
tion, i.e., the best fitting design point for this decision-making problem, is 6 sta-
tions and 20 trains per hour. To evaluate this outcome and see how it compares
to other design points, we include both the real-life as-built solution (9 stations
and 12 trains per hour) and the a-posteriori corner point solutions. Note that, in
general, one needs at least three alternatives for such an overall evaluation.
The outcomes of the different design points/configurations are first of all plot-
ted on the different preference functions showing the different objective func-
tion values (O1..4 ) and their corresponding individual preference function values
206 CHAPTER 8. SUMMATIVE ODESYS APPLICATIONS

Table 8.3: Results of the objective functions (O1..4 ) and the corresponding preference functions
(P1..4,1..4 ) of the light rail design application.

Optimisation methods OM [€x106 ] P1,M OU [min] P2,U OO [€x106 ] P3,O OP [years] P4,P
x1 = 3; x2 = 2 1.3 0 34.5 11 0.35 60 1.5 100
x1 = 3; x2 = 20 1.75 34 7.5 100 0.37 60 1.5 100
x1 = 10; x2 = 2 4.80 82 45 0 1.19 8 5 0
x1 = 10; x2 = 20 5.25 60 18 85 1.21 0 5 0
As-built 4.55 91 18.5 83 1.08 45 4.5 24
MODO IMAP 3.25 94 12 96 0.73 100 3 76

Table 8.4: Evaluation of different design configurations per optimisation method and their relative
ranking (based on aggregated preference scores) for the sued-link design application.

Optimisation methods x1 [m] x2 Aggregated preference score

x1 = 3; x2 = 2 3 2 15
x1 = 3; x2 = 20 3 20 72
x1 = 10; x2 = 2 10 2 0
x1 = 10; x2 = 20 10 20 32
As-built 9 12 63
MODO IMAP 6 20 100

(P1..4,1..4 ), see Figure 8.2 and Table 8.3. Secondly, the numerical results of the
different design points/configurations per optimisation method (SODO and/or
MODO) can be read from Table 8.4. In this table one can also find the ag-
gregated preference score, which was used to determine the overall score/ranking
via the PFM-based MCDA tool Tetra, where the resulting aggregated preference
scores are re-scaled between scores of 0 and 100 (0 reflects the ‘worst’ scoring
configuration/alternative and 100 the ‘best’, see Appendix C for further details).

Finally, as there are only two design variables in this design application, the
two-dimensional design space (sometimes called solution space, see Dym & Little
(2004)) containing the different design points/configurations can be plotted, see
Figure 8.3. Note that the optimal outcome is not a corner point solution, because
some of the preference functions are non-linear/ non-monotonic. This illustrates
the pure added value of the a-priori optimisation Odesys methodology and its
IMAP/Preferendus method as illustrated in this book.
8.2. NORWEGIAN LIGHT RAIL 207

Figure 8.2: The four stakeholder preference functions (P1..4,1..4 ) for different objectives (O1..4 ) for
the light rail design application, including the results of the different optimisations. The numerical
results can be found in Table 8.3.

Figure 8.3: The design space of the light rail design application and the design configuration/points
for the different optimisation methods. The numerical results can be found in Table 8.4.
208 CHAPTER 8. SUMMATIVE ODESYS APPLICATIONS

The preference functions show that the objective of the municipality is in conflict
with the other objectives. In light of optimising for the best fit for common purpose
design point, it is then logical that the final outcome favor the stakeholders other
than the municipality, given that the weight (power) of the municipality is not
the highest of all the stakeholders. This is also what is expected in the real case.
A municipality’s goal is not to make the highest profit on a project, rather to
financially facilitate the new project so that societal goals can be achieved. The
as-built solution favors the municipality, where the IMAP/Preferendus solution
favours the common purpose for all stakeholders involved.

8.3. German power transmission line

Technical context: SuedLink is set to connect the offshore wind farms of the Ger-
man North Sea and Norway with the industrialized area of South Germany (see
Figure 8.4), enabling Germany to be closer to its goal to utilize 80% power from
renewable sources by 2050. In the project, HVDC was chosen over HVAC. DC
transmission lines have gained popularity for long distance transmission, however,
the technology is not as mature as AC which leads uncertainties and thus to higher
risks. Although in reality SuedLink will be built fully underground, in this example
we assume that the choice for AC/DC and the lengths of over/underground cables
has not been decided upon yet.
Social context: This application demonstrates a multi-objective design optimisa-
tion (MODO) approach for the decision on type of current (direct or alternating)
and the length of the underground cable is demonstrated based on different (con-
flicting) interests from the project organization’s view: i.e., the installation costs,
use of area and project duration. Take the German branch of Tennet, for example,
with a project delivery department. Both installation costs and project duration
objectives are primarily linked to this stakeholder organisation. The permitting
department with its objective of minimising area usage is presented as a second
stakeholder.

We will first describe the integrative design problem by running through the
Odesys threefold mathematical statement framework (Chapter 6), resulting in
design performance-, objective-, and preference functions.

Design performance functions

For this example we will use two design variables:
1. x1 : the type of current (DC or AC)
2. x2 : the length of underground cable (ACU or DCU).
8.3. GERMAN POWER TRANSMISSION LINE 209

Figure 8.4: SuedLink route.

With x2 and the overall length of 700km, we can also determine the length of
over ground cable. From these two design variables and the given overall length,
we can thus construct four different lengths as functions of both design variables:
1. Direct Current Underground, DCU
2. Alternating Current Underground, ACU
3. Direct Current Overground, DCO
4. Alternating Current Overground, ACO
These four variables thus indirectly represent the design variables and are used in
the objective functions. The route will pass some cities and waterways. At cities
it will need to pass underground, and at waterways it will mostly have to pass over
ground. These limitations constrain the problem:

300 ≤ x2 ≤ 600 (8.10)

Objective functions
As mentioned before, three (conflicting) objectives are investigated in this design
application: the installation costs, use of area and project duration.The objective
functions read as follows.

Installation costs for this project depend on the fixed cost of transformers,
etc., and the cost per kilometer. Literature (e.g. Meah ,2007) shows that the fixed
installation cost is lower for a HVAC line, but the cost per kilometer is higher.
Second, the cost for underground cable is higher because insulation is required
(with overhead cable, insulation is created by air). Next, the costs objective func-
210 CHAPTER 8. SUMMATIVE ODESYS APPLICATIONS

tion can be structured as follows:


0.475ACO + 0.580ACU + 375
OC = (8.11)
0.120DCO + 0.190DCU + 430

where the installation costs are in €×106 .

Local area for overhead high-voltage power lines is needed to place the large
masts in which the cables are suspended. This takes up much more space or local
area than an underground route where the cables can be laid virtually side by side.
To determine how much area we need, the number of conductors (i.e., individual
high-voltage lines) must be determined. These depend on the type of current and
the line voltage. For this system, the details are given in Table 8.5.

Table 8.5: Configuration of power lines.

Current type Line voltage [kV] # of conductors

AC 800 12
DC 525 8

Only the area directly below or above power lines is required. However, power
lines emit noise and magnetic flux, making the area unusable for buildings etc. In
this example, we will consider the noise component. The magnetic component is
perhaps even more important, but also quite extensive, making it difficult for this
design application. For describing the noise we use the principle assumptions from
Chartier (1981). In the case of AC, this results in the following equations:
3
!
X
(PWLi −11.4log(Ri ))/10
noiseAC = 10log 10 (8.12)
i=1

PWLi = −164.6 + 120log(e) + 55log(69.4) (8.13)

noiseDC = −133.4 + 86 ∗ log(e) + 40log(109.71) − 11.4log(R) (8.14)
where: e is the voltage gradient in kV/cm, and R is the radial distance between
the observer and the power lines in m.

The following numeric values are used: i.e., for (a) DC: e = 22kV /cm; (b) the
outer phase of AC: e = 13.66kV /cm; (c) the inner phase of AC: e = 14.58kV /cm.
For this example the AC and DC lines are located 12 meters above the ground
and the distance between the three phases in the AC case is 20 meters. The
maximal sound level is assumed to be 45 dB(A). With the equations above, we
can calculate the distance from the power lines where this sound level is reached.
8.3. GERMAN POWER TRANSMISSION LINE 211

For AC this is 287 meters, for DC 42 meters. These values need to be added to the
already needed clearance right below the power lines. This results in the following
objective function:

(0.170 + 0.287)ACO + 0.018ACU
OA = (8.15)
(0.120 + 0.042)DCO + 0.015DCU

Project duration is especially of importance for the transmission system op-

erator (TSO)/Tennet). The installation of underground cables is more intensive
than over ground cables. More groundwork is needed, and these cables also come
in shorter pieces at once. Secondly, there is a difference in conductors for the
different current types, resulting in different construction times. Since the prepar-
ation duration is considered equal for all type of currents and lengths of cables,
only the duration of installation is considered. This results in the following project
duration objective function (in days):

2.5ACO + 2.6ACU
OD = (8.16)
1.8DCO + 2.2DCU

Preference functions
To construct the preference curves, we define our preference for given a range of
decision variable values as inputted by each stakeholder. The outcome of this
process is shown in Table 8.6. The resulting functions (i.e., relations between
different values P1,1..3 and O1..3 ) are shown as blue curves in Figure 8.6. Note
that the preference function elicitation was performed using the fundamentals of
relevant preference functions research by Arkesteijn et al. (2017).

Costs Area use Project Duration

P = 100 €500 × 10 P = 100 20 km P = 100 1,300 days
6 2

P = 50 €600 × 106 P = 50 130 km2 P = 40 1,450 days

P=0 €800 × 106 P = 0 200 km2 P = 0 1,700 days

Table 8.6: Preference points SuedLink.

Finally, the problem statement of the systems design integration is conceptualised

with the threefold diagram as shown in Figure 8.5.

Design optimisation results & conspection

For this decision-making problem, and to generate the design points (i.e., design
configuration results) for the different multi-objective optimisation methods (MODO
Min-max and IMAP), the weights for each objective must first be determined. Here
212 CHAPTER 8. SUMMATIVE ODESYS APPLICATIONS

Figure 8.5: Conceptual threefold diagram, describing the systems design integration for the sued-link
design application. Note: the aim of this figure is to illustrate the relationship between the different
functions and some curves may not represent the actual function.

the weights are taken as w1,C = 0.4, w1,A = 0.2, and w1,D = 0.4. Both costs and
time are taken as important. Time is given this relative high priority due to the
incentive to move faster to renewable energy and the reduce the use of fossil fuels.
Note that we assume that for this case the objectives relate to one stakeholder.
8.3. GERMAN POWER TRANSMISSION LINE 213

We can construct a design space for the problem and evaluate the corner points
as a first optimisation strategy (a-posteriori evaluation). If we evaluate these design
alternatives using Tetra, we get the outcomes for the relative ranking as shown
in Table 8.7. Alternatively we can now use the IMAP/Preferendus as part of the
Odesys methodology. The result of this optimisation, i.e., the best fitting design
point for this decision-making problem, is HVDC an length of the underground
cable of 300km.
Alternative P1,C P1,A P1,D Score Rank
AC – 400 ACO – 300 ACU 9 9 6 0 4
AC – 100 ACO – 600 ACU 3 87 11 22 3
DC – 400 DCO – 300 DCU 80 81 52 100 1
DC – 100 DCO – 600 DCU 69 98 16 63 2

Table 8.7: Alternatives, scores and ranking.

To evaluate this outcome and see how it compares to other design points, we
include both the design point as obtained by the Min-max method (see Chapter
6) and the a-posteriori corner point solutions. Note that, in general, one needs at
least three alternatives for such an overall relative evaluation.
Table 8.8: Results of the objective functions (O1..3 ) and the corresponding preference functions
(P1,1..3 ) of the sued-link design application.

Optimisation methods OC [€] P1,C OA [km2 ] P1,A OD [days] P1,D

AC – 400 ACO – 300 ACU 739 9 188.21 9 1780 6
AC – 100 ACO – 600 ACU 770 3 56.50 87 1810 11
DC – 400 DCO – 300 DCU 535 80 69.15 81 1410 52
DC – 100 DCO – 600 DCU 556 69 25.16 98 1560 16
MODO Min-max 535 80 69.15 81 1410 52
MODO IMAP 535 80 69.15 81 1410 52

The outcomes of the different design points/configurations are first of all plotted
on the different preference functions showing the different objective function values
(O1..3 ) and their corresponding individual preference function values (P1,1..3 ), see
Figure 8.6 and Table 8.8. Secondly, the numerical results of the different design
points/configurations can be read from Table 8.9. In this table one can also find the
aggregated preference score, which was used to determine the overall score/ranking
via the PFM-based MCDA tool Tetra, where the resulting aggregated preference
scores are re-scaled between scores of 0 and 100 (0 reflects the ‘worst’ scoring
configuration/alternative and 100 the ‘best’, see Appendix C for further details).
Note that, in general, one needs at least three alternatives for such an overall
evaluation.
214 CHAPTER 8. SUMMATIVE ODESYS APPLICATIONS

Table 8.9: Evaluation of different design configurations per optimisation method and their relative
ranking (based on aggregated preference scores) for the sued-link design application.

Optimisation methods x1 [m] x2 Aggregated preference score

AC – 400 ACO – 300 ACU 1 300 0
AC – 100 ACO – 600 ACU 1 600 26
DC – 400 DCO – 300 DCU 0 300 100
DC – 100 DCO – 600 DCU 0 600 68
MODO Min-max 0 300 100
MODO IMAP 0 300 100

Figure 8.6: The three stakeholder preference functions (P1..3,1..3 ) for different objectives (O1..3 ) for
the sued-link design application, including the results of the different optimisations. The numerical
results can be found in Table 8.8.

As there are only two design variables in this design application, the two-dimensional
design space containing the different design points/configurations per optimisation
method can be plotted, see Figure 8.7.
8.3. GERMAN POWER TRANSMISSION LINE 215

Figure 8.7: The design space of the sued-link design application and the design configuration/points
for the different optimisation methods. The numerical results can be found in Table 8.9.

We can thus conclude that the direct evaluation of corner design points gives the
same result as via the IMAP/Preferendus approach. This example was therefore
chosen to show this, in addition to the peculiarity of a ’design space’ which, due to
its mixed integer nature, consists of only two lines. Because in a design problem
we do not know whether the corner point solutions are the maximum (see also
examples Chapter 7 or the light rail design application in section 8.2), the use
of the IMAP/Preferendus is essential, which we will see in the following design
applications. Finally, we note that the resulting design point via the Min-max
method is equal to the IMAP/Preferendus design point. It can also be concluded
that the objectives relating to the use of area and the costs of the line are aligned,
where the time objective is not. In other words, the area and costs objectives are
conflicting with the project duration objective. The time objective has a similar
weight as the costs objective, so it would be expected that time and costs would
have a similar final preference score. However, this is not the case. This indicates
that a decrease in time would not only mean a decrease in preference for the costs,
but also a significant decrease in use of area. Since costs and use of area together
have a higher weight than the project duration, it explains why time has a lower
final preference rating than the costs.
216 CHAPTER 8. SUMMATIVE ODESYS APPLICATIONS

8.4. Dutch rail level crossing

Technical context: Railways and roads often cross each other at level-crossings.
Because heavy vehicles must also be able to cross, the railway crossing is often cast
in a concrete foundation. The mechanical properties of this concrete foundation
are very different from the foundation of the other parts of the railway track. As
a result, transitional radiation occurs during the passage of a train, potentially
resulting in faster degradation of the local rail system or a negative passenger
experience due to vibrational hindrance, see Wolfert et al. (1998) and/or Metrikine
et al. (1998). Therefore, a transition zone is created by varying the number of
sleepers and the distance between them to contribute to a smoother transition,
which should have a positive effect on both operational performance and passenger
comfort.
Social context: In this application, a multi-objective design optimisation (MODO)
of the transition zone is demonstrated, based on several conflicting interests of
multiple stakeholders: i.e., (1) capital investment and (2) operational mainten-
ance expenditures, and (3) travel comfort objective functions. It is assumed that
these three objectives are linked to three different stakeholders. Take for instance
the Dutch ProRail organisation, where there is both a project delivery and a
service operations department. They are linked to the capital and operational
expenditure objectives, respectively. The Dutch train passenger is represented as
the stakeholder linked to the travel comfort objective.

We will first describe the integrative design problem by going through the Odesys
threefold mathematical statement framework (Chapter 6 and Figure 8.8), resulting
in design performance, objective, and preference functions.

Design performance functions

In reality, this design depends on a multitude of design variables, but for now, it
will be limited to just two of them:
1. F1 = x1 > 0: the distance between the sleepers. Sleepers are the concrete
(or sometimes wooden) beams that support the rails, as part of the ballast
bed.
2. F2 = x2 (≥ 1): the number of sleepers in the transition zone. The transition
zone consists of a different type of sleeper than the rest of the track.
Note that (1) in order to be consistent with the general mathematical statement
from section 1, the design performance functions F1 and F2 are added here, equal to
x1 and x2 respectively, and (2) from the practical application context, the design
variables are bounded by 0.3 ≤ x1 ≤ 0.7 and 4 ≤ x2 ≤ 15, which defines the
design space (i.e., the solution space defined by the design variables). The key
design performance functions describing the dynamic behaviour of the track at
8.4. DUTCH RAIL LEVEL CROSSING 217

the level-crossing transition zone are the force F3 = F (x1 , x2 ) and the acceler-
ation F4 = a(x1 , x2 ). These are usually the result of extensive numerical finite
element and/or analytical calculations. For this design application, the phys-
ical/mechanical relationships between the design variables are simplified by using
interpolation of discrete numerical calculations derived from a finite element based
structural dynamic model (Shang et al., 2023). These interpolated results are the
input to the design performance functions.

Objective functions
As mentioned before, three objective functions are investigated in this design ap-
plication: maintenance costs, travel comfort and investment costs. Given these
three objectives, the optimal design for the level-crossing zone is determined. The
objective functions read as follows.

Maintenance costs (OPEX) are the key driver for the design of the level cross-
ing transition zones. Large forces and accelerations will have a negative effect on
the degradation of the track and foundations, resulting in increased maintenance
costs. Hence, this objective can be written as a function of the force and acceler-
ation. For that purpose, the force and acceleration are normalised and combined
via the root sum of the square. The final maintenance costs per year objective
reads as:
q
OM = FN2 + a2N · 15 000 (8.17)
where
F − Fmin a − amin
FN = ; aN = (8.18)
Fmax − Fmin amax − amin
and where OM expresses the OPEX per year in EUR. Note that at the level of
design performance functions (i.e., capability-object level), it holds that F3 = F
and F4 = a respectively.

Passenger travel comfort is an important consideration in railway design.

When the dynamic behaviour (due to transition accelerations) during a passage
of a level-crossing is substantial, it may lead to a negative travel experience or,
in the worst case, to minor mishaps in the train (falling while walking, spilling
drinks, etc.). To integrate this into the design problem, an objective is added that
describes travel comfort as a function of the normalised acceleration:

O C = 1 − aN (8.19)
with aN as given in Equation 8.18.
218 CHAPTER 8. SUMMATIVE ODESYS APPLICATIONS

Investment costs (CAPEX) must be considered as a key decision-making

parameter. The installation of more sleepers will result in higher investment costs.
However, more sleepers spread out over a greater distance will also mean that
the investment costs for other parts of the rail will be reduced. Therefore, the
investment costs objective can be represented as follows:

OI = 1000x2 − 350x1 x2 (8.20)

where OI expresses the CAPEX in EUR.

Preference functions
The preference functions for this design application are constructed based on the
input from relevant stakeholders (Shang, 2021, 2023). The three resulting func-
tions, which describe the relations between different values for P1..3,1..3 and O1..3 ),
are shown as blue curves in Figure 8.9. Note that the preference function elicit-
ation was performed using the fundamentals of PFM research by (see Arkesteijn
et.al. (2017).

The systems design integration problem statement is now conceptualised with the
threefold diagram shown in Figure 8.8.

Design optimisation results & conspection

To generate the design points (i.e., design configuration results) for the different
multi-objective optimisation methods (MODO Min-max and IMAP), the weights
for each objective must first be determined. Since traditional (contractor) design
offices often give a dominant weight to investment costs alone and less to the qual-
ity of service (QoS) oriented interests of maintenance and travel performance, here
it is deliberately done ‘the other way round’, resulting in w1,M = 0.4 for mainten-
ance, w2,C = 0.4 for travel comfort and w3,I = 0.2 for investments. For evaluation
purposes, the design points for the different (1...3) Single-Objective Design Optim-
isations (SODO) are also determined for maintenance-, investment costs and travel
comfort respectively. The outcomes of the different design points/configurations
per optimisation method are first plotted in the preference functions showing the
different objective values (O1..3 ) and their corresponding individual preference val-
ues (P1..3,1..3 ), see Figure 8.9 and Table 8.10. Secondly, the numerical results of
the different design points/configurations per optimisation method (SODO and/or
MODO) can be read from Table 8.11. In this table one can find the aggregated
preference score, which was used to determine the overall score/ranking via the
PFM-based MCDA tool Tetra (the resulting aggregated preference scores are re-
scaled between scores of 0 and 100, where 0 reflects the ‘worst’ scoring configur-
ation/alternative and 100 the ‘best’, see Appendix C for further details). Note
8.4. DUTCH RAIL LEVEL CROSSING 219

Figure 8.8: Conceptual threefold diagram, describing the systems design integration for the rail level-
crossing design application. Note: the aim of this figure is to illustrate the relationship between the
different functions and some curves may not represent the actual function.

that at least three alternatives are needed for such an overall evaluation (e.g. one
reference configuration and two different MODO configurations).
Since there are only two design variables in this design application, we now
plot the two-dimensional design space (sometimes referred to as solution space, see
Dym & Little (2004)) containing the design points/configurations per optimisation
method, see Figure 8.10.
220 CHAPTER 8. SUMMATIVE ODESYS APPLICATIONS

Figure 8.9: The three stakeholder preference functions (P1..3,1..3 ) for different objectives (O1..3 )
for the level-crossing design application, including the results of the different optimisations. The
numerical results can be found in Table 8.10.

The following three conclusions can be drawn from these figures and table:
(#1) The IMAP configuration is either equal to or closest to the best result on
all single objectives (the SODO configurations). Only for the single-objective
investment costs, IMAP is second best, since it also aims to optimise the other
two objectives OM and OC . For these objectives, a low sleeper spacing (x1 ) is
expected, while the number of sleepers (x2 ) has a relatively small influence on the
outcome of these objectives. For this design application in particular, and given the
different objectives and associated stakeholder preferences, a low sleeper spacing
(x1 ) is expected to have a significant impact on objectives OM and OC , while the
number of sleepers (x2 ) will have a smaller impact. However, for objective OI ,
the influence of x2 will be significant, because for lower x2 the investment costs
decreases. Furthermore, the influence of x1 on OI is opposite to its influence on
the other two objectives. Therefore, the design configuration that is optimised for
8.4. DUTCH RAIL LEVEL CROSSING 221

Table 8.10: Results of the objective functions (O1..3 ) and the corresponding preference functions
(P1..3,1..3 ) of the level-crossing design application.

Optimisation methods OM [€] P1,M OC P2,C OI [€] P3,I

Single objective OM (SODO1) 3942 94 0.75 83 4319 76
Single objective OC (SODO2) 4297 90 0.76 84 4381 75
Single objective OI (SODO3) 18243 0 0.27 36 3020 100
MODO Min-max 4305 90 0.76 84 4382 75
MODO IMAP 3974 94 0.75 83 3466 91

investment costs only is not representative. A MODO optimisation is expected

to find the ideal balance for the sleeper spacing (x1 ), at lowest investment costs
with the number of sleepers on the lower bound (i.e., x2 = 4). The result of the
IMAP optimisation does indeed reflect this best fit-for-common-purpose balance.
As a result, IMAP may be characterised as a pure synthesis multi-objective design
method.
(#2) The IMAP configuration achieves better or equal individual preference func-
tion values (P1..3,1..3 ) and, more importantly, much better overall scores than the
MODO min-max method result. This is because, according to the Min-max prin-
ciple, this method will not be able to outperform the one objective score that shows
the maximum attainable minimum distance to 100 (i.e., the minimum dissatisfac-
tion). Thus, the min-max method inherently produces a sub-optimal compromise
design configuration which, depending on the specific input parameters, can at
best perform as well as the synthesis IMAP method. This limits the applicability
of the Min-max method as a real multi-objective design optimisation method.
(#3) From the design space figure it is seen that, perhaps counter-intuitively, both
the SODO 1 and 2 and the MODO min-max results fall within the design space
(x1 ; x2 equals 0.35/0.39 and 5 respectively) and that the MODO IMAP and SODO
3 results lie on the edge and in a corner point of the design space respectively. This
is because the set of design points that fall within the design space are the result
of optimising the ’technical’ design performance only. In other words, this means

Table 8.11: Evaluation of different design configurations per optimisation method and their relative
ranking (based on aggregated preference scores) for the level-crossing design application.

Optimisation methods x1 [m] x2 Aggregated preference score

Single objective OM (SODO1) 0.39 5 84
Single objective OC (SODO2) 0.35 5 81
Single objective OI (SODO3) 0.70 4 0
MODO Min-max 0.35 5 81
MODO IMAP 0.38 4 100
222 CHAPTER 8. SUMMATIVE ODESYS APPLICATIONS

Figure 8.10: The design space of the level-crossing design application and the design configura-
tion/points for the different optimisation methods. The numerical results can be found in Table 8.11.

that these optimal solutions move to an optimum only within the feasibility space
(i.e., a solution space defined by the physical engineering variables only, and which
is a subset of the design space) and lie on the ’classical’ Pareto front. Note that in
this case a possible Pareto front, which defines an edge of the feasibility space as a
function of F and a, results only from the minimisation of OM and OC . Despite the
fact that SODO 3 actually does find the edges of the design space (corner point),
it still scores low overall because it is by far the lowest on the other two objectives
(1 and 2). MODO IMAP gives the overall best design point on the edge of the
design space (x1 and/or x2 equals 0.38 and/or 4 respectively), and can therefore
be considered the pure best fit-for-common-purpose design point.
Note that when the emphasis in the design application is on optimising the integ-
rated socio-technical problem, the overall best configuration will be found within
and/or on the edge of the design space. When optimising solely on cost or tech-
nics, one can either end up at the classical Pareto front or in a corner point of the
design space (see also the next design application in Section 8.5).

8.5. South Korean floating wind farm

Technical context: A promising solution for wind energy production in deep waters
(e.g., within the Korea Strait which is a sea passage between South Korea and
Japan, connecting the Yellow Sea and the Sea of Japan sea) could be the use
8.5. SOUTH KOREAN FLOATING WIND FARM 223

of floating wind turbines (FWT). Rather than being placed on a fixed monopile,
these turbines are placed on a platform moored to the seabed by anchors. The
floating wind farm considered in this design application consists of 36 FWTs and
108 suction anchors (i.e., 3 anchors per FWT).
Social context: This application illustrates a MODO approach for the installa-
tion of multiple FWTs, taking into account several conflicting interests of multiple
stakeholders: i.e., (1) project duration, (2) installation costs, (3) fleet utilisation,
and (4) CO2 emissions. Given these four overall interests, an energy service pro-
vider (stakeholder one, e.g. Shell) requires a marine contractor (stakeholder two,
e.g. Boskalis) to determine the optimal installation design plan. While cost re-
mains a significant factor in the offshore industry, the energy service provider’s
primary concern lies in minimising delivery time to expedite resource income gen-
eration. Secondly, the energy service provider will have an interest in reducing
the CO2 emissions of the project, as this will benefit its carbon footprint and the
societal acceptance of the project. The marine contractor’s primary focus will be
on reducing the costs, as this will make it more competitive. Secondly, the fleet
management department may express a preference for optimising fleet utilization
to maximise operational efficiency.

We will first describe the integrative design problem by working through the
Odesys threefold mathematical statement framework (Chapter 6, and Figure 8.11),
resulting in design performance-, objective-, and preference functions.

Design performance functions

Several types of vessels are available for the installation of the FWTs and their
suction anchors. The amounts of vessels used in the project are the initial three
design variables:
1. F1 = x1 (0 ≤ x1 ≤ 3): small offshore construction vessels (OCV), capable of
carrying up to 8 anchors.
2. F2 = x2 (0 ≤ x2 ≤ 2): large offshore construction vessels, capable of carrying
up to 12 anchors.
3. F3 = x2 (0 ≤ x2 ≤ 2): self-propelled crane barges, capable of carrying up to
16 anchors.
Note that the lower bound of these three design variables is equal to zero. There-
fore, a design performance constraint is required to ensure that the sum of all
vessels on the project is greater than one (reflecting that at least one vessel is
required):
g1 = −(F1 + F2 + F3 ) + 1 ≤ 0 (8.21)
This design application also considers the design of the anchors themselves. To do
224 CHAPTER 8. SUMMATIVE ODESYS APPLICATIONS

this, design performance functions are defined that describe: (1) the resistance of
the anchor to the forces acting on it, and (2) the amplitude of the forces acting
on the anchor. The resistance of the anchors considered in this design application
can be estimated using analytical design calculations according to Arany(2018;
Houlsby (2005); Randolph (2017). These calculations usually depend on several
design variables, only two of which are considered here:
1. F4 = x4 (> 0): Diameter of the suction anchor in meters.
2. F5 = x5 (> 0): Penetration length of the suction anchor in meters.
For practical reasons, these variables are bounded by 1.5m ≤ x4 ≤ 4m and
2m ≤ x5 ≤ 8m. The other design variables are uncontrollable variables y in this
design application, where y = [working point Fa , mooring configuration, anchor
type, soil conditions, mooring line properties]. Consequently, the anchor resistance
can be mathematically formulated as F6 = Ra (x4 , x5 , y). The soil is assumed to
be clay with an undrained shear strength of su = 60 kPa and a submerged weight
of γ ′ = 9 kN/m3 . The coefficient of friction between the anchor shaft and the soil
is α = 0.64. The mooring line consists entirely of a chain with a nominal diameter
of 240 mm. This chain is attached to the anchor at a depth of 0.5 times the pen-
etration length. Furthermore, the coefficient of friction between the seabed and
the chain is taken as µ = 0.25 and the active bearing area coefficient AWB = 2.5.
While anchor resistance can be determined by analytical calculations, the forces
acting on the anchor can not be determined in the same manner. This is due to
their dependence on various variables such as platform type, mooring line charac-
teristics, pre-tension, and/or anchor radius. To obtain accurate normative forces,
numerous numerical time-domain calculations must be performed, as outlined in
DNV (2021). These calculations are beyond the scope of this paper. Instead,
the relevant design variables are considered as uncontrollable physical variables y,
resulting in the following (assumed) force on the anchors: F7 = Fa (y) = 3.8M N ,
where y = [platform type, mooring line characteristics, pre-tension, mooring line
length, anchor radius].
The two design performance functions F6 and F7 are related through a design
performance constraint. This constraint describes (part of) the feasibility space of
the ’technical’ design by defining the boundary where the resistance of the anchor
is larger than or equal to the force on the anchor:

g2 = F7 (y) − F6 (x4 , x5 , y) = Fa − Ra ≤ 0 (8.22)

Objective functions
As mentioned before, four objectives are investigated in this design application:
project duration, installation costs, fleet utilisation, and CO2 emissions. Given
these four objectives, the optimal design plan for installing the FWTs is determ-
8.5. SOUTH KOREAN FLOATING WIND FARM 225

ined. The objective functions read as follows.

Project duration depends on the number of vessels involved in the project, their
deck capacity and the speed at which they can install anchors, which is assumed
to be one anchor/day/vessel. In addition, after all the anchors on board have been
installed, the vessels will have to load new anchors. This process takes 1.5 days for
the small OCV, 2 days for the large OCV, and 2.5 days for the barge. To obtain
the overall project duration, a discrete event simulation (DES) was incorporated
into the model, which depends on the type and number of vessels (i.e., x1 ..x3 ).
See the data availability statement for the code of the DES. In conclusion, the
objective function for the project duration can be expressed as follows:

OP D = f (x1 , x2 , x3 ) (8.23)
where f is the DES and OP D is expressed in days.

Installation costs of this project depends on two components: (a) the day rates
of the vessels, and (b) the cost of the anchors. The following theoretical day rates
R are assumed: (1) Small OCV (x1 ): R1 = €47,000/day; (2) Large OCV (x2 ):
R2 = €55,000/day; (3) Barge (x3 ): R3 = €35,000/day.
The cost per anchor can be divided into a fixed part (€40,000/anchor) and a
variable part, where the variable part depends on the material costs (€815/t).
This results in the following objective cost function:
3
X
OC = (815Ma + 40, 000)na + xi ti Ri (8.24)
i=1

where OC is expressed in EUR, na is the number of anchors (i.e., na = 108), ti the

time a vessel is needed (result from the DES), and Ma the mass of the anchors,
which is defined as:
 π 
Ma = πx5 x4 t + x24 t Wsteel (8.25)
4
with Wsteel is the weight of steel, assumed as 78.5 mt.

Fleet utilisation is a key driver for a maritime contractor and describes the
extent to which its vessels are (optimally) utilized. Consequently, this objective
focuses on evaluating the probability of a vessel being better utilized in another
project (e.g. specialised vessels are preferred to multi-purpose vessels). For this
purpose, the following values are assumed::
1. Small OCV (x1 ): p1 = 0.7
2. Large OCV (x2 ): p2 = 0.8
3. Barge (x3 ): p3 = 0.5
226 CHAPTER 8. SUMMATIVE ODESYS APPLICATIONS

The fleet utilisation objective is then defined as:

3
Y
OF = pxi i (8.26)
i=1

where OF is expressed as the combined chance with a value between [0, 1].

CO2 emissions is one of the sustainability aspects, and is becoming an increas-

ingly important aspect within offshore (wind) project development. Most of the
emissions will be generated by the vessels, for which the following theoretical av-
erage emission rates are assumed:
1. Small OCV (x1 ): E1 = 30 t/day
2. Large OCV (x2 ): E2 = 40 t/day
3. Barge (x3 ): E3 = 35 t/day
As other sources of emissions are neglected, the emission objective is defined as:
3
X
OS = xi Ei ti (8.27)
i=1

here OS is expressed in t (’toness’) and with ti the time a vessel is needed (result
from the DES).
Note that the Odesys mathematical statement allows for the direct integration
of design performance and objective functions. However, in certain cases, design
performance functions will not only directly link to the objective functions but can
also connect through (in)equality design performance constraints. This indirect
linking is common in design problems where, for example, force constraints play
an important role. In such cases, these constraints define the feasibility space, and
together with directly linked design performance functions, they span the design
(solution) space.

Preference functions
The preference functions for this design application were developed with floating
wind project experts within Boskalis, based on the input from an energy service
provider. The four resulting functions, which describe the relations between dif-
ferent values for P1..2,1..4 and O1..4 ), are shown as blue curves in Table 8.12. Note
that the preference function elicitation was again (like in the previous design ap-
plication) performed using the fundamentals of PFM research by Arkesteijn et.al.
(2017).

The systems design integration problem statement is now conceptualised with the
threefold diagram shown in Figure 8.11.
8.5. SOUTH KOREAN FLOATING WIND FARM 227

Figure 8.11: Conceptual threefold diagram, describing the systems design integration for the floating
wind turbine design application. Note: the aim of this figure is to illustrate the relationship between
the different functions and some curves may not represent the actual function.

Design optimisation results & conspection

To generate the design points (i.e., design configuration results) for the different
multi-objective optimisation methods (MODO Min-max and IMAP), the weights
for each objective must first be determined. Traditionally, installation costs have
been the main driver for offshore projects and/or tender bids. However, with
the introduction of the Odesys design optimisation methodology, it is now pos-
sible to optimise the design considering other relevant objectives that reflect the
228 CHAPTER 8. SUMMATIVE ODESYS APPLICATIONS

shared value of the installation plan for both the energy service provider and the
contractor. The following weight distributions were chosen to model this joint
plan: w1,P D = 0.30 for project duration, w1,S = 0.20 for sustainability (emissions),
w2,C = 0.35 for the installation costs, and w2,F = 0.15 for fleet utilisation. For
evaluation purposes, both the single-objective optimisation of OC (SODO costs)
and the MODO min-max optimisation design points are also determined. Note
that the other SODOs (single-objective optimisations on OP D , OF , and OS ) can-
not be included in the integral evaluation as they are not dependent on x4 and x5
(but only on x1 ..x3 ).
The outcomes of the different design points/configurations per optimisation
method are first plotted in the different preference functions showing the differ-
ent objective function values (O1..4 ) and their corresponding individual preference
function values (P1..2,1..4 ), see Figure 8.12 and Table 8.12. Secondly, the numerical
results of the different design points/configurations per optimisation method can
be read from Table 8.13. In this table, one can also find the aggregated reference
score, which was used to determine the overall score/ranking via the PFM-based
MCDA tool Tetra (the resulting aggregated preference scores are re-scaled between
scores of 0 and 100, where 0 reflects the ‘worst’ scoring configuration/alternative
and 100 the ‘best’, see Appendix C for further details).

Table 8.12: Results of the objective functions (O1..4 ) and the corresponding preference functions
(P1..4,1..4 ) of the floating wind design application.

Optimisation methods OP D [days] P1,P D OC [€] P2,C OF P2,F OS [t] P1,S

Single objective OC (SODO costs) 110.5 5 9.96E6 69 0.50 60 3868 73
MODO Min-max 91 43 10.45E6 38 0.04 97 7135 15
MODO IMAP 72.5 70 10.47E6 37 0.35 74 3722 79

Table 8.13: Evaluation of different design configurations per optimisation method and their relative
ranking (based on aggregated preference scores) for the floating wind design application.

Optimisation methods x1 x2 x3 x4 x5 Aggregated preference score

Single objective OC (SODO costs) 0 0 1 2.2 8.0 69
MODO Min-max 1 0 2 2.2 8.0 0
MODO IMAP 1 0 1 2.2 8.0 100
8.5. SOUTH KOREAN FLOATING WIND FARM 229

Figure 8.12: The four stakeholder preference functions (P1..4,1..4 ) for different objectives (O1..4 )
for the floating wind design application, including the results of the different optimisations. The
numerical results can be found in Table 8.12.

The following three conclusions can be drawn from these figures and table:
(#1) Comparing the IMAP configuration with the SODO design point on install-
ation costs, IMAP outperforms the SODO on three of the four objectives. This
difference is most evident when the result of the project duration objective is
compared with the result of the installation cost objective. These objectives are
opposite by the impact of the number of vessels (x1..3 ) on them. More vessels leads
to faster project completion but higher costs. Therefore, a design configuration
that scores well on cost will not score well on project duration, as can be seen
for the SODO on installation costs. This result illustrates that considering only
costs (single stakeholder and single objective approach) is not an accurate repres-
entation of real planning challenge. In contrast, IMAP demonstrates a balanced
approach by considering multiple objectives, including both the technical design
and economics.
230 CHAPTER 8. SUMMATIVE ODESYS APPLICATIONS

(#2) he overall score of the IMAP configuration is substantially higher than that
of the min-max method. As the min-max method tries to minimise the distance
to a score of 100 for all different preference scores P1..2,1..4 , it can result in very low
preference scores for conflicting objectives. In this design application, this is the
case for the project duration (OP D ) and installation costs (OC ) objectives. As a
result, the min-max solution scores low for these two objectives. This is in contrast
with the IMAP design solution, which can find higher preference scores P1..2,1..4
for these two objectives. The presence of these conflicting interests thus limits the
applicability of the min-max method, as also shown in the first design application.
Note that it can still perform well for a ‘single’ interest, as shown by the positive
reflection of the fleet utilisation objective with the use of more barges.
(#3) Table 8.13 shows that all three solutions have the same result for design vari-
ables x4 and x5 . This indicates that this particular combination of x4 and x5 yields
the lowest anchor cost without violating the design performance constraint g2 . In
other words, for all three methods, there would be no difference in the optimisation
if limited to a purely technical optimisation within the feasibility space. However,
the added value of IMAP is evident from the results for design variables x1 , x2 ,
and x3 , where IMAP can arrive at an overall better design solution than the other
two methods by including both technical and vessel-related installation planning
concerns. Note that also the best outcome within the feasibility space for x4 and
x5 will change if objectives in the managerial (subject desirability) domain favour
technical over dimensioning of the suction anchors. In such cases, the solution
may be selected from the edge of the feasibility space (i.e., the Pareto front) as it
offers greater benefits to the overall planning and design performance.
 Part III

Educating the Odesys engineer

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Chapter 9

The art of Open Design Learning

In this chapter, we will first briefly revisit some parts of Chapter 2. We placed
design in a broad scientific and technical context in that chapter. Here we add
what this means for the real Odesys system integrator, introducing the Zeta (ζ)
engineer and explaining their integrative position. From this positioning and from
a comprehensive educational incitement following a number of key educators, we
show the main founding principles and educational paradigms of the Open Design
Learning concept (ODLc). Finally, we conclude this chapter with the open-ended
ODL U-diagram. In this, students follow an open-ended design learning approach
of three cycles (1) the technical - concept, (2) the social -context, and (3) the pur-
pose - consign/conceive cycle respectively: an open-ended design learning meta-
morphosis integrating the open mind, heart, and will with an open design impulse
as unifying result.
Note that these founding principles and the key elements of ODLc are sum-
marised here in the form of a concise management summary. To place ODL in the
current context of other educational concepts, reference is made to the education
paper by Binnekamp, Wolfert et al. (2020) and/or the education section from the
paper by Wolfert et. al (2022). Finally, it is noted that ODLc is not a method
but a learning concept in itself. It is therefore not an instruction, but a concept
with constructivist learning principles which you have to live and do yourself. This
chapter assumes that the basic principles and concepts from Chapters 1, 2, 3, and 4
are known, as we continue to work with them (such as Odesys’ paradigms & views
on world and man, theory U and the different U-diagrams, and the (extended)
4-Quadrants models).

233
234 CHAPTER 9. THE ART OF OPEN DESIGN LEARNING

Incitement 9.1 Learn to learn and do not be afraid of the future dark

As a recently graduated civil engineer, I had a talk with the CTO on my first working day at
Deutsche Telekom (DT), an ICT service provider. Being a promising new DT ‘trainee’, he
had invited me to join him. He explained to me that we needed to develop a new build and
roll-out concept in which a new so-called ‘network-sharing’ concept was a major cost driver
for DT. He was confident that we would be able to manage this concept technically. At the
moment, however, we did not have approval from the licensing authority OPTA to roll out
the network sharing concept. He had just had a meeting with them and the OPTA asked
for a reasoned case for the network sharing concept in which not only cost savings were the
driver, but also the benefits for the users, the ‘civilians’, and the living environment. Given
my background in civil engineering, this ‘civilian’ assignment seemed a good fit for me. In
addition, he handed me a stack of documents that, coincidentally, I couldn’t transport all
at once to my workspace. Furthermore, he requested me to create a poster that we could
bring to the upcoming OPTA meeting, scheduled to occur within the next week or two.
This was my response: “Although I am a civil engineer, I have never heard of licensing and
legal-technical matters.” I asked him: “Do you have any ’old’ mock exams within DT to
qualify me in this field? And, do you perhaps have a sample elaboration of this and or a
similar problem that you earlier solved? And lastly, what should the poster look like, what
is the format and do you have a list of what should be on that poster?”
The CTO replied: “We actually just hired you to work on this problem, or on other problems
that have not yet been solved. I expect you to come and ‘educate us’ and surprise us with
solutions that we just don’t know yet ourselves.” Moreover, he stated: “It seems that you
are being unlearned and that you are afraid to push yourself into new domain and solve
unknown problems. In any case, I hope you have confidence to find your way, and you know
my door is always open for co-creation. And by the way, don’t worry too much yet about
the underlying modeling in detail, we can always send these after. It is about the bigger
picture first.” Finally, he said: “A civil engineer should be able to work especially for and
with civilians, not just only from and with civil technics, shouldn’t he?” “Think about that
again”, he advised me and I started my way into the future dark.

9.1. Positioning the Odesys engineer

In Chapter 2 we have distinguished between the natural or Beta (β) domain and
the social or Gamma (γ) domain, providing a distinction between scientific re-
search and engineering development. This allowed us to define a research and
development framework containing four types of empirical R&D domains, see the
4-Quadrant model in Figure 9.1.
9.1. POSITIONING THE ODESYS ENGINEER 235

Having made these distinctions, we can now position what we call the Odesys (open
design systems) or Zeta (ζ) engineer: a real systems integrator. Note that the Zeta
(ζ) is the symbol of integration as it signifies the integration of multiple domains.
Here the top part of the symbol signifies the broadening of the management domain
and the bottom represents the anchoring in the technical domain (‘hook in the
ground’).

Figure 9.1: The 4-Quadrant model with the distinction of the β and γ domains

Let us now look closer at the activities of the Odesys engineer, a ζ professional
within the empirical R&D fields. Although the open design systems engineer
operates mainly in Q3 and Q4, there is a need for integrating the other quadrants.
Namely, to solve design/decision problems the ζ engineer makes use of the body
of knowledge of the natural and social sciences. Think of the laws of physics,
the output of (lab) experiments, preference function modeling and measurement
theory, the output of the statistical analysis of interviews, etc.. This serves as
one of the contextual starting points for the ζ engineer. However, because the
ζ engineer at technical universities is also schooled in the engineering domain,
they have an advantage over those who are only schooled in business management
schools that focus primarily on social science (Q2). Figure 9.2 illustrates how we
position the open design ζ engineer within the empirical R&D context.
Let us now zoom out to the activities of the Odesys engineer, a ζ professional
within the broader context of the spiritual mind and the physical matter. To do
this, we must first recall the essence of design. Design means (in a non-artistic
sense) a plan or scheme in the mind (inner) for a potential realisation in the
observed world (outer). Design is a process of concretization within the synergetic
236 CHAPTER 9. THE ART OF OPEN DESIGN LEARNING

context of mind, matter, subject, and object that unites the open design impulse
(see Chapters 1 and 2). Designing to best fit for common purpose is also a U-
process which moves from an open configuration (mind-imagination) through an
open space (heart-inspiration) to the open source. The process then moves in the
opposite and ’renewed’ direction to an action of response, through an inner (will-
intuition) and model (will-deliberation) dialogue (see Chapters 4 and 6). This is
a process of U-ncovering the common will resulting in a realisation of a prototype
configuration (see Chapters 3 and 4).

Figure 9.2: The position of the Odesys ζ engineer, mainly positioned in Q3/4, gathering knowledge
from Q1/2.

In addition, we have seen that the final outcome of a design is that artefact
which, given its specific desirabilities and capabilities, is best-fitting. However,
this artefact is certainly not the one and only fitting ‘socio-technical construct’,
which can lead to a possibly different outcome somewhere else in another place in
another time (see Chapters 2 and 4). Last but not least, the open designer should
at all times realise that his creation of the mind has a moral impact to open ‘doors’
for the future (see Chapter 2). In summary, this means that the open design ζ
engineer is more than just an integral designer, but is a true ζ professional which
must be able to ’navigate’ integratively (‘synergetically’) in the con-scientific (hol-
istic) fields of empiricism, spiritualism/art, constructivism, and rationalism/formal
logicism. Figure 9.3 illustrates how we position the open design ζ engineer within
this extended con-science context (via the extended 4-Quadrant model of Chapter
2). Note that the process of inner dialogue/ meditation makes just that differ-
ence between computer logics and applied mathematics, the difference between AI
9.1. POSITIONING THE ODESYS ENGINEER 237

and art (see Chapter 2). The ζ professional needs both logics (computer model)
and real-life mathematical modeling (reflective/meditative dialogue) to come from
mind toward matter.

Figure 9.3: The position of the Odesys ζ engineer, integrating subjective Q2/Q4 supported by ob-
jective Q1 into the socio-technical design.

Now that we have positioned the Odesys engineer as a genuine ζ systems in-
tegrator (‘synergeticor’), we must establish a learning concept that ensures that
the aforementioned integrations can be achieved as much as possible. Important
principles will therefore be constructivist, formal logical, inner-outer dialogical,
experiential, and observational characteristics. To lead the way for ζ engineers,
we have developed an open design learning concept (ODLc) that supports the
Odesys education. Why? Because we observed that today’s education is often
based on existing static and past (research) knowledge transfer, where teachers in-
struct what students have to think. Alternative thought pathways are closed and
students are funnelled towards using single solutions derived from past problems
rather than opening them so that they are prepared to solve future multi-faceted
problems. Teachers or ‘instructors’ mostly do believe that they are empowered
to only fill the inner of their students with known facts and procedures to under-
stand existing situations. We believe that education should also work outwards to
create solutions from and for our societal challenges and aims. Educators should
therefore not simply be teachers, but developers or ‘constructors’, people who do
incite, co-create, and learn to learn by designing within a real-life context. For
this purpose we devised the ODL concept (ODLc) that is fully congruent with
aforementioned ζ principles. ODLc truly unlocks and integrates/synergizes the
inner ego and the outer eco along the U-model, as described in the next sections.
238 CHAPTER 9. THE ART OF OPEN DESIGN LEARNING

Incitement 9.2 ‘Steiner Waldorf education in Silicon Valley – a preparation

for life?’

“Why Are Silicon Valley Executives Sending Their Kids to a Tech-Free School? Parents
employed by Google and Apple are sending their kids to computer-free Steiner Waldorf
schools. Are they on the right track?”
You’d think executives at Silicon Valley’s top tech firms would be keen to enrol their children
in schools chock-full of the latest education technology: one-to-one laptops, iPad programs,
digital textbooks, and teachers engaging students using Twitter. But according to The New
York Times, some Silicon Valley parents are doing a 180 and sending their kids to the area’s
decidedly low-tech Steiner Waldorf school. Waldorf’s computer-free campuses are a sharp
contrast from most schools, where access to technology is seen as key to getting kids college-
and career-ready. Instead you’ll find plenty of play-based learning and storytelling.
While that may sound out of place at a time when moms brag about their 3-year-olds’ abil-
ities to operate iPads, there’s an appeal to Waldorf schools’ philosophy that students should
”experience” literature, math, and science—along with visual and performing arts—in a
developmentally appropriate way. The tech-free teaching methods are designed to foster a
lifelong love of learning and teach students how to concentrate deeply and master human
interaction, critical thinking, creativity, and problem-solving skills. Indeed, through knitting
socks, Waldorf students pick up math and patterning skills, and they come out of it with
something beyond a standardized test score to show for their effort.”
The Steiner Waldorf approach to education is both innovative and insightful (more than
1200 schools in more than 60 countries worldwide). Students are well-balanced as individu-
als and develop a general enthusiasm for learning, whatever the context might be. With over
a hundred years of experience to draw on, the education is well-proven and central themes of
innovation and enquiry ensure that it remains at the forefront of contemporary education in
a fast-changing world. It enables students to mature in a balanced way; innovative and rig-
orous academic education is combined with the development of impressive human qualities.
These human qualities promote purposeful engagement and, more than anything else, they
ensure that our pupils take up meaningful and fulfilling roles that contribute in a positive
way to our rapidly-unfolding future. It’s all about igniting the flame rather than filling the
barrel. The Steiner Waldorf educator addresses the whole child and each lesson integrates
academic work with fine arts and practical skills, so that a child is not only intellectually
engaged, but also emotionally and aesthetically invested in their learning. By addressing
intellectual capacities (thinking), artistic and emotional capacities (feeling), and practical
skill-building capacities (willing), the Steiner Waldorf curriculum brings key attributes of
the human being into balance. The Steiner Waldorf schools develop analytical, logical and
reasoning skills as education has always done, but also fosters social skills, cooperation,
imagination, inspiration, intuition, creativity, and flexible systems-thinking.

We conclude with some typical Steiner Waldorf educational quotes:

...‘What matters to us is how you learn something to the student, not what you learn to the
student.’
...‘You get to know things in their genuine reality only when you can relate to them in the
real world.’
...‘To come to yourself you must look out into the world together.’
...‘It is important that we should discover an educational method where people learn to
learn, and go on learning from life their whole life long. There is nothing in life from which
9.2. OPEN DESIGN LEARNING (ODL) CONCEPT 239

we cannot learn.’
...‘Education can be a force for social change.’
...’Knowledge is power, that prescribes (normative). Wisdom is love, that lets free (explor-
atory).’
...’Not everything that counts can be counted.’
...‘Luminosity (clarity) in thinking, engagement (compassion) in feeling and warmth (con-
ductive) in the will, this is how man comes enthusiastic in being.’ (compare these with the
‘daily’ sayings like: shining a light on something; I am warming up to the idea; that’s close
to my heart; having a warm heart for the matter; that’s a real light-bulb moment).’

See also: waldorfeducation.org or waldorftoday.com or

amexpas.net/articulos/a-silicon-valley-school-that-doesnt-compute

9.2. Open Design Learning (ODL) concept

The ODLc may actually deserve an independent book given its importance and
impact on young people who use education to lay their foundations and build their
future (”he who has the youth has the future”). However, that is not the focus of
this book (that is the new design methodology Odesys). Nevertheless, to give ODL
some place here, we will first summarise the basic founding principles of the ODL
concept following from some inspirators and/or educators in the form of an extens-
ive incitement. A conscious decision was made, as in the ODL concept itself, to
work from an incitement and let it be a basis for the main components of the ODL
concept (rather than an entirely bottom-up narrative). Indeed, the incitements
of these great educators and a few of their short quotes: Ackoff (2008); Argyris
& Schön (1987, 1996); Aristotle (1985); Biesta (2014); Buber (2004); Hamelink
(2015); Heusser (2022); Hoffmann (2020); Katz (2011); Palmer & Zajonc (2010);
Robinson (2011); Schieren (2012, 2023); Steiner (1995,1996); Wiechert (2012), and
some well-known names without references form the basis for ODL. Note that the
order of the incitements is not educator alphabetical, but aims to line up various
terms and concepts that are printed in bold and italics in the text. These terms
will be used later in the nine basic ODL foundations. Before we start with these
nine foundations, we provide a general summary of the concept (followed by the
extensive incitement which is partly the design basis for the ODLc).
ODLc is both a constructivist and a design and systems thinking-based learn-
ing approach (“learn to design by real-life designing”), where students actively de-
velop new solutions originating from their inner and outer designs. It goes beyond
240 CHAPTER 9. THE ART OF OPEN DESIGN LEARNING

research and inquiry based learning concepts such as experiential and/or organiz-
ational learning, see Binnekamp, Wolfert et al. (2020). ODL integrates the human
learning & development process, viewed from the general human (threefold) prin-
ciples. It forms the fundamental basis for creating open, integrative and persistent
learners concerned about solving future world problems.
The ODL concept is an innovative educational concept for higher education.
It is a reflective, creative, and engaged learning approach that opens human devel-
opment and U-nlocks new knowledge and solutions. The ODL concept stimulates
students’ curiosity, clarity, and creativity. ODL constructors and students are
working in an open spirit levelling relation.
The ODL approach connects the inner personal learning ego and the outer
real world eco. The students and the teachers cooperate in a living dialogue in-
and on-action. This co-reflective dialogue creates an open space where alternative
views can co-exist and new insights can be conceived. We argue that only a living
and reflective dialogue with luminosity in the mind, intimacy in the heart, and
warmth in the will can conceive openings for the challenges from tomorrow.
ODL students learn and design a self-chosen system of interest (SoI), as op-
posed to a given and predefined casus that has already been solved by the teach-
ers (such as in most of the traditional experiential education concepts such as
PBL/CBL/CDIO). They follow ODL U-model as the basis for the design learning
process to arrive at an original ODL response demonstrating their unique indi-
vidual achievements. In other words, students follow an open-ended design learn-
ing approach of three cycles (1) the technical - concept, (2) the social -context,
and (3) the purpose – source (consign/conceive) cycle respectively: an open-ended
design learning metamorphosis integrating the open mind, heart, and will.
The ODL concept has been developed and applied over the last 10 years. It
has found its way into several MSc curricula within TU Delft. The number of
the participating students varied between 25 - 350 students per course in differ-
ent topics (engineering asset management, engineering projects management, con-
struction management systems, information systems, systems engineering design,
R&D methodology, and innovation management).
ODLc can be seen as a thorough extension of Steiner Waldorf education for
Master students within the age of 21+ (so far this education concept has only been
developed for students under 18-21).
9.2. OPEN DESIGN LEARNING (ODL) CONCEPT 241

Incitement 9.3 ‘Open Design Learning, the art of education’

Gert Biesta: “We cannot understand education as a powerful, production-like process, but
only as a weak, existential process. He shows that we must set goals in education more
broadly than just measurable yields and outcomes, and argues in unsurpassed fashion that
if education is to succeed, it cannot be enforced by anyone.. Today, education is mostly
knowledge-driven and must effectively and efficiently contribute to the knowledge economy.
Two entirely different goals are often overlooked: socialization and personal development
or ‘Bildung ’. Education is sinking into ’learnification’ and is no longer a free place for crit-
ical thinking about developments in society.. Education is a form of co-creation. Creating
what is not yet there. Making yourself vulnerable by showing that you too do not know
everything but dare to explore and create the new: a beautiful risk .”

Nigel Hoffmann: ”Concern for the world today provides the impetus to ask of ourselves
a profound question. how can our way of knowing, the very style of our thinking which
informs our research and our teaching, come to express care, to reveal itself to be a deed
and duty of care?’ Basing this practical study on the human quality of care for the world
around us, Hoffmann takes us to a threshold beyond which lies a true science of living form.
Care, he says, springs from the whole human being - the thinking, heart and will - and
is implicit in the scientific method of conscious inner participation in nature that derives
from the work of the poet and scientist Goethe. The Goethean approach - a living form that
unites science and art - is not an alternative to contemporary science but complements it.”

Peter Heusser: “Thinking is a pure activity of the will and identical to the activity of
the artist (’designer’). When thinking wants to be experienced one speaks of artistry
(’design’). In reality, thinking is not just ”passive” thinking, but actively working thinking
and also feeling and willing . Willing is an activity, activity and productivity Feeling is
a connection, devotion and receptivity. And, thinking is the ideal perception of thought-
content. Therefore, in thinking the whole person, including the willing (productive) and
feeling (receptive) person is present. Consequently, a state of artfulness arises in the think-
ing man at the same time. ... Only teachers and educators who acquire skills in these
fundamentals of artistic (design-based) thinking are able to awaken in their pupils and stu-
dents the desire to develop such skills themselves. Heusser argues that only in this way can
one bring about the necessary transformation in educational (and scientific) culture.”

Rudolf Steiner (1): “We must conceive of pedagogy not as a theory, not something that can
be learned, but as an ability of the teacher or lecturer to develop by strengthening his living
artistic (‘design’ ) mind itself in a living humanity and in pure thinking ... Our task in
our method of education is always to consider the whole human being . We could not do
this if we did not focus our attention on the formation of the artistic (design) sense in human
disposition. By doing so we will also make man inclined for later to take an interest in the
whole world with his whole being. All educational methodologies must be immersed in the
artistic. Educating and teaching must become a true art (‘an art of design education’).
Knowledge should be only the basis there too... When you look at the whole human life,
not just childhood, it becomes clear for the first time what a central significance in the
whole human life education actually has, how often happiness/ luck or unhappiness/ unluck
in terms of the spiritual, psychological and physical are related to education..When those
who want to become teachers or educators are examined today, they mainly look at what
242 CHAPTER 9. THE ART OF OPEN DESIGN LEARNING

they have gained in terms of knowledge, which is actually quite superfluous. After all, what
they need for teaching, they can always reread in a suitable book or on an internet site when
preparing. After all, what one has learnt for the exam is soon forgotten afterwards anyway.
Exams are just a comedy in life. Real education is learning to learn a life long.”

Rudolf Steiner (2): “A curriculum should be an echo of humanities. You relate everything
you see in the world to what you see in humans . Our highest endeavour must be to develop
free human beings who are able of themselves to impart purpose and direction to their lives.
The need for imagination, a sense of truth, and a feeling of responsibility—these three
forces are the very nerve of education. We are fully human only while playing, and we play
only when we are human in the truest sense of the word... Intuition is for thinking what ob-
servation is for perception. Intuition and observation are the sources of our knowledge...
Our highest endeavour must be to develop individuals who are able out of their own initi-
ative to impart purpose & direction to their lives... Reverence, enthusiasm and a sense
of care, these three are actually the panacea, the magic remedy, in the soul of the educator.”

Pablo Picasso: “Everything you can imagine is real... Inspiration exists, but it has to
find you working... Every child is an artist. The problem is how to remain an artist once
he grows up.”

Parker Palmer & Arthur Zajonc: “We propose an approach to teaching and learning that
honors the whole human being – mind, heart, and spirit – an essential integration. Who-
ever may be, whatever the subject we teach, ultimately we teach who we are.Good teaching
cannot be reduced to simply a technique, good teaching comes from the identity and in-
tegrity of the teacher... The educator is a person who has the possibility through destiny to
know the people, to recognize their capacities, and to bring them to bear on the problem.”

Russell Ackoff (1): “Except for practices that incorporate design as the way they practice,
the art of design is not incorporated into students’ experiences in schools, despite its su-
periority in many situations, even to such analytical problem solving as scientists employ.
The power of design as an instrument of learning is almost completely overlooked by the
educational system. For example, the best way to learn how an automobile works and to
gain understanding of why it works the way it does is to design one. Moreover, it is in design
that people learn what they want... Reality consists of sets of interacting problems, systems
of problems we call ‘messes’. As previously noted, problems are abstractions extracted from
reality by analysis. Therefore, education for practice should develop and apply method-
ology for dealing holistically with systems of problems. Because messes are complex,
this requires an ability to cope with complexity. It is much easier to deal with complexity
through design in practice than in dealing with it academically in a classroom or research
facility. The theory of complexity is not required for dealing with complexity in practice;
design can handle it... Those involved in the redesign process must know what they would
do if they could do whatever they wanted. Such knowledge is essential if they are to set
meaningful goals for the future. The outcome of such a design is idealized in the sense
that the resulting system is ideal seeking , not ideal. It should be subject to continuous
improvement with further experience and changing environments. The only certainty is that
some of whatever we think we will want five or ten years from now will not be wanted then.
Such a vision should be inspiring, a work of art... Scientists are searching for a way of
dealing effectively with such complexity . Unfortunately, most of them are approaching
9.2. OPEN DESIGN LEARNING (ODL) CONCEPT 243

the subject analytically. The result is identification of such a large number of variables and
relationships between them that we are not able to handle them. However, if complexity is
approached synthetically , by design, there seems to be no limit to the complexity we can
handle effectively.”

Russell Ackoff (2): ”All through school, we are shown that making a mistake is a bad thing,
something for which we are downgraded. This reveals how little conventional schools are
interested in learning, because we never learn by doing something right; we already
know how to do it. Doing it right does confirm what we already know, and this has some
value, but it contributes nothing to learning into the future... Exams do not assess any-
thing significant to the future of children, because no one knows how to assess or measure
the key factors to the future success of any person. They are a closed system; tests exist
for their own sake. They measure the ability of the entire school community—children, par-
ents, teachers, administrators—to focus all their efforts on producing good results on tests!
Nothing more, nothing less.”

Russell Ackoff (3): “The objective of education is learning , not teaching. The ideal school
is a school where there is no teaching but a lot of learning...One might wonder how on earth
learning came to be seen primarily a result of teaching. Until quite recently, the world’s
great teachers were understood to be people who had something fresh to say about some-
thing to people who were interested in hearing their message. Moses, Socrates, Aristotle,
Plato, Jesus, Steiner etc.—these were people who had original insights, and people came
from far and wide to find out what those insights were. One can see most clearly in Plato’s
dialogues that people did not come to Socrates to “learn philosophy,” but rather to hear
Socrates’ version of philosophy, just as they went to other philosophers to hear (and learn)
their versions. In other words, teaching was understood as public exposure of an individual’s
perspective, which anyone could take or leave, depending on whether they cared about it.
No one in his right mind thought that the only way you could become a philosopher was
by taking a course from one of those guys. On the contrary, you were expected to come up
with your own original worldview if you aspired to the title of philosopher... The educational
environment of students should encourage them to continue to explore the open-ended
connections between their experiences, and to be receptive to new interconnections and
interpretations of theories and explanations that they have either learned or developed.”

Ken Robinson: ”Our task is to educate our students whole being so they can face the fu-
ture. We may not see the future, but they will and our job is to help them make something
of it. Creativity now is as important in education as literacy, and we should treat it with
the same status. Imagination is the source of all human achievement. Too many people
never connect with their true talents and therefore don’t know what they are capable of
achieving..We have to go from what is essentially an industrial model of education, a manu-
facturing model, which is based on linearity and conformity and batching people. We have
to move to a model that is based more on principles of agriculture. We have to recognize
that human flourishing is not a mechanical process; it’s an organic process. And you
cannot predict the outcome of human development. All you can do, like a farmer, is create
the conditions under which they will begin to flourish. Learning happens in the minds
and souls, not in the databases of multiple-choice tests... Teaching for creativity involves
teaching creatively. There are three related tasks in teaching for creativity: encouraging,
identifying and fostering..To improve our schools, we have to humanize them and make
244 CHAPTER 9. THE ART OF OPEN DESIGN LEARNING

education personal to every student and teacher in the system. Education is always about
contextual relationships. Great teachers are not just instructors and test administrat-
ors: They are mentors, constructors, motivators, and lifelong sources of inspiration to their
students. Do schools kill creativity? Everyone is born a genius but mainstream education
kills creativity .”

Chris Argyris & Donald Schön: “Complexity, instability, and uncertainty are not removed
or resolved by applying specialized knowledge to well-defined tasks. If anything, the effective
use of specialized knowledge depends on a prior restructuring of situations that are complex
and uncertain. An artful practice of the unique case appears anomalous when professional
competence is modelled in terms of application of established techniques to recurrent events.
Problem setting has no place in a body of professional knowledge concerned exclusively with
problem solving ... We are in need of inquiry into the epistemology of practice. What is the
kind of knowing in which competent practitioners engage? How is professional knowing
like and unlike the kinds of knowledge presented in academic textbooks, scientific papers,
and learned journals? In what sense, if any, is there intellectual rigor in professional prac-
tice? Reflective practice is the ability to reflect on one’s actions so as to engage in a
process of continuous learning from real life experiences.. Most people define learning too
narrowly as mere ’problem-solving’, so they focus on identifying and correcting errors in the
external environment only . Solving problems is important. But if learning is to persist,
managers and employees must also look inward . The need to reflect critically on their
own behaviour, identify the ways they often inadvertently contribute to the organisation’s
problems, and then change how they act.. Individual learning is a necessary but insufficient
condition for contextual learning.”

Herbert Simon: “We can conclude that, in large part, the proper study of mankind is
the science of design, not only as the professional component of a technical education but
as a core discipline for every liberally educated person.”

Jost Schieren: ”Steiner Waldorf Education: An all-round, balanced approach to educa-

tion that is equally concerned with intellectual-cognitive and artistic-creative learning. A
practice- and experience-based pedagogy. An alternative education that has been success-
fully practiced for over a century. Recent scientific inquiry into Steiner Waldorf Education
is breaking new ground, casting light on its fascinating humanistic ideal and wholistic
potential ...Since the beginnings of Western thought, there have been broad discussions
in philosophy, psychology, and pedagogy, on the issue of the learning process and its sig-
nificance for the human being. Within the Steiner Waldorf education concept some core
aspects of learning are transformation, knowledge & capabilities, holistic orienta-
tion, truth, freedom and purpose. The learning concept of Waldorf education is based
in Steiner’s epistemology. In his early philosophical writings, Steiner took a basic epistem-
ological position in regard to man’s relationship with reality. He distances himself from
a naı̈ve-realistic notion of reality that takes the world phenomena as given and attributes
purely reflective mirroring functions to human cognitive processes. It is the human nat-
ural constitution (mind and matter) of increased ability in man’s encounter, interplay, or
dialogue with the world. The learning environment created by arranging an internship, a
synergist practice, is an essential determinant of the learning and development process.An
essential core of Steiner’s thought is the perspective of a capacity for freedom of the human
being. He outlines a view of man that leaves development open-ended and contains the
9.2. OPEN DESIGN LEARNING (ODL) CONCEPT 245

ethos of individual freedom. This is in contrast to contemporary historical-pedagogical

anthropology, which rejects any form of an authoritative view of man.”

Christof Wiechert & Jacques Meulman: “Teaching is a dialogic process, where dialogue
can be multidimensional. Show the world as image not as an understanding. Learning is
bottom-up, we nurture will and action, which they can feel and which awakens the intel-
lect. Learning is also top-down, we incite the intellect, to which they connect and which
warms the will... Education is the integration of knowing and being able to... Learning is
not linear but pulsing (a periodic process)... Education must move either from one-sided
knowledge (‘kennis’) or only skills (‘kunde’) toward arts (‘kunst’), and/or design, because
arts integrate knowledge and skill with the ”heart,” the social human context... Light
in mind (‘sheds new light on the matter’), warmth in the will (‘getting warmed up about
doing something’) and love from the heart (‘having a heart for the matter’, ‘it is contagious’,
‘enthusiastic and emotion’). Not knowledge is power (past) , but wisdom is love and art
(design) is future... Not all that counts can be counted... Education should be so vivid
that a test is no longer needed. A final open project instead of a final closed test.”

Martin Buber: “Human life and humanity come into being in genuine encounters. The
hope for this hour depends upon the renewal of the immediacy of a living dialogue among
human beings. When two people relate to each other authentically and humanly, ‘spiritual
electricity’ surges between them.The real struggle is not between East and West, or capit-
alism and communism, but between education and normative propaganda. There are three
principles in a man’s being and life: the principle of thought, the principle of speech, and
the principle of action. The origin of all conflict between me and my fellow-men is that I
do not say what I mean or think, and I do not do what I say: a open-minded dialogue with
action of response.”

Joseph Beuys: ”There is an artist (designer) in every person. Every human being is an
artist (designer), a freedom being, called to participate in transforming and reshaping the
conditions, thinking and structures that shape and inform our lives. To make people free
is the aim of art, therefore art or design for me is the science of freedom..For instance,
in places like universities, where everyone speaks so rationally, it is necessary for a kind of
enchanter (magic or spell) to appear.”

Cees Hamelink: “He argues for re-enchantment at universities. After all, a scientific theory
that gives insight into a wondrous reality is certainly enchanting. By accepting that won-
drous reality, however, we should sometimes also dare to say, ”I don’t know (yet).” But for
that, we often lack courage... To understand the soul of world and solve the complicated
problems of today’s society, we need enchanters. That is why we need people who encourage
us to create art unabashedly and ‘chant’ in the classrooms, full of enchanted stories and
incitements. Even as an educator, you should want to remain continuously enchanted just
as, for example, Einstein was. He said, ’I can explain a storm physically very well but a
storm was at the same time a religious experience for him because in the storm he had the
experience of being part of a much larger universe’...He advocates Amor Mundi as Hannah
Arendt described it earlier: feeling at home in the world and dealing with it and learning
from it without hesitation. Dare to love the world. Dare to take responsibility for a world
that is bigger than people alone and includes everything that lives in the world, more than
a world of passive, producible and inanimate objects... This is what we should already start
246 CHAPTER 9. THE ART OF OPEN DESIGN LEARNING

with in education, teach students to feel at one with the earth, nature and the experiential
context around them... It is important to teaching ’compassionate communication’ to
students and others.”

Aristotle: ”Within universal knowledge, roughly three areas of knowledge can be distin-
guished: the use of reason (logic), being (physics and metaphysics) and man’s actions
(ethics and design).. Many observations and the memory of them lead to experience (‘em-
peiria’). Skill or ”art” (‘technê’) comes from much repeated experience of similar situations;
Technê is always practical or productive. Many practitioners do their work without know-
ing exactly what they are doing, routinely, but the masters do know what they are doing
and for this reason are able to impart their professional knowledge. Scientific knowledge
(‘epistêmê’) is not productive, but always theoretical. Epistêmê is knowledge for knowledge’s
sake and never aimed at practical utility or enjoyment.. The only reliable characteristic of
profound knowledge is the ability of teaching . The purpose of art is not to represent
the outward appearance of things, but the inward that is the real reality... Doubt is the
beginning of wisdom.”

Let us now summarise the main principles and elements as part of the ODL concept
point by point. These principles are only briefly described here to get started
and can be seen as the ODL basic foundations (‘grondslagen’) for further self-
development. Although these should not be seen as static elements, but as living
ones, some of them can typically be included in a course syllabus. Note that an
additional section (9.3) is devoted to the new ODL-U model, as the overarching
foundation of the ODL concept.

# 1 ODL’s human-centered paradigm

ODL is based first and foremost on the three- and/or nine-fold human image as
described in Section 1.6. This view on world and man and its associated general
human-centered paradigms form the basis for ODL’s educational approach. Here
we formulate two extra pedagogical human image paradigms, PIII and PIV, to
complement the earlier two paradigms (PI and PII). We focus in this section on
Master students aged 21+.

The first additional pedagogical paradigm has to do with the developmental stage
of the child/student and it reads:

PIII – ‘the curriculum and the teaching approach should match the develop-
mental stage of the student’
9.2. OPEN DESIGN LEARNING (ODL) CONCEPT 247

Steiner pointed out that human development takes place in periods of about seven
years (later confirmed and further examined by Lievegoed (1996, 2013), among
others). In each period, the focus of development is on something different. With
the alternation of the periods a change occurs, but also halfway through the periods
there is a change, which is referred to as I-realisation. This I-realisation is a kind
of impact (insert) moment or conscious awakening via the I-sense. An I-realisation
is a moment in a phase of life when you begin to experience yourself differently
(via your I-sense). You become aware of something. You see yourself differently
from the rest of the world. In this section we briefly discuss the development from
birth to age 28, to better tailor the educational approach to the specifics of the
student’s developmental stage (with a focus on the stage of 21-28 years, the age in
which the MSc student is usually located). These four periods can be seen from
the child’s/student’s relationship with his environment, see also Figure 9.4: i.e.,
1. In the period from 0 - 7 years, the child’s relationship to the environment
is that of outside to inside. The small child perceives a lot. However, the
perceptions and experiences that are gained do not yet come together in a
centre. Everything is simply absorbed and is imitated. Halfway through, at
about the age of three, the child begins to refer to itself as ”I” instead of its
own name. From then on, it distinguishes between itself and all other beings
and things (this is the first I-impulse or I-realisation, in Dutch ‘ik-inslag’).
2. In the second period (7 - 14), the child lives in a world of his own and has
become a closed unit. Perceptions no longer penetrate unhindered, but are
modified. From a centre, forces work up to the limit of one’s own world (an
example is the self-conceived and designed imaginative play, where attributes
are something other than they are in reality). Around the ninth to tenth year,
there is another I-impulse or moment of self realisation. The child withdraws
more into themself. It starts to see differences and notices that its neighbours
are different. Its own emotional life awakens. Discourses with others follow,
with accompanying criticism and opposition.
3. In the third period (14 - 21), the main direction is from inside to outside.
The environment must be conquered and is adapted to one’s own perceptions
and emotions. The I-impulse is around the 19th year. The adolescent starts
looking for his ideals and values. With the I-impulse, the I in the will and
activity is born (realized). This allows judgment to become more personal
and coloured more from one’s ideals. With the I-impulse, the spiritual basis
for self-education is laid. One starts trying out a lot, experiencing and also
traveling into the world alone.
4. After the 21st year, this unilateral movement comes into balance as man
strives to explore the environment. The environment again intrudes more
inwardly. This period (21 - 28) has the characteristic that the activities
248 CHAPTER 9. THE ART OF OPEN DESIGN LEARNING

outward interact with the experiences coming from there. By the 21st year,
the body has grown. The I organization is born as the fourth part of beings.
The personality can appear. The will has matured to act independently and
the young person can begin to take responsibility for themself and for others.
Part of the powers of the will become available for independent creation and
for creative thought. One passes from the imitation to the self phase. The
sensing or sentient soul (‘gewaarwordingsziel’, and see Chapter 1), the part of
the soul directed toward perceiving, is developed. Until the 21st year, things
are bestowed; after that, the young person must work to develop themself.
That has to do with the self that is born. Before 21, you imitate everything,
so to speak: mimic (’after-doing’), imitate (’after-feeling’) and reflect (’after-
thinking’). From 21 you start doing it yourself from the sensing soul (next
seven-year focus). Self-education begins, everyone is specifically sensitive to
certain impressions and they seek them unconsciously. The educators must
work together to realize the ideals. It is this striving that is formative to
young people.

Figure 9.4: The child/student’s relationship with their environment, conform Lievegoed (2013).
9.2. OPEN DESIGN LEARNING (ODL) CONCEPT 249

We can distinguish the following specific I-organization characteristics of the 21+

student (we will use these three later in the ODL U model):
Thinking (open mind) - In thinking, two things stand out. Because the young
person must be able to discover where their ideals lie, they must be exposed
to and offered many things. They can then notice where their preferences lie.
Perception supports the will because perception clarifies will intentions. Thinking
also becomes focused on fathoming coherence in the world. The step to self-
education can be made so that one takes charge of one’s own development, alone
or with others.
Feeling (open heart) - In the previous age phase (from 14-21, with focus on the
astral body), feelings were strongly experienced and lived out. This continues, the
feeling life remains intense. The opinion others have is still important, but slowly
shifts through one’s own awareness to an independent placement towards, and in,
the world.
Willing (open will) - The impulses that emerge from willing must be followed
and explored. Practical situations are at least as suitable for this as training
situations, because in practice one encounters the world more strongly and sees
what one wants to do.
Incitement 9.4 Ecce Homo

(Dr. Rudolf Steiner, philosopher/ artist and educator)

In the heart, feeling weaves

In the head, thinking illuminates
In the limbs, willing strengthens
Weaving illuminating
Strengthening weaving
Illuminating strengthening
That is the human.

The second additional pedagogical paradigm, also called Steiner’s pedagogical prin-
cipal law, has to do with the way an educator should focus their interaction, and
it reads:

PIV - ’the educator is acting on the development of the child/student from the
adjacent part of being (from a specific realm)’

For example, children in infancy develop their physical body, the educator/teacher
works on this with their ether body (see the ninefold figure of man in Chapter 1).
That means the educator works on their ether body and perfects it. Precisely the
continual work on the ether body is important. It is the same with the successive
ages. With children from 7 to 14 one must work on their astral body. With
250 CHAPTER 9. THE ART OF OPEN DESIGN LEARNING

youngsters from 14 - 21 educators work on their I-body and with youngsters from
21 - 28 on their spirit-self (from imagination). That means working together to
develop constructively the education. This has a forming effect on the students’
self. Note that the I-organization, the body is what makes man an individual, gives
him a centre and makes man constructively and creatively active. This paradigm
PIV has been schematically summarized in Table 9.1.

Table 9.1: Result of Steiner’s pedagogical principal law, see Lievegoed (1996,2013) and Steiner (1996)

Educator operates primarily from their... Educator interacts primarily with the Aged
... of the child/student
Ether body Physical body 0-7
Astral body Ether body 7 - 14
I-organisation Astral body 14 - 21
Spirit self (imagination) I-organisation with the sentient soul* 21 - 28

*from 28-35: comprehension soul, and from 35-42: conscious soul, as the predominant focus.

In short, before the 21st year of life, the education goes mainly through the way
of demonstrating and imitating, pre-feeling and after-feeling, and pre-thinking
and considering. After the 21st year of life, the educator engages in the real-life
world together through the path of co-create, co-sense, and co-reflect in order to
stimulate the student’s sensing soul and enhance flash-forwards instead of flash-
backs. To promote this, the educator will be required to have an open mind and/or
spirit levelling attitude to the perceptions of their students. They can ”feed” their
students with enchanting incitements (from practice and/or real experiences) and
concept introductions rather than traditional from a to z spelled-out lectures.
Making a connection through experiential learning and reflective practitioning to
the real-life context is thus now called for and an absolute must. We do not want
to move toward a pre-prescribed reality but towards a truly experiential reality.
This is the challenge in the 21+ phase and at the same time the risk of dealing
with this in an open (and therefore not normative), explorative, and collaborative
way to provide condition-creating constructivist education.

#2 Integrative & constructivist U-nlocking

We have already discussed the term (social) constructivism in Chapter 2. This
form of knowledge inquiry is always via human and social construction, and is also
reflected in the ζ engineering position of the Odesys engineer (see the previous
section 9.1). Constructivism can also be used in pedagogy. Within this movement
(opposed to instructivism) we see the traditional teacher taking a step aside to a
new role as facilitator, connecting students with peers, prompting learning, and
reflecting on key moments based on data and observation, while students create
9.2. OPEN DESIGN LEARNING (ODL) CONCEPT 251

their own knowledge and even their very first designing pathways (”learning to
learn and design”), see Figure 9.5.
Constructivist education is human-centered, where educators strive for human
manifestation and foster the individual self to liberate (”freewill”), see as Fig-
ure 9.6. Instructivist schooling is certainly more focused on teachers and institu-
tions, where policy makers and ”rulers” create processes, resources, and conditions
for (their) success.

Figure 9.5: Difference instructivism versus constructivism (by Dr. J. Gerstein)

It should be clear from the foregoing that educating a 21+ Odesys engineer via
the constructivist approach thus offers the most persistent success for the future.
The condition-creating constructivist educator has basically two main facilitating
tasks to unite the open design impulse: (1) unlocking the individual’s freewill (2)
synergyzing integrative design aspects, see Figure 9.6. From this arises the basic
principle of the so-called design dialogue as part of the U model (see Chapters
1, 3-4). A design dialogue is a way of ‘intuitive thinking’ via concentrative inter-
sensing-acting on practice that brings together awareness and insights as stepping
stones towards the creation of new design. We argue here that this intuitive
thinking can also be complemented by a form of logical thinking. This logical
thought is also a formal process of the mind, supported by open glass-box models.
The living design dialogue is then an active ‘inner’ dialogue with yourself and/or
an ‘outer’ dialogue with the model representing the open design problem. See the
next section devoted separately to the U-model and its dialogue principles within
the education context.
As a final ’cautionary’ note, most students are used to instructivist education
from classical mainstream schools. They think good teaching is the same as easily
getting a good grade and passing an exam. Consequently, the grades of the best
teachers are usually directly proportional to the degree of instructivism. However,
students forget that in this way, good teacher grades are more related to being
252 CHAPTER 9. THE ART OF OPEN DESIGN LEARNING

able to show what you have learned and have been taught, rather than to being
able to persistently solve future problems and learning to learn. Would it not be
far better to have the student evaluation take place well after the course and then
call the award ’educator of your study’ instead of ’teacher of the year’ ?

Figure 9.6: Synergizing and unlocking the integrative open design impulse, a process of dialogical
transformation.

#3 Self-chosen System of Interest (SoI)

Within ODL it is the main idea that the integration of ‘knowledge and skills’ should
be able to be connected to a self-chosen System of Interest (SoI) from a real-life
context. Via this stimulus driven SoI (student’s area of interest), ’new knowledge
and skills’ (R&D) can be transformed by means of this experiential learning vehicle
into creative and new conceptions and improvements (within scope of the course
context). It is important to note that the SoI is also unknown to the educator/
constructor beforehand (at the start of a course). Thus, the student and the con-
structor co-develop new open designs. Examples of such a SOI could be a systems
service provider, a construction project, a contractor organization, etc. It is im-
portant that the student has a certain connection and interest in this SOI, and a
desire to always know more about it (e.g. a student chooses a French toll road with
a number of large bridges and engineering structures because as a young child he
used to go and stay with his grandmother near one of these bridges; he has always
been curious to know more about it, not only the technical system, but also the toll
service system. It is also important that the student can find a reflective practi-
tioner ’within this system’, an experienced and involved professional(‘buddy’) who
is able to reflect on real-life contextual matters and on the new design proposals
9.2. OPEN DESIGN LEARNING (ODL) CONCEPT 253

that are made. These open designs or disclosures are things for the student that
he did not know or could not do yesterday and will be able to do tomorrow within
a systems thinking context. Students transform or design (designs) existing know-
ledge and/or skills into new insights or improvement conceptions. The self-chosen
& experiential SOI vehicle and state of the art course concepts are themselves a
prerequisite for the student to learn and develop experientially. The ODL concept
forms the bridge between the learning subject and the empirical systems within
its context.
The latter aspect in particular makes the ODL education unique in its kind
and at the same time resistant to the influence of AI tools like ChatGPT. Indeed,
it is through a unique self-selected social context that students transform course
concepts and make an individual (through) translation. This from an idealised
design and his individual purpose. In short, the SOI makes it possible to make
maximum use of the unique and creative human to create a future-oriented design
instead of regurgitating existing knowledge from reference material.

#4 Open living dialogue

We know from Buber and Glasl that engaging in dialogue, confronting the conflict,
doing what you say, and saying what you think is the way to reach new insights
and solutions. Even within education, we can add that what is a question or an
issue for you is usually also a question or an issue for the other person. Dare to ask
and seek an open dialogue. This is not about ’can you give me the answer to the
next question’, but rather an open-ended question where in the interspace between
people a first draft of a solution can emerge. This is the basis for learning from a
free encounter and that is the basis for co-design. This form of dialogical education
can return in dialogue sessions in which the educator (constructor) shares experi-
ences and/or reflections based on dialogue questions, meaning open questions with
an interest for the whole group. Dialogical education can also be returned in the
master class, where the student gets the chance to reflect on their design work with
the educator and the class. This requires an open attitude from both student and
educator, in which the student comes up with the ‘O’-question and the educator
moves forward together, not from authority but from authenticity. Finally, it also
requires courage from both the student who dares to share his O-question (’what
is a question for you is mostly also a question for your neighbour...’), but also from
the educator who has to make themself vulnerable and sometimes dares to say ”I
have to think about it again” without already knowing the answer. In this way, this
question becomes a joint development question. Finally, it should be noted that
dialogue is not only with another person, but also with the SoI and/or with the
open glass box model what this system can be represented by. The student reflects
or dialogues with the SoI, the model (the outer), and with themself (the inner)
254 CHAPTER 9. THE ART OF OPEN DESIGN LEARNING

with an open design impulse as a unifying result. We call this dialogical learning,
and the modeling component in particular, a form of ’play-based’ learning.

#5 ODL’s rhythm & sessions

Education is not linear but cyclical. More specifically phrased, education is pulsat-
ing, like man’s inhalation and exhalation. Education is about repeating, recalling,
working through, and living through. In addition, there is another remarkable
thing at stake; something that one practises one day is mastered better the next
day. Similarly, one can observe that upon waking up, problems from the day
before are often solved or one knows what to do. Many of the best impulses (’real-
isasations’) occur immediately after waking up. During the night, something has
happened to what one has done or learnt the day before. This means that learning
and processing continue during the night (see also Incitement 9.4). We make use
of this fact in the rhythm of the ODL course, which has a weekly cycle ‘inhalation
and exhalation’ rhythm within a ten-week cycle as a whole. Every week students
are asked to study specific concepts and apply these to their self-chosen System of
Interest (SoI) by means of a self-created response and related open-glass-box (com-
puter) models. The teachers incite the course concepts using both reference books
and dialogue questions from the students during a weekly concept and dialogue
sessions. Students can also upload their dialogue questions upfront. Moreover,
students have a weekly practical work session, which is preferably two days
after the concepts and dialogue session. During the co-creative and co-relective
work sessions, students can work on their ODL response under supervision of the
coach/constructor.

Figure 9.7: A typical conducting masterclass.

In addition, master class (MC) sessions are held, where students and con-
9.2. OPEN DESIGN LEARNING (ODL) CONCEPT 255

structors co-reflect on a group’s concept translation. A masterclass is a short event

in which a selection of groups share their work in progress (WiP), followed by feed-
back from the constructor. It is a moment of co-creation and co-relection rather
than an assessment moment. There is no formal evaluation, the goal is only to
bring the content further. It is also not a moment of formal presentation, but a
moment in which the work in progress can be interactively reflected on by both
the constructor and colleague students. It is an art-moment, like when a music
masterclass the ‘conductor is going to sit in the room and the student is going
to conduct’ (see Figure 9.7). The goal of a MC is to identify a group’s issues,
problems, ideas, and opportunities that mostly also apply to other groups. We
have experienced that masterclasses are found very useful, both by the students
who share their work and by the listening students.

#6 General ODL learning goals

In general, we can summarize the learning objectives of an ODL course as follows
so that they can be used for multiple courses. After an ODL course students
should be able:
• To understand and be familiarized with the course specific concepts, prin-
ciples, and practices, through dialogue with the constructors, navigation
through the course reference documents, and engagement with a self-chosen
real-life System of Interest (SoI).
• To apply, relate, and examine these course concepts, by dialoguing and ex-
periencing the concepts with the SoI and its reflective practice, and by (if
relevant) constructing SoI specific (computer) models.
• To (re)work this course knowledge and SoI specific concepts observations, by
transforming and linking the SoI-dialogues into new insights and/or devel-
oping improvement proposals.
• To form an individual judgement and conspect these new insights/developments.
• To demonstrate the aforementioned learning goals by creating and internal-
izing the openings/ learning outcomes in an original Open Design Learning
(ODL) response.

#7 ODL response
Education should be so vivid that there is no need for a test. A self-designed kind
of final work instead of a final test. After all, a test ‘seals’ something and are closed
systems which do not assess anything significant to the future of students, because
no one knows how to assess or measure the key factors to the future success of
any person. Moreover, doing something right confirms what we already know,
which has some value but contributes nothing to future learning. This is why
we are working towards an open-ended deliverable, which is the so-called Open
256 CHAPTER 9. THE ART OF OPEN DESIGN LEARNING

Design Learning response. This ODL response is a group deliverable based on the
self-chosen SoI. The ODL response is an original enabler demonstrating both the
group and personal learning and development achievements. The ODL response
illustrates how the general concepts have been transformed, linked, and evaluated
to the self-chosen SoI using logical diagrams/reviews and/or a computer model(s).
All of these, including relevant open glass box models, should be presented in a
self-chosen format (we stimulate to delivering a self created poster with annexes).
This poster can also be presented during the course as a work-in-progress in a kind
of atelier setting. It must contain a clear justification of the individual contribu-
tions of each group member. Each group member must also write an individual
Comment in which they write a collegial review of a specific individual contri-
bution from the other one. Good collegial Comments make use of specific ODL
Commendation aspects (see one of the other basic ODL foundations). Finally,
everything in the ODL response is intended to be unique, new, and completely
proprietary to the SoI and its context. Existing knowledge from reference material
therefore has no place in an ODL response.

Figure 9.8: A facilitating U-shaped swimming stick.

To accommodate students somewhat with this rather free ODL form (which
they are mostly not used to from classical instructivist ‘spoon-feeding’ teaching),
we typically make an auxiliary table with so-called ODL building blocks, see an
engineering asset management course example in Figure 9.9. This is not a pre-
scribed outline of the ODL reponse but serves as a reference point and auxiliary
structure for the student (getting them ‘water-free’ and like an ongoing ‘stick to
stay above water’, see Figure 9.8).
9.2. OPEN DESIGN LEARNING (ODL) CONCEPT 257

Figure 9.9: ODL response building blocks, an example for an engineering asset management course.

#8 ODL commendation
The Open Design Learning commendation principle will be applied as a sort of
‘grading rubric’ for the ODL response. Both the SoI content characteristics (sub-
products), and the student’s learning process are integrated within these com-
mendation principles, as summarized in Table 9.2. We call it ‘commendation’
because when we grade a response, we start from a grade of 10 and only deduct
points if aspects are missing or only partially worked out. The commendation
table also serves as a basis for the grading obtained in any subsequent dialogue of
the ODL response.

#9 ODL-CC coding language

Actually, this is not really an additional foundation, but a summary of ‘coding
language’ of ODL. We will not present a definition list, but below we list the
‘creative-common-terms’, see Figure 9.10.
258 CHAPTER 9. THE ART OF OPEN DESIGN LEARNING

Table 9.2: Commendation table and the C’s

Categories Relates to: Expressed in (the making of) the

ODL response:
Connect Learning process Showing courage, being curi-
ous and/or compassionate, being
a creative problem solver: the
commitment factor.
Construct Model / concept transformation, Showing proper concept conver-
improvement proposals and veri- sion, conceptions for improve-
fication ments, correctness in modeling.
Going the extra mile in concept
conversion: the compile factor.
Conclude Developed results, validation Showing a cyclical approach,
and reflection dealing with completeness, crit-
ical and open-end reflection of
own work: the conspection
factor.
Convey Reporting and presenting the re- Showing a crystal clear line of
sponse reasoning. Easy to comprehend.
Being concise (signal to noise ra-
tio). Not copying reference ma-
terial: the cognoscible factor.
Convince Response speaking to / arousing Being cogent and demonstrating
the imagination a critical attitude: the compel-
ling factor.

Note that the list is not limiting as, for example, essential non-.. (e.g. non-
conventional, non-conformist etc.) and/or co-... (e.g. co-creation, co-reflection
etc.) are not included. These CC terms are used in several places in the text of
this chapter and the reader can find them there. These CCs form the common
ground of ODLc and are meant to be guiding and not prescriptive (as definitions or
norms). We encourage the interested educator/student to use this coding language
to ’write your personal program’.
Ultimately, with a (coding) language usually belongs an alphabet. This is
also the case for ODL’s language. Namely, within ODL we know the expression
”U,V,W,X,Y,Z these are the letters of the ODLc alphabet.” These capital let-
ters symbolize a number of important elements of ODLc (and Odesys) and are
summarized in Table 9.3.

9.3. ODL, an act of U-nlocking

In this section, we zoom in one more time on the second foundation of ODL from
the previous section. There we have shown the importance of achieving a real
unlocking of the open design impulse through an integrative and constructivist
9.3. ODL, AN ACT OF U-NLOCKING 259

Figure 9.10: ODL’s coding language and its creative common terms.

Table 9.3: ODLc alphabet: U,V,W,X,Y,Z

U U-model; Utility; U = f (x, y) in- X X-factor, don’t be afraid of the

ner (‘ego’) development dark; Design variables U =
f (X, y)
V V-model; systems engineering Y tY, design to Y (common socio-
outer (‘eco’) development eco interests); Design constraints
U = f (x, Y )
W Double-U (ODL & Odesys) Z Zeta (capital of zeta) profes-
Double loop W = U + V ; sional; dialectical synthesis; sym-
W = ∧ + ∨; Double diamonds bol for integration

approach. We saw that for the individual student this involves an integrative pro-
cess of the mind, heart, and will. We also noticed that it is important for MSc
students that the educator engages them in the real life world through a collab-
orative and integrative path of co-create, co-sense, and co-reflect to maximally
stimulate the student’s sensing soul and enhance flash-forward open designs. A
fitting process model which unifies these issues is the U-model we described in
Chapter 3. Here we assume that the reader is familiar with theory U as described
in Chapter 1 and the further developed for Odesys U diagrams from Chapters 3
and 4. The starting point for ODL is the conceptual Figure 3.13.
260 CHAPTER 9. THE ART OF OPEN DESIGN LEARNING

Incitement 9.5 ‘Dream-education’

We very easily forget that very important processes take place at night,
even if ’sleeping on something’ or ’I’ll have to sleep on that’ is widely
practised. In the night, problems are ’digested’, as it were, by consciously
’taking them into the night’. It proves extremely fruitful to ’letting go’
of something that at first seemed difficult to solve without judgement or
decision and leave it until the next day. The solution presents itself more
easily, often with great obviousness. After a night’s sleep, you also often
feel a lot better. Logical, after all, the physical life body has had a rest
despite the organs ’working through’ the night. In this ‘breathing-out’
pause or release pause, a human can ‘dialogue’ with his healthy primal
image and can then reorient himself accordingly.
In the earlier healing mysteries, the so-called temple sleep (incubation)
made use of this idea. The mystery leader guided the patient to behold his
healthy primal image. With this recorded new imprint all the realms of the
human being then returned to awakening. This imprinting or recording
does not only happen upon awakening, it takes place several times during
the night. In the electroencephalogram of the brain, this shows itself in
the short-lived periods of REM sleep (REM = rapid eye movement). After
this, the human awakens or simply sleeps on. This imprinting is at least
as important for health as the resting of his physical body.
We might now ask whether we could not also make active use of this know-
ledge in learning and development processes? Could it be that actively
‘letting go’ could also lead to openings and ‘letting come’ ? Or in other
words, could we not ’actively’ involve the night ’re-generation’ principle
(breathing in and out) in which organs do go full steam ahead and cogni-
tion rests completely into the learning and development process? What
could this ‘night-conceiving’ mean for the design process? Could there
possibly be such a thing as a ‘mind-fullness’ process as contemplative
learning and design (‘dream-education’)? And finally, would this prin-
ciple also play a role in actively enhancing the generation of a so-called
‘aha-erlebnis’, a situation in which a person suddenly gains a new open
‘in-sight’ (a ‘gut intuition’)?

The common thread of these U-diagrams is that when the actor, in our case
the designer or learner, goes through the U, they actually go through an awareness
process of consciously disclosing/unlocking their purpose or uncovering their will
(thinking slow combined with thinking intuitive). We refer to the left side of the
U as top-down learning, and the right side as bottom-up learning. The U-process
goes from an open mind (imagination) via an open heart (inspiration) to the open
will (intuition), and then is ’renewed’ in reverse to an action of response via an
inner dialogue. This action comes from the free will where the ’contradiction’ or
reversal of impulse and motive have coincided. For the ODL U, this involves a
metamorphosis or transformation from various course concepts to an ODL response
in which these concepts are converted to the real-life contextual system of interest
9.3. ODL, AN ACT OF U-NLOCKING 261

(SoI). We will now make only some notes in addition to Chapters 3 and 4 which
are specific to ODL, followed by the new ODL model and the corresponding ODL
system diagram at the end of this section:
(#1) To the left of the U-model we see cognize instead of observe and contextualize
instead of sense, compared to the Odesys U. To the right of the U-model we see
externalize instead of conciliate and response instead of prototype, again compared
to the Odesys U. The ODL U-model thus reflects a design-based learning meta-
morphosis from concept cognition via contextualize/externalize of the concepts
within the self-chosen SoI towards the self-creation of an ODL response. This
ODL metamorphosis can be supported by the Odesys open glass box modeling
approach and the corresponding Odesys U (see Chapter 6). Note that in that case
we are actually dealing with a ‘double-U’, which we then refer to as the W- model.
(#2) The ODL threefold system diagram comprises of the sub-systems: (a) con-
ceptual, (b) contextual, and (c) purpose. Design-based learning, like design, is
cyclical. Therefore, the ODL U incorporates three open-ended design learning
loops, a spiral of: (1) Open concept– technical cycle, (2) Open context -social
cycle, and (3) Open source - purpose cycle. In other words, students follow an
open-ended design learning approach integrating the open mind, heart, and will.
(#3) We have already recognized that the ODL U actually consists of two parts
in the learning process: a so-called top down learning process and a bottom up
learning process. In other words from top-head cognition to hands-on and from
bottom-hands practicing back to head, connected via the heart. This is called pure
integrative education, a path of knowing (‘kennis’) and being competent (‘kunde’).
The emergence resulting from this knowledge/competence synthesis is the art of
designing (‘kunst’). A second interesting addition/ observation is that the heart
in the ODL case means the social context represented by a so called self-chosen
system of interest (SoI), which is a stimulus driven learning vehicle and can be
used as reflective practice. The essence is that the student transforms existing
course concepts through this self-chosen SoI into a self-created ODL response,
which consists of an appraisal or improvement proposal for that specific context.
(#4) The deepest U point also deserves some extra attention. It requires on
the one hand letting go but at the same time this letting go needs a kind of
counterforce to play (practice, test) with the concepts and new ideas in the self
chosen context (playing like a young child that learns through playing). This
play-based learning or practice becomes more natural when we add open source/
Odesys modeling to the ODL U model (see chapters 4 and 6). Dialoguing in
the now, which we do in the depth point of the U with the glass box model,
with the SoI, with the inner self (partly through the night), with the reflective
practitioner and/or the constructor, and finally playing with the concepts, can
bring the real transformation. Thus the ‘depth-point’ of the U can culminate into
an aha-erlebnis, a ‘high-point’, a pure living design dialogue impulse disclosing a
262 CHAPTER 9. THE ART OF OPEN DESIGN LEARNING

new response (’eureka-effect’). This depth-point is also characterized by a state of

chaos or confusion; in a state of being in the storm and at the same time in the
eye of the storm. Note here the intertwined definitions of chaos and/or confusion:
chaos is the confused unorganized state of primordial matter before the creation
of distinct forms. This means that the student (design-based learner) must have
the courage to inhabit this ‘dialogical chaos’ from the confidence that this is the
very condition necessary for creation; a state of being in the now with focused and
open attention. After these additional ODL U notes, we now can introduce the
full new U-model that has been developed for the purpose of open design learning
(ODL). ’The individual will become visible from the concepts, connected with the
SoI, and both the inner and open sources work towards/ in the concepts’ via a
threefold of re-converting the open concepts, re-validating the open context, and
re-uniting and/or re-purposing the open source, see Figure 9.11. And then all the
way to the open-end, we make the following art of ODL note.
We argue that according to the definition in this book (see also Chapter 2),
complex problem solving is a spiritual (mind) act of design, because we are con-
necting design systems with the living world around us. The truly meaningful
moments are those in which one recognizes why a certain solution is found to be
true. Verification is then nothing but a process after the fact, although necessary
it does not represent the core of problem solving. The moments of solution finding
are often very spiritual moments in which an individual connects with what is
real. Such an event transcends the everyday and one really does not have to be
spiritual in any sense to see and feel the extraordinary power and authenticity of
such moments. Design develops in the moments when things are problematic and
at times when more is required than technical knowledge and skills alone. While
both are necessary, they are ultimately not what matters most. A comparison can
be made with art and speech. Both human speech formation and music cannot be
created through technique alone, but it is the whole symbiosis of tones, rhythm,
and timbre through which music and speech ’emerge’. It is true that good tech-
nique is necessary to be able to speak or play music, however technique is only a
means and not an end. Art cannot be reduced to technique. To claim that design
is only technique is the same as claiming that art consists only of technical use
of inner and outer instruments. Similarly, students will experience that ODL is
an art to really internalize problem-solving potential which, from the sensing soul,
can be carried along on the personal development journey. Integrating knowing
and doing through a living idealized design dialogue, linked with the inner self
and the social real-life context, allows them to incorporate these experiences into
their body-soul system (genuine ‘memory’). This capacity may then be ‘retrieved’
in the future to solve a new problem with courage and confidence (like you can
forever invoke your ability to swim because you have made it through and are
never afraid to swim again).
9.3. ODL, AN ACT OF U-NLOCKING 263

Figure 9.11: The new ODL U-model with the ODL system diagram, as developed by Wolfert from
earlier ‘U-work’ by Glasl (1998) and Scharmer (2016).
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Open end
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Conspection & outreach

’Odesys & ODL join forces for social change. Odesys is the key to un-
locking conflicts and is capable of delivering socially responsible ’get-
ting into yes’ solutions. ODL is a pure act of design-based learning
to confront the emerging future and to become open and persistent
problem solvers. Everyone has a designer within themselves; it is the
art of Odesys & ODL to awaken this inner designer.’

If we distinguish feasible and infeasible design solutions, then we can make a

distinction between three types of design solutions: (1) capable but undesirable
design solutions which we call bridges to no-where; (2) incapable but desirable
design solutions which we call ‘bridges to no-land’; (3) capable and desirable design
solutions which we call ‘bridges to anywhere’. It is clear that only the last type
of ‘bridges’ are feasible as these bridge the socio-technical gap and are best fit for
common purpose.
In this book we introduced the Odesys methodology for transparent design/
decision support models which are crucial to offer unprecedented opportunities and
‘bridges for anywhere’ solutions. The aim of Odesys is to promote the adoption of
engineering artefacts in our future society by following an open config/space/source
design and systems integration approach supported by sound mathematical open
glass box optimisation models. This as a means to achieve well informed decision-
making leading to the best-fitting socio-technical solutions. This requires systems
thinking and a stakeholder-oriented focus to explore different solutions within an
open-ended optimisation process, uniting both capability (technological) derived
from the engineering asset’s performance and desirability (sociological) derived
from each stakeholder’s preferences.
Odesys results in an open dialogue and a co-design approach that enables
a-priori best fit-for-common-purpose design synthesis dissolutions rather than a-
posteriori design compromise absolutions. The Odesys combination of intuitive ‘U-
thinking’ and deliberative ‘thinking-slow’ made the IMAP/Preferendus method
and tool an effective and transparent design/decision support tool. It offers an
open-ended U-modeling approach for a spiral design metamorphosis of technical,

267
268 CONSPECTION & OUTREACH

social and purpose cycles, incorporating three open-ended design loops: (1) Open
config - technical concept/concreation, (2) Open space -social context/conciliation
and (3) Open source - common purpose/synthesis.
Within this book we presented a pure and a-priori socio-technical systems
design integration methodology, together with a new Integrative Maximised Ag-
gregated Preference (IMAP) synthesizing method. Furthermore, IMAP has been
integrated into the Preferendus tool, which combines state-of-the-art principles of
PFM with a specifically developed inter-generational GA synthesizing solver. Four
specific engineering systems design and planning applications have been worked
out by first using the threefold diagram to formulate the mathematical problem
statement. The resulting outcomes of these applications clearly demonstrate the
added value of IMAP/Preferendus.
Firstly, IMAP/Preferendus provides a single best fit-for-common design point,
unlike a Pareto front where a systems designer still has to choose the final design
because the front does not define a single optimal design point. This solves an
important modeling error, in addition to the fact that classical design synthesizing
methods leading to these Pareto fronts contain fundamental aggregation errors,
namely that design configurations lying on the Pareto front cannot all have the
same preference scores.
Secondly, IMAP/Preferendus returns the best design configuration in all design
applications compared to a set of single-objective design configurations and a
design configuration obtained by the classical multi-objective min-max method.
This allows IMAP to be synthesizing as a pure synthesis, multi-objective design
method that ensures a best fit-for-common-purpose point within the design space,
rather than a sub-optimal, one-sided corner point and/or best point in the feasib-
ility space only.
Finally, IMAP/Preferendus truly unites design performance functions (sup-
ply), via the level of inter-play objective functions, with stakeholder’s preference
functions (demand), synthesizing for the best fit-for-common-purpose solution and
outperforming one-sided design approaches that focus only on the technical do-
main. This means that the IMAP/Preferendus is either equal to other design
methodologies in the technical domain, but outperforms methodologies within the
management domain (see design application DA-4: a floating wind turbine install-
ation) or outperforms other design methodologies in both the technical and the
management domains (see design application DA-1: a rail level-crossing service
life design).

Further developments
Although the design applications are simplified for methodological illustration
puposes, they already demonstrate the added value of the Preferendus/IMAP in
CONSPECTION & OUTREACH 269

the field of multi-objective design optimisation. However, at the time of publish-

ing this first edition of this book, it is also being applied and further validated in
the following real-life projects: (1) the primary dredging and offshore design and
construction/production management processes of the marine contractor Boskalis
(e.g., Van Heukelum et al. (2023)); (2) the EU NRG-Storage research project
(Zhilyaev et al., (2022)); (3) several PhD/MSc thesis project applications (Shang
et al. (2021, 2023); van Eijck & Nannes, on TU Delft’s repository (2022)). In all
projects, the decision-making stakeholders (both on the project developer side and
on the contractor side) are predominantly positive about the unexpected design
solutions that they would not have been able to achieve without the use of this
computer-aided decision support system, the Preferendus, as part of the Odesys
methodology.
For the two design applications DA-3,4 (rail level-crossing and floating wind
turbine), a major extension of the models is currently underway to better fit the
design/decision problem in practice, so that more realistic design performance and
better preference functions will be included. For the floating wind application, this
means that Open- FAST, an open-source wind turbine simulation tool, is linked
via a surrogate model and integrated at the level of design performance functions.
For the level-crossing application, the modeling input will be refined at all levels
(focus on the preference and objective functions). In addition, for the floating
wind application, but also for a dredging application, validation sessions will be
carried out to refine the modeling inputs (especially the performance functions)
and to evaluate the results, especially of the new IMAP and the existing min-
max methods. This is done in the form of a serious game, using the Preferendus
as a design support ’engine’, with the aim of increasing the internal acceptance
and the link with the iterative group design engineering process. Based on these
current developments, we can formulate at least three main focuses for further
development of the Preferendus:
(#1) The output depends not only on the best possible design performance func-
tions, but also on a good reflection of human objectives and preferences. Especially
for the latter, further preference elicitation research is needed to arrive at balanced
preference functions with corresponding individual preferences as input. Finally,
further research is also required to determine stakeholder weight distributions in
an efficient and effective way where the sociocratic principles (consent principle
and preferendum) are leading.
(#2) The result of the optimisation may also be an empty design solution space:
i.e. a so-called stalemate situation. In this case, additional decision support func-
tionality will be required to support stakeholders to achieve the best possible ne-
gotiation and arrive at an acceptable design solution space.
(#3) The design performance functions are currently deterministic. However, for
more realistic applications, probabilistic design modeling techniques will need to be
270 CONSPECTION & OUTREACH

integrated, e.g. for the offshore design application, uncertainty in working hours or
operational weather slots. Improvements to the current Discrete Event Simulator
(DES) may be required, particularly for repetitive production and installation
operations.
(#4) Finally, the Odesys methodology has already been taught, and further tested
and validated in several MSc courses in Systems Engineering Design at the Faculty
of Civil Engineering & Geosciences at Delft University of Technology this year.
The purpose is to further explore the added value and potential improvements of
the Preferendus as soon as possible. Within these courses, MSc students develop a
Preferendus/IMAP-based model of a self-selected real-life system of interest as part
of the so-called Open Design Learning (ODL) response (see Wolfert et al. (2022)
and Chapter 9). Some findings from these courses have already been incorporated
into the current Preferendus code, see the Odesys Github for further details

Future applications
The Preferendus and the IMAP method will be applied in future systems design
and management applications, including (1) dynamic preference and performance
based mitigation control (MitC) of large construction project, in combination with
discrete event simulation (DES), (2) optimal socio-technical planning of flood
defence system reinforcements, and (3) a far-reaching improvement on playing
a Preferendus-based serious game, incorporating improved preference elicitation
techniques and or expert judgment and the application of a stalemate solver (see
e.g. Kammouh et al. (2022) and Klerk et al. (2021) for the actual state of the art
planning and control solutions without an IMAP/Preferendus application).
Furthermore, the added value within the so-called concurrent engineering and
design developments in the field of ’Early Contractor Involvement’ is also investig-
ated. In particular, the Preferendus will be used to support and evaluate the new
so-called two-phase contract for infrastructure projects, in which the activities of
the Dutch national infrastructure service provider (RWS) and its contractors are
further intertwined, to avoid major contract changes that are the result of the
classic serial, non-participative design and engineering process.
The future developments described above are, at the time of publishing this
first edition of this book, envisaged to materialize in at least the following three
projects.

Preferendus based Mitigation Controller (MitC) “Construction projects

management require dynamic mitigation control ensuring the project’s timely com-
pletion by a best fit for common purpose strategy for all stakeholders. Current
mitigation approaches are usually performed by an iterative Monte Carlo (MC)
analysis focusing on lowest cost strategies which do not reflect (1) the project
CONSPECTION & OUTREACH 271

manager’s goal-oriented behavior (2) automated network restructuring potential

(3) multi-dimensional optimisation criteria for best fitting mitigation strategies.
Therefore the development statement within this paper is to design a method
and implementation tool that properly dissolves all of the aforementioned short-
comings ensuring the project’s completion date by finding the most effective and
efficient mitigation strategy. For this purpose, the Mitigation Controller (MitC)
has been developed using an integrative approach of non-linear optimisation tech-
niques, probabilistic Monte Carlo simulation, and preference function modeling.
MitC’s applicability is demonstrated using a recent tunnel construction within
one of the largest Dutch infrastructure construction projects showing its added
value for multi-criteria decision making on-the-run. It is shown that the MitC is
a state-of-the-art decision support tool that a-priori automates and optimises the
search for the best set of mitigation strategies for common purpose rather than
a-posteriori evaluating the potentially sub-optimal and over-designed mitigation
strategies (as commonly done with modern scheduling software such as Primavera
P6). The extended MitC has proven its added value within a real-life project
context.”
This text is part of a key publications on dynamic project control, in which the
MitC is implemented, see Kammouh et. al. (2021, 2022). Within the current MitC
(see github.com/tudelft-odesys/mitc), the focus is primarily single objective
optimisation strategies (time or costs) on-the-run where different preferences and
multi-projects optimisation have not been incorporated yet. Currently this is being
developed within state-of-the-art R&D projects of the EAM group of the author,
and in particular within the project Logiquay: Adaptive Multi-Actor Multi-Modal
Closed- Loop Planning and Logistics for Renewal and Renovation of Urban Bridges
and Quay Walls (# NWA.1431.20.005). This project is continuing to integrate the
IMAP/Preferendus to enhance systems engineering optimisation with the best-fit
for common purpose methodology. Moreover, within several Boskalis project the
MitC will also be extended both with IMAP/Preferendus and DES simulation
techniques to improve multi-objective optimisation and control especially given
the dynamic workable weather windows.

Preferendus based 3C-planner “The well-being of modern societies is depend-

ent upon the functioning of their infrastructure networks. This paper introduces
the 3C concept, an integrative multi-system and multi-stakeholder optimisation
approach for managing infrastructure interventions (e.g., maintenance, renova-
tion, etc.). The proposed approach takes advantage of the benefits achieved by
grouping (i.e., optimising) intervention activities. Intervention optimisation leads
to substantial savings on both direct intervention costs (operator) and indirect
unavailability costs (society) by reducing the number of system interruptions.
The proposed optimisation approach is formalized into a structured mathemat-
272 CONSPECTION & OUTREACH

ical model that can account for the interactions between multiple infrastructure
networks and the impact on multiple stakeholders (e.g., society and infrastruc-
ture operators), and it can accommodate different types of intervention, such as
maintenance, removal, and upgrading. The different types of inter-dependencies,
within and across infrastructures, are modeled using a proposed Interaction Mat-
rix (IM). The IM allows integrating the interventions of different infrastructure
networks whose interventions are normally planned independently. Moreover, the
introduced 3C concept accounts for central interventions, which are those that
must occur at a pre-established moment, where neither delay nor advance is per-
mitted. To demonstrate the applicability of the proposed approach, an illustrative
example of a multi-system and multi-actor intervention planning is introduced.
Results show a substantial reduction in the operator and societal costs. In addition,
the optimal intervention program obtained in the analysis shows no predictable
patterns, which indicates it is a useful managerial decision support tool.”
This text is part of a key publication on engineering systems design/decision
making, in which the 3C-planner method is implemented to accommodate for
multi-system intervention optimisation of interdependent infrastructure (see Kam-
mouh et al., (2021b) and/or github.com/tudelft- odesys/3c- planner). It is noted
that for an optimal service operations plan a system thinking approach is re-
quired to arrive at a best-fit for common purpose plan. The so-called 3C-planner
method has been develop to accommodate for multi-system intervention optimisa-
tion of (interdependent) infrastructures, using traditional optimisation techniques
that are not preference based. Within the current 3C-Planner the focus will be
on multi-system service intervention planning and IMAP/Preferendus based op-
timisation, where both the mechanical behavior and trade-offs based on individual
preferences will be incorporated. Currently this is being developed within state-of-
the-art R&D projects of the EAM group of the author of this book, in particular
within the Dutch NWO perspective program Future FRM Tech: Future Flood
Risk Management Technologies for Rivers and Coasts.

Preferendus, confronting the urban planning conflict... ”Waelpolder is

an area development project between ‘s-Gravenzande and Naaldwijk in the muni-
cipality of Westland. The area will be a residential neighbourhood with a focus
on greenery. Waelpolder, together with other sub projects, is part of the Wael-
park area development. The first goal of the IMAP/Preferendus application to
Waelpolder was to investigate which design methodology, Preferendus or the Min-
max goal attainment, is best suited to support the urban design/decision making
process. The second goal for Waelpolder was to test the acceptance of the Preferen-
dus. Investigating the acceptance is linked to the entire process of the Preferendus,
starting with the input and ending with a design. The approach is accepted if the
stakeholders endorse the added value in the use of the Preferendus. With regards
CONSPECTION & OUTREACH 273

to the second goal, the stakeholders expressed that they preferred the design ob-
tained using the Preferendus method. The Min-max method optimisation results
were deemed less satisfactory for the group as a whole. Although there was a differ-
entiation in stakeholder satisfaction when using the Preferendus, the optimisation
result was more diverse and attractive.”
This text is part of a MSc students thesis project (for details on this project
application the reader is referred to the work of van Eijck &, Nannes (2022) on
TU Delft’s repository). As an example, where the Preferendus result showed a
pronounced housing differentiation, the Min-max result showed very little housing
differentiation making it a rather bland end result. With regards to the second
goal, the stakeholders showed great interest in this new approach for solving urban
design problems. The stakeholders appreciated that the model can give insights
into the consequences of certain requirements. Stakeholders did not expect that the
stated requirement would still allow for as many houses as the optimisation results
showed. The representative of the municipality wants to use the Preferendus within
the organization to show the effects of adjusting certain constraints and coefficients
(e.g. parking norms).
Overall, this project has shown promising results that make traditional Linear
Programming (LP) and/or single-objective optimisation techniques things of the
past. The Preferendus here proved itself as an ultimate conflict dissolver, where
the compromise solution was outperformed. For a next step, an improvement in
the social cycle is proposed along with a stalemate solver. The latter can ensure
that at least a transparent and objective start (perhaps as a ’start-compromise’) in
the social cycle can be made with a possible solution space in which all conflicting
interests are secured.

Outreach
The Preferendus as a decision support tool for the Open Design Systems (Odesys)
methodology introduced in this book is what Kahneman would call a ‘thinking
slow’ as opposed to ‘thinking fast’ decision system. As we saw in Chapters 1,3
and/or 4, Kahneman distinguishes between two systems that drive the way we
decide. System 1 is fast, instinctive, and emotional; System 2 is slower, more
deliberative, and more logical. The logical aspect relates to the unbiased mod-
eling output that cannot be any other than a pure reflection of all stakeholders’
preferences. It is not uncommon that the model output surprises stakeholders in
the sense that it defies instinctive preconceptions about possibilities and impossib-
ilities. In other words, applying Open Design Systems methodology allows the
creation of a design and decision model that ‘talks back’. Odesys and its Prefer-
endus is therefore congruent with the negotiation principles based on the Harvard
Negotiation Project, advocated by Fisher & Ury in their book ”Getting to Yes”,
274 CONSPECTION & OUTREACH

see Fisher (1997). These negotiation principles aim for reaching mutually satisfy-
ing solutions by focusing on stakeholder interests, rather than positions, working
together to find creative and fair solutions. In addition, Glasl’s book ”Confronting
Conflict” describes a model of conflict escalation that aids in conflict analysis, see
Glasl (1999). Appropriate reactions can be derived from this analysis. Within
this conflict model, so-called non-values and/or no-go areas also play a role. The
Odesys methodology proposed in this book can be considered as the implementa-
tion of these negotiation and confronting conflict principles within a participatory
design framework, using the Preferendus to search for the maximum of aggregated
preferences (values) within a given and constrained solution space. Even if the
confronting conflict calls for a compromise solution rather than a synthesis, the
Preferendus via the Min-max method can also provide relief.
Moreover, we learned that the use of the Odesys open glass box mathematical
models greatly helps to resolve such situations to pinpoint the exact reason why
the design or decision process got stuck. Most commonly the reason for such
a stalemate situation can be traced back to a few conflicting constraints. The
Preferendus is used to find these constraints and related stakeholders. A check is
then performed on whether these individual stakeholders are willing to relax their
constraints. If constraints can be relaxed then the design process can proceed, if
constraints cannot be relaxed then the project can be considered infeasible. In the
near future, similar project applications will be used to build a stalemate solver
within the Preferendus. The idea behind this solver is to let the Preferendus
generate alternatives that give the stakeholder insight in what or where they have
to be willing to move in order to come to a feasible solution. The Preferendus
makes the most effective and efficient proposal for this, as a true next generation
stalemate solver to ‘confronting the conflict’. The Preferendus might even be able
to support Hamelink’s invitation to disarming conversations in urban spaces, as
one of the approaches to preventing mass media aggression, see Hamelink (2015).
Some concluding remarks. In the Odesys’s examples weights were used to ex-
press the importance of criteria but also of stakeholders. When using weights to
express the importance of stakeholders we introduce the power game. Who is
to decide what weight/power each stakeholder gets? In a sociocratic setting all
individual inputs will be taken into account and by using the consent principle
any principled and reasoned objection against the distribution of weights will be
removed. However, the rationale for choosing the weights for expressing a stake-
holder’s power in a typical design problem remains a matter for a genuine social
debate.
Before we concluded completely with the open-ending, we also want to put
down an outreach to ODL. After some 10 years of experience with this new edu-
cation concept, we are convinced that it is mature enough to cross over to other
domains. Besides being embedded in the engineering and management domain,
CONSPECTION & OUTREACH 275

ODL is also suitable for all other empirical studies. The condition is that the study
is not only focused on the accumulation of existing (research-)knowledge only, but
is open to innovation through integrative design in a real-world context. Thus, we
are convinced that with ODL, even, for example, the subject of literary history can
be studied by associating it with a ”design task” in the actual social context as an
experiential learning vehicle. It should be noted here that ODL is absolutely not a
ready-made method, but a learning concept in itself. ODL is not instruction, but a
concept with constructivist design learning principles that you have to experience
yourself and further tailor and develop specifically for your educational context.
We started this book in the Preface with some questions, the first of which read
as ”Why, so often, do we build what no one wants?” and later, ”Why, so often,
do conflicts stem from failed attempts to constructively design?” If stakeholders
dare to openly confront with the conflicts, then pure best-fit for common purpose
design solutions will become possible.
We finish this book with a final question: ”Why, so often, do decisions lead
to normative absolutions?” In other words, it is not uncommon that design pro-
cesses lead to predetermined solutions that represent what politicians or policy
makers consider to be the group optimum. The design process is in that case
not open ended or unbiased, but predetermined and normative. The methodology
we present takes human interests as starting points and are considered to reflect
each stakeholder’s preferences. The output of applying this socially responsible
design methodology is initially unknown but, from a logical point of view, because
only mathematical operations are applied to the input, non-biased and free of any
manipulation. That is Odesys’ real potential of designing ”getting into yes” dis-
solutions in many kinds of multi-stakeholder conflict or interest situations, where
so far only subjective and political judgments and preconceptions have mattered.

Figure: ‘A bridge for anywhere’, bridging the socio-technical gap.

This page intentionally left blank
Connect

If you have any questions or comments, or if you see any omissions in the book
and/or our Github site, we kindly ask you to share them with us via the websites
below.

Odesys connectors
We would be delighted if you would like to embrace Odesys and start working with
it yourself. For more inspiration, see all our creative commons and the Preferendus
at the Odesys Github:

github.com/TUDelft-Odesys/

If you have interesting and novel design applications (academic and/or industrial
context, preferably also outside the civil engineering domain) for example for a next
edition of the book, we would be grateful if you would connect with us. Should
you want support in implementing Odesys within an industrial environment, we
are more than happy to facilitate that. Please feel free to contact us via:

odesys.nl

ODL connectors
To further grow and branch out the Open Design Learning concept into the edu-
cation world, we are cooperating with our connectors who practice and tweak the
ODL concept to their specific local learning needs. We are convinced of ODL’s
potential even beyond civil engineering and management education. Together with
you we would like to form an open source community of enthusiastic ODL educa-
tion professionals, an open design school. Should you have any questions regarding
the local implementation of ODL in your education, please feel free to contact us
via:

open-design.school

277
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Appendices
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Appendix A

Research & Development Methods

Modeling and simulation (silico)0

Type Orientation Method/technique
●
(Non)-linear programming1
Development validation ●
Dynamic programming
●
Preference Function Mod.
●
Stat. meth.2/data mining
Research evaluation - ●
Neural networks
Development validation ●
Prob. Methods3/
forecasting
Mathematical
●
Analytical (PDE
continuous)
●
Numerical (finite
Mngt. Research evaluation - elements)
& Development validation ●
Discrete events
Phys. ●
System dynamics
●
Agent based
●
Adaptive pathways

●
Diagramming4
Research evaluation -
Logical ●
Functional/OCD design
Development Validation ●
Scenario validation

●
Diagramming4
Research evaluation ●
Software utilization5
Digital
Development validation ●
Software developing5

Figure A.1: Research and development methods overview.

0
Research modeling has generally a descriptive/confirmative orientation to
understand questions/hypotheses for the body of knowledge. Development has
generally a ameliorative/constructive orientation to enable problems/prototypes
for the body of products.

281
282 APP. A. RESEARCH & DEVELOPMENT METHODS

Experimenting and observation (vitro/vivo)

Type Orientation Method/technique

Mngt. ●
Statistical methods2
Research evaluation -
- True experimental ●
Serious gaming / observ.
Development Validation
vitro methods (human process)

Research evaluation - ●
Statistical methods2
Quasi6-true experimental
Mngt. Development Validation ●
Observational methods8
-
vivo ●
Statistical methods2
Pre experimental5 Research evaluation ●
Observational methods8
●
Statistical methods2
Phys.
Research evaluation - ●
Lab or mock-up /
- True experimental
Development Validation observational methods8
vitro
(physical object)
Research evaluation - ●
Statistical methods2
Phys. Quasi6-true experimental
Development Validation ●
Observational methods8
-
vivo ●
Statistical methods2
Pre experimental5 Research evaluation ●
Observational methods8

Figure A.2: Research and development methods overview.

1
Using different algorithms such as genetic algorithms, simplex algorithm,
negotiable constraints, etc.
2
Regression analysis, q-method, structured expert judgement, Multi Criteria
Decision Analysis (MCDA) (eg. Preference Function Modeling (PFM), Analytical
Hierarchy Process (AHP)), random forests, data and image processing, etc.
3
Such as Bayesian networks, Markov chains, stochastic processes, etc.
4
Frameworks, process flow charts, organization models, breakdown structures,
swimming lanes, relation diagrams, etc.
5
Object models (e.g. UML), entity relationship models or XML schemas or
other computer programming languages techniques (Python, semantic web design,
JSON, etc.)
6
Could also be performed as a pre-modeling context analysis
7
Quasi is like a true experiment, a quasi-experimental design aims to establish
a cause-and-effect relationship between an independent and dependent variable.
However, unlike a true experiment, a quasi-experiment does not rely on random
assignment. Instead, subjects are assigned to groups based on non-random criteria.
8
Active and structured data and information acquisition from a primary source
(objects/human) that also involves observing behavior in the environment in which
it typically occurs (structured, controlled, naturalistic, participative): e.g. sensors,
inter-views, audits etc. It also contains a specific research method to observe
the impact of human actions named action research: i.e., action research is a
philosophy and methodology of research generally applied in the social sciences.
 283

It seeks transformative change through the simultaneous process of taking action

in vivo and doing research, which are linked together by critical reflection.
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Appendix B

Linear versus non-linear optimisation

When it comes to optimisation problems with multiple objectives, three different
approaches can be taken:
• A-priori – preferences of stakeholders are defined before the beginning of the
optimisation process.
• A-posteriori – preferences are defined after the optimisation process is com-
plete and a set of possible solutions is found.
• Interactive – a combination of the abovementioned methods where prefer-
ences are provided during the optimisation run.
Within this course, you will only be working with a-priori methods. As men-
tioned, within those methods preferences of stakeholders are being collected and
introduced into optimisation before running it. When preferences – expressed
as weights and preference functions – are collected, we can transform our multi-
objective optimisation problem into a single objective one. This, in turn, enables
us to use single-objective optimisation algorithms that are less complex and faster
(optimisations with multiple objectives are essentially running multiple single-
objective optimisations within a single run).
There are multiple optimisation algorithms available for solving single-objective
optimisation problems and it is very important to understand what kind of problem
you are dealing with. The following characteristics of the problem should be
considered:
• What objective function you are dealing with - linear/non-linear, continu-
ous/intermittent, differentiable/black-box? If the objective function is non-
linear, is it convex or non-convex?
• What kind of variables do you have - real, integer, Boolean? Do they have
bounds?
• Do you have constraints?
• If you have constraints, what kind of constraint function you are dealing
with? (The characterization is the same as for the objective function)

285
286 APP. B. LINEAR VERSUS NON-LINEAR OPTIMISATION

The very first thing to think of is if your problem is linear or not. Linear
problems are those problems where both, objective and constraints, are linear. If
either the objective function or one of the constraints is non-linear, your problem is
non-linear. Linear problems are simple to solve and you will always get a definitive
solution to your optimisation problem. However, when it comes to non-linear
optimisations, things become more complicated.
Within non-linear optimisation, non-linear functions are generally divided into
two subcategories: convex and non-convex. Take a look at Figure B.1 depicting
two functions – one convex and one non-convex. By definition, a function is
convex if when you draw a straight line between any two points of this function,
the resulting line will lie above every single function point within this interval.
Or, in other words, the resulting straight line will not intersect with the function
graph (see the red dotted line in the figure).

Figure B.1: Convex and non-convex functions.

non-convex functions introduce another complication to optimisations –multiple

optima. Look at the figure above, at the non-convex function. Let’s say we want
to minimize it. It has two minima – one local and one global. Looking at the
figure, you can clearly say which point is lower (and, thus, better) because you
can see both of them. However, when running optimisations, we don’t see the
whole picture and basically are trying to find the best option while being blind-
folded. We can first find the local optima and stop there thinking it is the best
because we don’t know that another one exists and is better. When it comes to
convex problems, they always have only one optimum and this optimum is global.
B.1. LINEAR OPTIMISATION ALGORITHMS 287

Following this logic, non-linear optimisation algorithms can be divided into local
(suitable for convex problems, looking for a local optimum) and global (suitable
for non-convex problems, looking for a global optimum).
As it was mentioned, the number of existing optimisation algorithms is very
large. Similar can be said about the ways of classifying those. Figure B.2 provides
a simplified classification of optimisation algorithms and further in the text you
can find a short description of each category. Be aware that this is not a definitive
guide to optimisation algorithms and that the landscape is much more broad and
complex. However, a deep dive into optimisation concepts and mathematics is
beyond the scope of this book.

Figure B.2: Classification of optimisation algorithms.

B.1. Linear optimisation algorithms

Linear optimisation algorithms are dealing with problems where both, objective
and constraints, are linear.
Those algorithms are very fast. Different variations are available and can
handle mixed-integer problems (problems where some of the variables are integer
and some are real-valued) as well as equality and inequality constraints. However,
the applicability of those algorithms is limited since not many real-world optim-
isation problems are strictly linear. For the linear Examples in this book we used
the following linear solver: docs.scipy.org/doc/scipy/reference/generated/
scipy.optimize.minimize.html.
288 APP. B. LINEAR VERSUS NON-LINEAR OPTIMISATION

B.2. Non-linear optimisation algorithms

When the objective or one of the constraints in an optimisation problem is non-
linear, it is necessary to use non-linear solving algorithms. These can be roughly
classified into local, multistart and global.

Local optimisation algorithms

As follows from their name, this category of algorithms is looking for a local
optimum. Or to be more exact, they look for any optimum that they can find
and stop as soon as they have found an optimum. It can happen that they will
find a global optimum this way but it is impossible to know and depends on
the optimisation parameters. A typical example of this group of algorithms are
gradient-based algorithms.
Figure B.3 shows the same non-convex function we used before. Let’s say we
want to minimize it. The algorithm is initiated by the user providing a feasible
starting point that lies within the solution space. Let’s say it is point A in the
figure. Then, the algorithm calculates the gradient in that point – a derivative
of the objective function. We know that in the point where the function reaches
its minimum, the first derivative is equal to zero and the second derivative is
positive. In point A our gradient is negative so the algorithm will start searching
for minimum by moving in the direction of the increasing gradient (following the
arrow in the figure). It will continue until it finds the point where the gradient
becomes equal to zero – point A’. Since the second derivative in point A’ is positive,
this is our optimum and the algorithm stops the search.
However, if we will start from point B, the algorithm will end up in point B’
which is a local minimum and will not find the global optimum. This illustrates
the key limitation of local optimisation algorithms – the optimisation result is
highly dependent on the starting point. Those algorithms can be safely applied to
convex problems but are not suitable for non-convex ones.
Depending on the complexity of the problem, it might be complicated to find
a suitable local solver. For example, while many of them accept continuous vari-
ables, much fewer work with mixed-integer problems. Not all of the algorithms
allow constraints or only allow inequality constraints. In many cases, they require
functions to be differentiable and are not working with black-box functions.

Multi-start optimisation algorithms

The idea behind multi-start algorithms is to use a local solver but instead of
starting from a single initial point, start from multiple. Then when each instance
has found a solution, those are compared between each other and the best one
is selected. This way, it is possible to explore a larger area of the solution space
and find a better solution compared to a regular local optimisation while using
B.2. NON-LINEAR OPTIMISATION ALGORITHMS 289

Figure B.3: Gradient-based optimisation.

the same algorithm. Those methods are, thus, taking an intermediate position
between local and global algorithms.

Global optimisation algorithms

Global optimisation algorithms have tools that allow them to search for the global
optimum in optimisation non-convex optimisation problems, to more efficiently
explore the solution space and to avoid getting stuck in a local optimum. However,
outside of some special cases, even global optimisation algorithms cannot guarantee
to find the global optimum for non-convex problems.
Population-based algorithms The idea behind population-based algorithms is
instead of working with a single point, they work with populations of solutions.
A population is a set of feasible solutions. It is initiated somewhat randomly in
the beginning of the optimisation (it is more complex than that but this is beyond
our scope) and then evolves as time goes by. A single step is called a generation.
Population-based algorithms are developed in a way that each new generation is
better than the previous one and, thus, is closer to the optimum. Figure B.4
illustrates an example of population-based optimisation progress. We start from
290 APP. B. LINEAR VERSUS NON-LINEAR OPTIMISATION

an initial population consisting of 10 members scattered around the solution space.

At the intermediate population, you can see it is migrating towards two minima
that this function has. In the final population, most of the members ended up
in the global optimum and some beyond it. The best member of this population
is then selected as the optimum solution (in this case, it will be one of those
6 members that ended up close to the global optimum). The ways populations
evolve depend on the algorithm and we will not go into detail on describing it here.

Figure B.4: Population-based algorithm progress (taken from Maier, Holger R., et al. ”Introductory
overview: optimisation using evolutionary algorithms and other metaheuristics.” Environmental mod-
eling & software 114 (2019): 195-213.).

The number of different population-based algorithms is immense but the most

commonly used are gGenetic Algorithm (GA), differential evolution algorithm,
particle swarm algorithm, ant colony algorithm and artificial bee algorithm.
There are differences when it comes to what kind of problems each of those
algorithms support and there are also multiple modifications and additions to each
of the algorithms. However, it is safe to say that population-based algorithms are
much more universal than local ones. For example, some genetic algorithms can be
used for solving black-box problems as well as mixed-integer problems with linear
and non-linear equality and inequality constraints.
Note that a GA is inherently stochastic in nature. The (semi-)randomness
of the initial (start) population makes it necessary to validate the optimisation
result(s) by running it multiple times with different start populations. This is to
check if there are no convergence issues that prevent the GA from resulting in an
ambivalent optimisation outcome. Moreover, within the Odesys methodology that
has to result in a best-fitting design, it is even more important to do this generic
validation step to check if we truly arrive at a single design point.
In the Examples of this book where we use the Preferendus methodology (link
between the optimisation algorithm and the Tetra) a proprietary algorithm was
B.2. NON-LINEAR OPTIMISATION ALGORITHMS 291

developed based on the fundamentals of different standard GA solvers1 . This Pref-

erendus algorithm can be found on github.com/tudelft-odesys/preferendus_
core_scripts.
Single-point algorithms As it follows from the name, this category of al-
gorithms is not operating with populations but instead with a single point. In
this regard, they are similar to local algorithms. However, in contrast to local
ones, single-point global search algorithms have features allowing them to escape
local optimums and search a larger area of the solution space. However, the result
to a high degree depends on the parameters selected. Two typical examples of this
type of algorithms are pattern search and simulated annealing.
Let’s take a quick look at how the pattern search algorithm works. The idea
behind it is very simple. Figure B.5 illustrates the search process using the pattern
algorithm. We have a starting point around which we build a pattern. An example
of a commonly used pattern is a so-called compass or cross where we have four
pattern points located right, left, above and below the central point (see panel (a)
in Figure B.5). The algorithm evaluates the function value in the central point
and all pattern points and finds the best of those (in case we want to minimize
a function, the lowest value). Then it moves the central point to the one that
had the best function value. In our example provided in Figure B.5, it is moved
North/up. Then the process is repeated. Sometimes it can happen that the central
point will have the best value compared to the pattern points. In this case, the
pattern is contracted (shrunk) and the values are evaluated again (panel (e) in
Figure B.5). The process continues until we reach termination criterion which is
normally related to the size of the pattern – when it becomes very small, there
is no feasible upgrade in results anymore since the central point almost doesn’t
move. There are versions of single-point algorithms that allow constraints and can
work with mixed-integer problems. However, they are generally not as thorough
as population-based algorithms and are better used for smaller problems. When
the search space is large, population-based algorithms perform better.
Surrogate optimisation If the objective function is computationally expensive
and takes a long time to evaluate or when it is a black-box function, surrogate
optimisation can be utilized. The idea behind surrogate optimisation is to build
a “surrogate” – a function that approximates another function that is normally
too computationally expensive. The whole surrogate optimisation process can be
divided into three stages:
1
GA sources (as also can be found on Github): Brownlee, J. (2021, March 3). Simple
genetic algorithm from scratch in Python. Machine Learning Mastery. Retrieved November
25, 2021, from https://machinelearningmastery.com/simple-genetic-algorithm-from-scratch-in-
python/. Kramer, O. (2008). Self-adaptive heuristics for evolutionary computation. Springer.
Solgi, R. M. (2020). geneticalgorithm: Genetic algorithm package for Python. GitHub. Re-
trieved April 20, 2022, from https://github.com/rmsolgi/geneticalgorithm
292 APP. B. LINEAR VERSUS NON-LINEAR OPTIMISATION

Figure B.5: Population-based algorithm progress (taken from esa.github.io/pagmo2/docs/cpp/

algorithms/compass_search.html).

• Sampling
• Surrogate function fitting
• optimisation of the surrogate
Surrogate optimisation is often used in cases where your objective function is
calculated within some dedicated software package but it is not known how exactly
it is calculated or it is known but the process is slow. For example, the energy
consumption of a building is a very important parameter and is a commonly used
objective in building design optimisation. However, it is normally being calculated
using energy simulation software such as EnergyPlus where each simulation takes
several minutes. The whole optimisation process includes many iterations and can
take days in this case. However, it is possible to build a surrogate model that would
approximate the outputs of energy simulation software and use it in optimisation.
That would greatly speed up the optimisation process.
Appendix C

Preferendus Genetic Algorithm

To find the design configuration which reflects the integrative maximum prefer-
ence aggregation (Preferendus/IMAP), it is necessary to use an optimisation al-
gorithm. Moreover, this IMAP algorithm will also need to be able to inter-operate
with Tetra, which is the Preference Function Modeling (PFM)-based Multi Cri-
teria Decision Analysis (MCDA) software tool. The algorithm of the non-linear
Tetra solver is based on minimizing the least-squares difference between the over-
all preference score and each of the individual scores (on all decision criteria) by
computing its closest counterpart (for more information on the Tetra software, see
scientificmetrics.com).
For this purpose, a Genetic Algorithm (GA) has been developed that is spe-
cifically tailored to inter-operate with Tetra and its specific features of normalized
scores and relative ranking. We will first describe these.

C.1. Normalized scores

Preference scores are expressed as number on a defined scale, here ranging from
0 to 100, where 0 reflects the ‘worst’ scoring design configuration/alternative and
100 the ‘best’. This means that when aggregated preference scores are normalized,
the best alternative will always get a score of 100 and the worst alternative will
always have a score of 0. As a GA will typically check whether the best score of the
current generation (Gn ) outperforms the previous one (Gn−1 ), normalized scores
will lead to problems in convergence because the GA can not determine whether
improvement is occurring since the best alternative always scores 100.
Also in the case of constrained problems, where the alternative with a score
of 100 might be unfeasible and should be taken out of consideration, problems
with convergence persist. As a result, it might be possible that the best feasible
design alternative will have a lower preference score in generation Gn compared to
generation Gn−1 . This is because due to normalization, the score of one alternative

293
294 APP. C. PREFERENDUS GENETIC ALGORITHM

always depends on the performance of all other alternatives. This needs to be

accounted for within the GA solver.

C.2. Rank reversal

Rank reversal, the notion that ranks might change when an alternative is added or
removed, is commonly encountered in different MCDA models, and is also present
in Tetra Wang and Luo 2009; Aires and Ferreira 2018. This phenomenon is com-
monly observed when a non-competitive (i.e., irrelevant) alternative is added or
removes from the population Aires and Ferreira 2018. In short, especially when
extreme or ’irrelevant’ (i.e., no real-life meaning) alternatives are added/removed,
rank reversal can occur, potentially leading to convergence problems in finding the
best solution by evaluating whether generation (Gn ) outperforms the previous one
(G( n − 1)). Moreover, as an initial population is generated (quasi) randomly, it is
not unlikely that extreme or irrelevant alternatives will be part of the first genera-
tion evaluated by the GA. These alternatives would never be considered in reality,
creating a discrepancy between the GA solver and real-life design alternatives that
should be mitigated to achieve convergence.

C.3. Modifications to the GA

To solve the aforementioned issues resulting from normalization and/or rank re-
versal, the following modifications were applied resulting in a so-called inter-
generational GA solver:
(1) an additional step must be added in the evaluation of a generation. After
determining the aggregated preference scores for the complete population, the
member with the highest rank is added to a list. This list contains the best
members of all generations (Gn , Gn−1 , ..., G0 ) and is evaluated separately to acquire
an aggregated preference score for all members of this list. In case the aggregated
preference score of generation Gn yields a lower score than Gn−1 , no improvements
are made. However, if the score of generation Gn equals 100, the GA has either
improved or, if the score of generation Gn−1 also equals 100, a temporary optimum
has been found.
(2) the initial population can be built from user defined initialized solutions.
These solutions can be arbitrarily chosen or guided by the single objective and/or
min-max design optimisation outcomes. Thereby, the initial population is not
(quasi) random anymore because it reflects true potential design points, reducing
the probability of non-convergence from the start. After this first starting evalu-
ation, mutation will start diversifying the population, making it again possible to
reach another optimal solution even though the initial population is directionally
determined.
Note that this implementation of ’arbitrary’ initialized solutions is also of great
C.3. MODIFICATIONS TO THE Genetic Algorithm (GA) 295

benefit for the validation of the final results. Running the same problem with
different starting points can confirm that the result is indeed optimal.
(3) at the re-evaluation of the function U (see Equation 6.1), always an addi-
tional specific re-evaluation is introduced by feeding the GA as much as possible
with potential real life design points. Here, a re-evaluation of the population is
implemented as follows, so that the very worst alternatives are left out, which re-
flect irrelevant non-competitive alternatives. This means that after this population
is evaluated, only alternatives with an aggregated preference score higher than a
specific lower limit P ∗ (which can be set by the designer, here fixed at 40) will be
re-evaluated a second time, improving GA convergence.

The three aforementioned modifications have been added to a fit for purpose
inter-generational solver GA, where key elements from standard available GA Py-
thon packages have been integrated enabling comparing the aggregated results of
one generation with another. See the data availability statement for the code of
this solver.
Note that the aforementioned modifications are the result of pragmatic engin-
eering judgment using the principle of reflection, and after validation of a multitude
of example problems. As a possible specific step for further research, it may be
of interest (partly in the perspective of improved solving speeds) to investigate
whether other optimisation algorithms than a GA might be more suitable for this
specific purpose.
This page intentionally left blank
Appendix D

A-priori versus a-posteriori methods

Multi-objective optimisation applies to decisions that need to be taken in the

presence of trade-offs between several objectives (of different stakeholders) that
are in conflict. Objectives relate to design/decision variables that stakeholders are
interested in. In terms of decision making these are the decision criteria. Several
methods have been devised to solve multi-objective optimisation problems. A
limited but most commonly used set is described below and the pros and cons of
each is summarized in a table. We distinguish between two main approaches: 1)
a-posteriori methods and 2) a-priori methods. For background information the
reader is referred to classical literature on engineering design optimisation (e.g.
Martins and Ning (2021)).

D.1. A-posteriori methods

A-posteriori methods: we determine (all the) potential solutions and make a de-
cision afterwards.

Weighted objective function

We use the ‘weighted objective function’ method, and give different values to
weights to cover all the possible combinations. We use an optimisation algorithm
to find optimal solutions. We plot the solutions we arrived at: the Pareto front.
We select a solution within the Pareto front.
Pareto optimal solution: none of the objective functions can be improved in
value without degrading another objective function values. All Pareto optimal
solutions are considered equally good. Stakeholders still need to negotiate on
selecting the design point on the Pareto front. The Pareto front is used in the
a-posteriori decision-making process.

297
298 APP. D. A-PRIORI VERSUS A-POSTERIORI METHODS

Preference Function Modeling

We determine the preference ratings of solutions we obtained by optimizing on
single objectives i.e. relevant criteria. We assign weights to the criteria. We
use the Preference Function Modeling (PFM) algorithm to determine the overall
preference ratings of solutions to determine which solution has the highest overall
preference rating. Preference ratings for decision variable values (criteria) are
determined using linear or nonlinear interpolation (curve fitting).

D.2. A-priori methods

A-priori methods: we translate the multi-objective problem into a single objective
optimisation problem.

Weighted objective function

Assign weights to criteria which define the relevance/importance of the criteria.
Use the weighted sum method to aggregate scores of candidate solutions to and use
an optimisation algorithm to find the local/global optimum solution. We use an
optimisation algorithm to find optimal solutions. We plot the solutions we arrived
at: the Pareto front. We select a solution within the Pareto front.
Pareto optimal solution: none of the objective functions can be improved in
value without degrading another objective function values. All Pareto optimal
solutions are considered equally good. Stakeholders still need to negotiate on
selecting the design point on the Pareto front. The Pareto front is used in the
a-posteriori decision-making process.

Goal attainment
Each criterion has an associated target value. We use an optimisation algorithm
to find the optimal solution by minimizing the largest difference between target
values for criteria and the values of a candidate solution. Also called the min-max
method.

Preference Function modeling

For each criterion preference function curves are defined and weights attached.
The PFM algorithm is used to aggregate scores and weights into overall prefer-
ence ratings. An optimisation algorithm is used to find the local/global optimum
solution.
Figure D.1 summarizes the pros and cons of each approach.
D.2. A-PRIORI METHODS 299

A posteriori methods

Pros Cons

●
Relatively easy to apply. ●
Mathematical operations are applied in
mathematical spaces where they are
not defined.
●
Problems with representation when
preference or utilization are ignored,
Weighted since then only weights are evaluated.
objective ●
Negotiation or a method like PFM is
function still needed to select the best fit for
(as used within purpose solution from a Pareto Front.
Pareto front) ●
Conveying / representing outcomes is
problematic when more than 3
objectives are considered.
●
Ignores the social aspect of decision-
making, which is unnatural.

●
Based on a sound mathematical ●
Aggregated alternative scores are
foundation. relative and dependent on the set of
Preference ●
Stakeholder preference is the basis of alternatives under consideration.
Function optimization. ●
Aggregation algorithm unknown.
Modeling ●
Considers the social aspect of
decision-making problems (socio-
technical optimization)

A priori methods

Pros Cons

●
Searches for a global/local optimum ●
Mathematical operations are applied in
Weighted that decision makers can accept/reject. mathematical spaces where they are
objective
●
Easy to apply. not defined.
function
●
No major problems with convergence. ●
Problems with representation when
preference or utilization is ignored,
since then only weights are evaluated.
●
Searches for a global/local optimum ●
Stakeholder preference is translated in
that decision makers can accept/reject. deviation from target value in relative
●
Relatively easy to apply. terms – linear proxy of preference.
Goal ●
Does not violate PFM theory. ●
Limited representation of a decision
attainment
problem because individual satisfaction
is considered more important than
group satisfaction.
●
Based on a sound mathematical ●
Aggregated alternative scores are
foundation. relative and dependent on the set of
●
Allows stakeholder to express non- alternatives under consideration;
linear preference functions. requires modification of optimization
Preference ●
Stakeholder preference is the basis of algorithm.
Function optimization. ●
Search algorithm convergence is
Modeling ●
Considers the social aspect problematic.
(preference) of decision-making ●
Aggregation algorithm unknown.
problems (socio-technical optimization) ●
Can be slow for large complex
●
Relative ranking of alternatives is objective functions (e.g., railway
representative for real-life DM dynamics)

Figure D.1: Overview of optimisation approaches.

This page intentionally left blank
Appendix E

Choice matrix algorithms

This book uses a variety of optimization algorithms. However, not all of them
are applicable to every design/decision problem. To give a quick overview of the
different algorithms and their applicability, Table E.1 is made, which can be found
on the next page.
The listed examples can be found on GitHub: github.com/TUDelft-Odesys/
Preferendus_core_scripts.

301
302 APP. E. CHOICE MATRIX ALGORITHMS

Table E.1: Overview of the different optimization algorithms used in this book and their applicability.

Decision Functions SODO MODO/IMAP MODO/Min-max

variables (P or O or F) (Preferendus)
xn = Linear MILP GA GA
continuous (option ‘aggregation’ = (option ‘aggregation’ =
Examples: ‘tetra‘) ‘minmax’)
xm = • Computer production
integer
xn = Non-linear GA GA GA
continuous (option ‘aggregation’ = (option ‘aggregation’ = (option ‘aggregation‘ =
None) ‘tetra’) ‘minmax’)
xm =
integer Examples: Examples: Examples:
• Dutch rail level crossing • Dutch rail level crossing • Dutch rail level crossing
• South Korean floating • German power trans- • German power trans-
wind farm mission line mission line
• Norwegian light rail • South Korean floating
• South Korean floating wind farm
wind farm

xn = xm = Linear Minimize GA GA
continuous (option ‘aggregation’ = (option ‘aggregation’ =
Examples: ‘tetra’) ‘min-max’)
• Bridge design
• Railroad maintenance Examples: Examples:
plan • Shopping mall • Shopping mall
• Shopping mall • Bridge design

xn = xm = Non-linear Minimize GA GA
continuous (option ‘aggregation’ = (option ‘aggregation’ =
Examples: ‘tetra’) ‘min-max’)
• Building design
Examples: Examples:
• Shopping mall • Supermarket
• Supermarket
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About the author

Prof.dr.ir. A.R.M. (Rogier) Wolfert has been appointed professor of engineering

asset management in the faculty of Civil Engineering and Geosciences at Delft
University of Technology since 2013. Here he has lectured within several MSc cur-
ricula and was an advisor of several PhD and MSc students. He has worked with
R&D groups at various universities and research institutes for the past 30 years,
both nationally and internationally. He gained R&D experience both at the level of
fundamental engineering design and at the level of applied engineering asset man-
agement. He is the author of several papers published in scientific journals and/or
presented at international conferences. He has acquired both governmental funds
(EU and Dutch NWO/TTW) and industrial research funds, and managed the
associated projects. Rogier has built a proven track-record for operating various
industrial management roles both within infrastructure service provider and engin-
eering projects & services contractors. Over the past 20 years, he has been involved
in the design, construction, financing, maintenance, and operation of various types
of inland and offshore infrastructures. He has contributed to the planning, devel-
opment, and management of leading projects and services contracts, all of which
have had a significant impact on Dutch society. He has extensive experience in
managing multidisciplinary and international teams with professionals from dif-
ferent cultural backgrounds. He is used to working at different levels within the
organization. He has authored several industrial reports.
As a person, Rogier is focused, goal-oriented, fast in grasping the big picture,
and able to quickly put his finger on the key problems. As a problem solver, he is
effective in implementing solutions to get results. As a systems integration thinker,
he is very much able to connect different domains and parties while retaining
their strong individual values. As an open design systems engineer, he is able to
find the golden mean and is prepared to follow creative, non-conformist, and/or
non-conventional paths for seemingly insoluble problems. He is in his element in
dynamic and complex systems environments where new solutions must be found.
He is convinced that everyone has a designer inside themselves, and his purpose is
to foster Odesys & ODL to awaken them. Last but not least, Rogier considers both
the outer mechanistic-matter observation and the inner spiritual-mind experience
as companions on his journey into the emerging future.
Rogier holds both doctor (Dr.) and master (Ir.) degrees from Delft University
of Technology. He is 53 years old, married, and has four children.

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