Validation of Simulation Based Models: A Theoretical Outlook

Morvin Savio Martis

Manipal Institute of Technology, India
oceanmartis@yahoo.com
Abstract: Validation is the most incomprehensible part of developing a model. Nevetheless, no model can be accepted
unless it has passed the tests of validation, since the procedure of validation is vital to ascertain the credibility of the
model. Validation procedures are usually framework based and dynamic, but a methodical procedure can be followed by
a modeller (researcher) in order to authenticate the model. The paper starts with a discussion on the views and burning
issues by various researchers on model validation and the foundational terminology involved. The paper later highlights
on the methodology and the process of validation adopted. Reasons for the failure of the model have also been explored.
The paper finally focuses on the widely approved validation schemes (both quantitative and qualitative) and techniques
in practice, since no one test can determine the credibility and validity of a simulation model. Moreover, as the model
passes more tests (both quantitative and qualitative) the confidence in the model increases correspondingly.
Keywords: Validation, simulation, dynamic models, validation schemes, validation process, modelling.

thing as an absolutely valid model, credibility of a

model can be claimed only for the intended use of
the model or simulation and for the prescribed
conditions under which the model or simulation
has been tested (DMSO 1996). Sterman (2000)
also argue validation and verification are
impossible; the emphasis should be more on
model testing i.e. the process to build confidence
that a model is appropriate for the purpose. Some
models may be better than others; some models,
while not completely valid, possess a greater
degree of authenticity than others. Furthermore,
all models are, in a sense, wrong because there
could always be a counter test to which the model
did not conform to completely.

1. Introduction
Validation has been one of the unresolved
problems of systems modelling (Mohapatra 1987).
It is true for simulation models in general and
system dynamic models in particular. System
dynamic modelling makes use of computer
simulation (packages like Matab, Stella) to
generate the consequences for studying the
dynamic behaviour of the system. In contrast,
validations of Optimisation Models, Decision
Theory or Game Theory are often not questioned
since the solution procedures are elegant and
correct. Reasons for conceptual and simulation
models having received more criticism could be
the ease with which the models and their overall
results being understandable. Another reason
being, the simulation model of any system could
only be an approximation of the actual system, no
matter the amount of time spent on the model
building. Hence if the model produced is not a
close enough approximation to this actual
system, conclusions derived from such model are
likely to be divergent and erroneous, leading to
possible costly decision mistakes been made
(Ijeoma et al. 2001).

Nevertheless, the power of a model or modelling

technique is a function of validity, credibility, and
generality (Solberg 1992). Hence model validation
is not an option but a necessity in a dynamic
modelling scenario. Usually the simplest model,
which expresses a valid relation, will be the most
powerful; however, there is no single test that
would allow the modellers to assert that their
models have been validated. Rather, the level of
confidence in the model can increase gradually as
the model passes more tests (Forrester, and
Senge 1980). The relationships of cost (a similar
relationship holds for the amount of time) of
performing model validation and the value of a
model to the user as a function of model
confidence are shown in Figure1. As shown in the
figure, the value of the model increases as the
level of confidence in the model is increased,
correspondingly the cost of model validation also
increases.

According to Law (2001), validation can be done

for all simulation models regardless of whether
their corresponding systems exist presently or
would be built in future. Also, Kleijnen (1999) and
Sterman (1984) give insight on validation of
simulation models using statistical techniques and
reasoned that the technique applied would
depend on the availability of data in the real
system. Contradicting the above authors, some
authors have also stated that there is no such

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Reference this paper as:

Martis, M S (2006) Validation of Simulation Based Models: A Theoretical Outlook The Electronic Journal of Business
Research Methods Volume 4 Issue 1, pp 39 -46, available online at www.ejbrm.com

Electronic Journal of Business Research Methods Volume 4 Issue 1 2006 (39 - 46)

3. Viewpoints on validation

Figure 1:

The viewpoints on validation are based on

modified views of traditional validation techniques.
These characteristics of validation are listed
accordingly below:
A model should be judged for its usefulness
rather than its absolute validity.
A model cannot have absolute validity but it
should be valid for the purpose for which it is
constructed.
There can be no one test with which the
model validity can be judged.
As a model passes the various tests,
confidence in the model is enhanced.
Failing a test helps to reject a wrong
hypothesis, but passing is no guarantee that
the model is valid (Sushil 1993).
Quantitative as well as qualitative validity
criterion should be given more credence
(Forrester 1961).
Most of the information from the real system
is used to check the consistency of model
behaviour.
Rejecting a model because it fails to
reproduce an exact replica of past data is not
acceptable.
Rejecting a model because it fails to predict a
specific future event is not acceptable
because social systems operate in wide noise
frequencies.

Value, cost vs. model confidence

(Source: Sargent 2003)

Validation cannot be carried out by the modeller

(or researcher) alone, communication with the
client (or user) plays a large role in building a valid
model and establishing its credibility (Carson
1989). Another relevant issue of concern is that by
how much the model output could deviate from
system output and still remain valid (Kleindorfer et
al. 1998). Since the model created is an
approximation of the actual system, some errors
and approximations are unavoidable. Model
validation thus resides in decision between the
modeller and client; when both groups are
satisfied, the model is considered valid (Goldberg
et al. 1990).
A wide range of tests to build confidence in a
model have been developed by authors like
Forrester and Senge (1980), Barlas (1989
and1996), Khazanchi (1996) and Saysel et al.
(2004) a summary of which is presented under
Validation Schemes.

4. Methodology for validation

Validation deals with the assessment of the
comparison between sufficiently accurate
computational results from the simulation and the
actual/ hypothetical data from the system.
Validation does not specifically address how the
simulation model can be changed to improve the
agreement between the computational results and
the actual data. The fundamental strategy of
validation involves identification and quantification
of the error and uncertainty in the conceptual/
simulation models, quantification of the numerical
error in the computational solution, estimation of
the simulation uncertainty, and finally, comparison
between the computational results and the actual
data. Thus, accuracy is measured in relation to
actual/ hypothetical data, our best measure of
reality. This strategy does not assume that the
actual/ hypothetical data are more accurate than
the computational results. The strategy only
asserts that simulation results are the most faithful
reflections of reality for the purposes of validation
(AIAA 1998).

2. Validation defined
The definitions of validation as stated by different
authors are listed below:
Substantiation that a computerised model
within its domain of applicability possesses a
satisfactory range of accuracy consistent with
the intended application of the model (Sargent
2003).
Validation is the process of determining that
the model on which the simulation is based is
an acceptably accurate representation of
reality (Giannanasi et al. 2001).
Validation is the process of establishing
confidence in the usefulness of a model
(Coyle 1977).
The process of determining the degree to
which a model is an accurate representation
of the real-world from the perspective of the
intended uses of the model (DoD 2002).

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Morvin Savio Martis

5. Model validation process

6. Reasons for failure of models

Figure 2 shows the model validation process in a

simpler form. The problem entity is the system
(real or proposed e.g. dynamics of integrated
Knowledge Management and Human Resource
Management can be considered as a problem
entity (Martis 2004)) to be modelled; the
conceptual model is the mathematical/ verbal
representation (influence diagram) of the problem
entity developed for a particular study; and the
computerised model is the conceptual model
implemented on a computer (simulation model).
The inferences about the problem entity are
obtained by conducting simulations on the
computerised model in the experimentation
phase.

Some of the reasons due to which the models fail

the validation tests are enumerated below as
follows:
Model-structure- In both the conceptual model
and the simulation model mathematical
simplifications might be inadequate for
capturing complex dynamics.
Numerical solution- The solution of the
simulation model might differ dramatically
from the ideal solution.
Input values- Proper numerical values of the
inputs that describe the scenario for prediction
might be known only approximately.
Observation errors- Inaccurate observations
of real system.
System noise- Failure to recognize random
changes existent in the system.
Project management errors- These errors
revolve around project management and
related communication issues (Carson 2002).
Inappropriate simulation software either too
inflexible or too difficult to use (Law 2003).
Misinterpretation of simulation results

Error!
Operational
Validation or
Credibility

Problem
Entity
Experimentation

Conceptual
Model
Validation
Influence
Diagram

Data
Validity
Computerised
Model

Simulation
Model

Conceptual
Model

Computerised Model
Verification

7. Validation schemes
7.1 Validation scheme as proposed by
Forrester and Senge (1980):

Figure 2: Model validation process.

There are three steps in deciding if a simulation is
an accurate representation of the actual system
considered, namely, verification, validation and
credibility (Garzia et al. 1990). Conceptual model
validation is the process of determining that the
theories and assumptions underlying the
conceptual model are correct and that the model
representation of the problem entity is
reasonable for the intended purpose of the
model. Computerised model verification is the
process of determining that the model
implementation
accurately
represents
the
developers conceptual description of the model
and the solution to the model (AIAA 1998).
Operational validation is defined as determining
that the models output behaviour has sufficient
accuracy for the models intended purpose over
the domain of the models intended applicability
(Sargent 2003). Operational validity determines
the models credibility. Data validity is defined as
ensuring that the data necessary for model
building, model evaluation and conducting the
model experiments to solve the problem are
adequate and correct (Love et al. 2000).

This validation criterion is used to validate

quantitative as well as qualitative models. The
validation scheme is mainly divided into four
phases; as the model passes more tests under
every phase the confidence in the model
increases correspondingly. The validation scheme
as proposed by Forrester and Senge (1980) is
enumerated below:

7.1.1 Importance of model objective:

The validity of a model cannot be greater than the
objective set for it. Therefore, the model objective
must be a justified representation of the values
prevalent in the real system. The method of
setting model objectives by the conceptualization
of problems in the existent system seems
unstructured, unless the problem elicitation is
done under the guidance of experts from the
various subsystems existent within the system. A
model could be proven valid by a series of
methods, but the validation may be totally useless
if the objectives are wrongly set.

7.1.2 Validating model structure:

These tests help in establishing confidence in the
model structure.

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parameter values, i.e. do the modes of the

behaviour change with parameter variations?
Structural sensitivity test: Is the behaviour of the
model sensitive to reasonable structural
reformulation, i.e. do the modes of the behaviour
change with structural variations?

Tests of suitability:
Structure-Verification Test: This test is meant to
answer the following question Is the model
structure not in contradiction to the knowledge
about the structure of the real system, and have
the most relevant structures of the real system
been modelled?
Dimensional-Consistency
Test:
Do
the
dimensions of the variables in every equation
balance on each side of the equation? This test
verifies whether all equations are dimensionally
constant.
Extreme-Conditions Test: Does every equation in
the model make sense even if subjected to
extreme but possible values of variables? Policy
equations are scrutinized for their applicability in
extreme conditions.
Boundary-Adequacy Test: This test verifies
whether the model structure is appropriate for the
model purpose (Barlas 1989). Is the model
aggregation appropriate and includes all relevant
structure containing the variables and feedback
effects necessary to address the problem and suit
the purposes of the study?

Tests of consistency:
Behaviour-Reproduction Test: Here the generated
model behaviour is judged with the historical
behaviour. How well the model generated
behaviour matches observed behaviour of the real
system in terms of symptom generation,
frequency generation, relative phasing, multiple
mode, and behaviour characteristics?
Behaviour-Prediction Test: This test calls for
pattern prediction. Whether or not a model
generates qualitatively current patterns of future
patterns of future behaviour in terms of periods,
shape or other characteristics?
Behaviour-Anomaly Test: Behaviour conflicting
with the real system helps in finding obvious flaws
in the model. Does behaviour shown by the
model is conflicting with the real system behaviour
and how implausible behaviour arises if the
assumptions are altered?
Family member test: Whenever possible, attempt
should be made to build a general model of the
class of system to which a particular member
belongs. The general theory is depicted in the
structure. Parameter values are chosen to depict
a particular situation. By choosing a different set
of parameter values the model can be applied to
other situation as well.
Surprising behaviour test: Does the model under
some test circumstances produces dramatically
unexpected or surprise behaviour, not observed in
the real system? Whether such a surprise
behaviour is due to model structure or some
causes in the real system can be assigned to
such a behaviour?
Extreme-Policy Test: Does the model behave in
an expected fashion under extreme policies, even
ones that have never been observed in the real
system? If the model behaves in an expected
fashion under extreme policies, then it boosts
confidence in the model (Saysel et al. 2004).
Boundary adequacy (behaviour) test: Does the
model include the structures necessary to address
the issues for which it is designed? If an extra
model structure does not change the behaviour,
then this extra structure is not necessary.
Alternatively, if a model structure does not
reproduce desired model behaviour, it calls for
inclusion of additional model structure (Barlas
1996).
Behaviour-Sensitivity Test: Whether plausible
shifts in parameters can cause model to fail

Tests of consistency:
Face validity test: Does the model structure looks
like the real system? Is it a recognisable
representation of the real system? Does a
reasonable fit exist between the feedback
structure of the model and the essential
characteristics of the real system?
Parameter-Verification Test: Parameters and their
numerical values should have real system
equivalents. Do the parameters correspond
conceptually and numerically to real life? Are the
parameters recognisable in term of real systems,
or are some parameters contrived to balance the
equations? If the values selected for the
parameters consistent with the test information
available about the real system?
Test of utility and effectiveness:
Appropriateness for audience: Is the size of the
model, its simplicity or complexity, and its level of
aggregation or richness of detail appropriate for
the audience for the study? The more the
appropriate a model for the audience the more will
be the audiences perception of model validity.

7.1.3 Validating model behaviour:

These tests help in establishing confidence in the
model behaviour.
Tests of suitability:
Parameter sensitivity test: Is the behaviour of the
model sensitive to reasonable variations in

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behaviour tests previously passed? Here the

sensitivity of the model to changes in parameter
values is judged (Saysel et al. 2004).
Statistical tests: Does the model behaviour
statistically like data from real system? (Law and
Kelton 2000).

7.2

This validation criterion is mainly used to validate

qualitative/conceptual models and consists of a
set of criteria for validation. The criteria for
validation as suggested by Khazanchi (1996) are
as follows:
1. Is it plausible/ reasonable? This criterion is
useful
to
assess
the
apparent
reasonableness of an idea and could be
demonstrated by deduction from past
research or theories.
2. Is it feasible? A feasible concept would be
operational only if it would be open to
graphical,
mathematical,
illustrative
characterisation.
3. Is it effective? An effective conceptual
model should have the potential of serving
our scientific purposes (Kaplan 1964).
4. Is it pragmatic? This criterion emphasises
that concepts and conceptual models
should have some degree of logical selfconsistency or coherence with other
concepts and conceptual models in the
discipline (Hunt. 1990).
5. Is it empirical? Empirical content implies
that a concept or conceptual model must
have "empirical testability" (Hunt 1990).
6. Is it predictive? A conceptual model that is
predictive would, at the least, demonstrate
that given certain antecedent conditions, the
corresponding phenomenon was somehow
expected to occur.
7. Is it inter-subjectively certifiable? This
criterion states Investigators with differing
philosophical stance must be able to verify
the imputed truth content of these concepts
or
conceptual
structures
through
observation,
logical
evaluation,
or
experimentation (Hunt 1990).
8. Is it inter-methodologically certifiable? This
criterion provides that investigators using
different research methodologies must be
able to test the veracity of the concept or
conceptual model and predict the
occurrence of the same phenomenon.

Tests of utility and effectiveness:

Counter intuitive behaviour: In response to some
policies, does the model exhibit behaviour that at
first contradicts intuitions and later, with the aid of
the model, is seen as a clear implication of the
structure of the system? (Richardson et al. 1981).
Is the model capable of generating new insights
or at least the feeling of new insights, about the
nature of the problem addressed and the system
within it arises?

7.1.4 Validating policy implications:

Tests of suitability:
Policy sensitivity and robustness test: The
sensitivity of a policy with respect to change in
parameter values is judged during this test.
Whether
the
model
based
policy
recommendations change with reasonable
changes in parameter values or reasonable
alteration in equation formulations?
Tests of consistency:
Changed Behaviour Prediction Test: Whether the
model correctly predicts how behaviour of the
system will change if a governing policy is
changed?
Boundary adequacy (policy) test: Whether
modifying
the
model
boundary
(i.e.
conceptualisation of additional structure) would
alter policy recommendations arrived by using the
model?
System Improvement Test: Whether the policies
found beneficial after working with a model, when
implemented,
also
improve
real
system
behaviour?
Test of utility and effectiveness:
Implementable policy test: Can those responsible
for policy in the real system be convinced of the
value of model-based policy recommendations?
How is the real system likely to respond to the
process
of
implementation?
the
policy
recommendations should be such formulated and
argued so as to fit in the mental models of those
to whom they are addressed.

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Validation scheme as proposed by

Khazanchi (1996):

8. Other validation techniques

Combinations of these techniques are generally
used for validating a simulation model. These
tests can be used in addition to the validation
schemes in the preceding section to increase the
credibility of the model.
1. Comparison to other models: Different outputs
of the simulation model being validated are
compared to those of other valid models.

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10. Black-box validation: This test is concerned

with determining whether if the entire model is
an adequately accurate representation of the
real world (Ijeoma et al. 2001).
11. Extreme Condition Test: The model structure
and output should be reasonable for any
extreme and unlikely combination of values in
the system. For example, if in-process
inventories are zero, production output should
be zero (Sargent 2003).

2. Degenerate test: This has to do with

appropriately selecting values of the input and
internal parameters to test the degeneracy of
the models behaviour. For instance, test to
see if the average number in the queue of a
single server continues to increase with
respect to time when the arriving rate is larger
than the service rate (Ijeoma et al. 2001).
3. Events validity: The events of occurrences of
the simulation model are compared to those
of the real system to see if they are similar
e.g. verify the exit rate of employees.
4. Face validity: This has to do with asking
knowledgeable people if the system model
behaviour is reasonable (Forrester 1961).
5. Historical Data validation: The experimental
data is compared with the historical data; to
check whether if the model behaves in the
same way the system does (Balci et al. 1982).
6. Predictive validation: The model is used to
predict the systems behaviour, and then
comparison is made between the real system
behaviour and the models forecast to
determine if they are the same (Sargent
2003).
7. Schellenbergers Criteria: This include
technical validation which has to do with
identifying all divergences between the model
assumptions and perceived reality as well as
the validity of the data used, operational
validity which addresses the question of how
important these divergences are and dynamic
validation which ensures that the model will
continue being valid during its lifetime (Ijeoma
et al. 2001).
8. Scoring Model Approach: Scores (or weights)
are determined subjectively when conducting
various aspects of the validation process and
then combined to determine category scores
and an overall score for the simulation model.
A simulation model is considered valid if its
overall and category scores are greater than
some passing score(s) (Gass 1993).
9. Clarity: Clarity refers to the extent to which the
model clearly communicates the implied
causality/linkages.

9. Conclusions
As rightly coined by (DMSO 1996), validation is
both an art and a science, requiring creativity and
insight. But validation is a convoluted, multifarious
and exasperating procedure, and is unavoidable
as it is the evidence for the steadfastness and
legitimacy of the model. Moreover, no single
procedure can suit all the models. Statistical
based validation techniques have been widely
accepted among the management community.
But the problem associated with this method is
being able to determine the suitable type of
statistical procedure, which in turn depends on the
right type of data that is available for analysis.
Moreover, the amount of deviation from the real
system that is within the acceptable limits is
uncertain.
The paper has given an insight on the widely
approved validation schemes and techniques in
practice. The validation schemes can be
applicable
to
quantitative
(mathematical/
computerised) as well as qualitative (conceptual)
models. But reliability of the model can only be
ascertained as the model passes more and more
tests. Also, the decision of accepting a model as
valid cannot be left to the modeller alone,
inclusion of the client / practitioners in the
validation procedure should be ascertained.
Researchers and practitioners may find this paper
quite useful as the procedures for validation
discussed are quite generic, and hence, may be
applied to other dynamic models as well.

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