Existing Technology Forecasting Methodologies

Technology Forecasting Defined

A forecast is developed using techniques designed to extract information and produce

conclusions from data sets. Forecasting methods vary in the way they collect and analyze data 2
and draw conclusions. The methods used for a technology forecast are typically determined by
the availability of data and experts, the context in which the forecast will be used and the needs
of the expected users. This chapter will provide a brief history of technology forecasting, discuss
methods of assessing the value of forecasts, and give an overview of forecasting methodologies
and their applications.

History

Technology forecasting has existed in one form or another for more than a century, but it was not
until after World War II (WWII) that it began to evolve as a structured discipline. The
motivation for this evolution was the U.S. government’s desire to identify technology areas that
would have significant military importance.

In the late 1940s, the RAND Corporation was created to assist the Air Force with, among other
things, technology forecasting. In the 1950s and 1960s, RAND developed the Delphi method to
address some of the weaknesses of the judgment-based forecasting methodologies of that time,
which were based on the opinions of a panel of experts. The Delphi method offers a modified
structured process for collecting and distilling the knowledge from a group of experts by means
of a series of questionnaires interspersed with controlled opinion feedback (Adler and Ziglio,
1996). The development of the Delphi method marked an important point in the evolution of
technology forecasting because it improved the value of an entire generation of forecasts
(Linstone and Turoff, 1975). The Delphi method is still widely used today.

The use of technology forecasting in the private sector began to increase markedly during the
1960s and 1970s (Balachandra, 1980). It seems likely that the growing adoption of technology
forecasting in the private sector, as well as in government agencies outside the military, helped to
diversify the application of forecasts as well as the methodologies utilized for developing the
forecasts. The advent of more powerful computer hardware and software enabled the processing
of larger data sets and facilitated the use of forecasting methodologies that rely on data analysis
(Martino, 1999). The development of the Internet and networking in general has also expanded
the amount of data available to forecasters and improved the ease of accessing these data. Today,
technology forecasting continues to evolve as new techniques and applications are developed and
traditional techniques are improved. These newer techniques and applications are looked at later
in this chapter.

DEFINING AND MEASURING SUCCESS IN TECHNOLOGY FORECASTING

Some would argue that a good forecast is an accurate forecast. The unfortunate downside of this
argument (point of view) is that it is not possible to know whether a given forecast is accurate a
priori unless it states something already known. Accuracy, although obviously desirable, is not
necessarily required for a successful forecast. A better measure of success is the actionability of
the conclusions generated by the forecast in the same way as its content is not as important as
what decision makers do with that content.

Since the purpose of a technology forecast is to aid in decision making, a forecast may be
valuable simply if it leads to a more informed and, possibly, better decision. A forecast could
lead to decisions that reduce future surprise, but it could also inspire the organization to make
decisions that have better outcomes—for instance, to optimize its investment strategy, to pursue
a specific line of research, or to change policies to better prepare for the future. A forecast is
valuable and successful if the outcome of the decisions based on it is better than if there had been
no forecast (Vanston, 2003). Of course, as with assessing accuracy, there is no way to know
whether a decision was good without the benefit of historical perspective. This alone necessitates
taking great care in the preparation of the forecast, so that decision makers can have confidence
in the forecasting methodology and the implementation of its results.

The development of a technology forecast can be divided into three separate actions:

 Framing the problem and defining the desired outcome of the forecast,
 Gathering and analyzing the data using a variety of methodologies, and
 Interpreting the results and assembling the forecast from the available information.
Framing the problem concisely is the first step in generating a forecast. This has taken the form
of a question to be answered. For example, a long-range Delphi forecast reported by RAND in
1964 asked participants to list scientific breakthroughs they regarded as both urgently needed
and feasible within the next 50 years (Gordon and Helmer, 1964).

In addition to devising a well-defined statement of task, it is also important to ensure that all
concerned parties understand what the ultimate outcome, or deliverable, of the forecast will be.
In many cases, the forecaster

and the decision maker are not the same individual. If a forecast is to be successful, the decision
maker needs to be provided with a product consistent with what was expected when the process
was initiated. One of the best ways to assure that this happens is to involve the decision maker in
the forecasting, so he or she is aware of the underlying assumptions and deliverables and feels
ownership in the process.3

Data are the backbone of any forecast, and the most important characteristic of a data set is its
credibility. Using credible data increases the probability that a forecast will be valuable and that
better decisions will be made from it. Data can come in a variety of forms, but the data used in
forecasting are of two types: statistical and expert opinion. The Vanstons have provided criteria
for assessing both data types before they are used in a forecast (Vanston and Vanston, 2004).

For statistical data, the criteria are these:

 Currency. Is the timeliness of the data consistent with the scope and type of forecast?
Historical data are valuable for many types of forecasts, but care should be taken to
ensure that the data are sufficiently current, particularly when forecasting in dynamic
sectors such as information technology.
 Completeness. Are the data complete enough for the forecaster(s) to consider all of the
information relevant to an informed forecast?
 Potential bias. Bias is common, and care must be taken to examine how data are
generated and to understand what biases may exist. For instance, bias can be expected
when gathering data presented by sources who have a specific interest in the way the data
are interpreted (Dennis, 1987).
  Gathering technique. The technique used to gather data can influence the content. For
example, subtle changes in the wording of the questions in opinion polls may produce
substantially different results.
 Relevancy. Does a piece of data have an impact on the outcome of the forecast? If not, it
should not be included.

For data derived from expert opinion, the criteria are these:

 Qualifications of the experts. Experts should be carefully chosen to provide input to

forecasts based on their demonstrated knowledge in an area relevant to the forecast. It
should be noted that some of the best experts may not be those whose expertise or
credentials are well advertised.
 Bias. As do statistical data, opinions may also contain bias.
 Balance. A range of expertise is necessary to provide different and, where appropriate,
multidisciplinary and cross-cultural viewpoints.

Data used in a forecast should be scrutinized thoroughly. This scrutiny should not necessarily
focus on accuracy, although that may be one of the criteria, but should aim to understand the
relative strengths and weaknesses of the data using a structured evaluation process. As was
already mentioned, it is not possible to ascertain whether a given forecast will result in good
decisions. However, the likelihood that this will occur improves when decision makers are
confident that a forecast is based on credible data that have been suitably vetted.

It is, unfortunately, possible to generate poor forecasts based on credible data. The data are an
input to the forecast, and the conclusions drawn from them depend on the forecasting
methodologies. In general, a given forecasting methodology is suited to a particular type of data
and will output a particular type of result. To improve completeness and to avoid missing
relevant information, it is best to generate forecasts using a range of methodologies and data.

Vanston offers some helpful discussion in this area (Vanston, 2003). He proposes that the
forecast be arranged into five views of the future. One view posits that the future is a logical
extension of the past. This is called an “extrapolation” and relies on techniques such as trend
analyses and learning curves to generate forecasts. A contrasting view posits that the future is too
complex to be adequately forecasted using statistical techniques, so it is likely

to rely heavily on the opinions or judgments of experts for its forecast. This is called an
“intuitive” view. The other three views are termed “pattern analysis,” “goal analysis,” and
“counter puncher.” Each type of view is associated with a particular set of methodologies and
brings a unique perspective to the forecast. Vanston and his colleagues propose that it is
advantageous to examine the problem from at least two of the five views. This multi view
approach obviously benefits from using a wider range of data collection and analysis methods for
a single forecast. Because the problem has been addressed from several different angles, this
approach increases the confidence decision makers can have in the final product. Joseph Martino
proposed considering an even broader set of dimensions, including technological, economic,
managerial, political, social, cultural, intellectual, religious, and ecological (Martino, 1983).
Vanston and Martino share the belief that forecasts must be made from more than one
perspective to be reasonably assured of being useful.

TECHNOLOGY FORECASTING METHODOLOGIES

As was discussed earlier, technology forecasting methodologies are processes used to analyze,
present, and in some cases, gather data. Forecasting methodologies are of four types:

 Judgmental or intuitive methods,

 Extrapolation and trend analysis,
 Models, and
 Scenarios and simulations.

Judgmental or Intuitive Methods

Judgmental methods fundamentally rely on opinion to generate a forecast. Typically the opinion
is from an expert or panel of experts having knowledge in fields that are relevant to the forecast.
In its simplest form, the method asks a single expert to generate a forecast based on his or her
own intuition. Sometimes called a “genius forecast,” it is largely dependent on the individual and
is particularly vulnerable to bias. The potential for bias may be reduced by incorporating the
opinions of multiple experts in a forecast, which also has the benefit of improving balance. This
method of group forecasting was used in early reports such as Toward New Horizons (von
Karman, 1945).

Forecasts produced by groups have several drawbacks. First, the outcome of the process may be
adversely influenced by a dominant individual, who through force of personality, outspokenness,
or coercion would cause other group members to adjust their own opinions. Second, group
discussions may touch on much information that is not relevant to the forecast but that
nonetheless affects the outcome. Lastly, groupthink 4 can occur when forecasts are generated by
groups that interact openly. The shortcomings of group forecasts led to the development of more
structured approaches. Among these is the Delphi method, developed by the RAND Corporation
in the late 1940s.

The Delphi Method

The Delphi method is a structured approach to eliciting forecasts from groups of experts, with an
emphasis on producing an informed consensus view of the most probable future. The Delphi
method has three attributes—anonymity, controlled feedback, and statistical group response 5—
that are designed to minimize any detrimental effects of group interaction (Dalkey, 1967). In
practice, a Delphi study begins with a questionnaire soliciting input on a topic. Participants are
also asked to provide a supporting argument for their responses. The questionnaires are
collected, responses summarized, and an anonymous summary of the experts’ forecasts is
resubmitted to the

participants, who are then asked if they would care to modify their initial responses based on
those of the other experts. It is believed that during this process the range of the answers will
decrease and the group will converge toward a “correct” view of the most probable future. This
process continues for several rounds, until the results reach predefined stop criteria. These stop
criteria can be the number of rounds, the achievement of consensus, or the stability of results
(Rowe and Wright, 1999).

The advantages of the Delphi method are that it can address a wide variety of topics, does not
require a group to physically meet, and is relatively inexpensive and quick to employ. Delphi
studies provide valuable insights regardless of their relation to the status quo. In such studies,
decision makers need to understand the reasoning behind the responses to the questions. A
potential disadvantage of the Delphi method is its emphasis on achieving consensus (Dalkey et
al., 1969). Some researchers believe that potentially valuable information is suppressed for the
sake of achieving a representative group opinion (Stewart, 1987).

Because Delphi surveys are topically flexible and can be carried out relatively easily and rapidly,
they are particularly well suited to a persistent forecasting system. One might imagine that
Delphi surveys could be used in this setting to update forecasts at regular intervals or in response
to changes in the data on which the forecasts are based.

Extrapolation and Trend Analysis

Extrapolation and trend analysis rely on historical data to gain insight into future developments.
This type of forecast assumes that the future represents a logical extension of the past and that
predictions can be made by identifying and extrapolating the appropriate trends from the
available data. This type of forecasting can work well in certain situations, but the driving forces
that shaped the historical trends must be carefully considered. If these drivers change
substantially it may be more difficult to generate meaningful forecasts from historical data by
extrapolation. Trend extrapolation, substitution analysis, analogies, and morphological analysis
are four different forecasting approaches that rely on historical data.

Trend Extrapolation

In trend extrapolation, data sets are analyzed with an eye to identifying relevant trends that can
be extended in time to predict capability. Tracking changes in the measurements of interest is
particularly useful. For example, Moore’s law holds that the historical rate of improvement of
computer processing capability is a predictor of future performance (Moore, 1965). Several
approaches to trend extrapolation have been developed over the years.

Gompertz and Fisher-Pry Substitution Analysis

Gompertz and Fisher-Pry substitution analysis is based on the observation that new technologies
tend to follow a specific trend as they are deployed, developed, and reach maturity or market
saturation. This trend is called a growth curve or S-curve (Kuznets, 1930). Gompertz and Fisher-
Pry analyses are two techniques suited to fitting historical trend data to predict, among other
things, when products are nearing maturity and likely to be replaced by new technology (Fisher
and Pry, 1970; Lenz, 1970).

Analogies

Forecasting by analogy involves identifying past situations or technologies similar to the one of
current interest and using historical data to project future developments. Research has shown that
the accuracy of this forecasting technique can be improved by using a structured approach to
identify the best analogies to use, wherein several possible analogies are identified and rated with
respect to their relevance to the topic of interest (Green and Armstrong, 2004).

Green and Armstrong proposed a five-step structured judgmental process. The first step is to
have an administrator of the forecast define the target situation. An accurate and comprehensive
definition is generated based on

advice from unbiased experts or from experts with opposing biases. When feasible, a list of
possible outcomes for the target is generated. The next step is to have the administrator select
experts who are likely to know about situations that are similar to the target situation. Based on
prior research, it is suggested that at least five experts participate (Armstrong, 2001). Once
selected, experts are asked to identify and describe as many analogies as they can without
considering the extent of the similarity to the target situation. Experts then rate how similar the
analogies are to the target situation and match the outcomes of the analogies with possible
outcomes of the target. An administrator would use a set of predefined rules to derive a forecast
from the experts’ information. Predefined rules promote logical consistency and replicability of
the forecast. An example of a rule could be to select the analogy that the experts rated as the
most similar to the target and adopt the outcome implied by that analogy as the forecast (Green
and Armstrong, 2007).

Morphological Analysis (TRIZ)

An understanding of how technologies evolve over time can be used to project future
developments. One technique, called TRIZ (from the Russian teoriya resheniya izobretatelskikh
zadatch, or the “inventor’s problem-solving theory”), uses the Laws of Technological Evolution,
which describe how technologies change throughout their lifetimes because of innovation and
other factors, leading to new products, applications, and technologies. The technique lends itself
to forecasting in that it provides a structured process for projecting the future attributes of a
present-day technology by assuming that the technology will change in accordance with the
Laws of Technological Evolution, which may be summarized as follows:

 Increasing degree of ideality. The degree of ideality is related to the cost/benefit ratio.
Decreasing price and improving benefits result in improved performance, increased
functionality, new applications, and broader adoption. The evolution of GPS from
military application to everyday consumer electronics is an example of this law.
 Nonuniform evolution of subsystems. The various parts of a system evolve based on
needs, demands, and applications, resulting in the nonuniform evolution of the
subsystem. The more complex the system, the higher the likelihood of nonuniformity of
evolution. The development rate of desktop computer subsystems is a good example of
nonuniform evolution. Processing speed, disk capacity, printing quality and speed, and
communications bandwidth have all improved at nonuniform rates.
 Transition to a higher level system. “This law explains the evolution of technological
systems as the increasing complexity of a product or feature and multi-functionality”
(Kappoth, 2007). This law can be used at the subsystem level as well, to identify whether
existing hardware and components can be used in higher-level systems and achieve more
functionality. The evolution of the microprocessor from Intel’s 4004 into today’s
multicore processor is an example of transition to a higher-level system.
 Increased flexibility. “Product trends show us the typical process of technology systems
evolution is based on the dynamization of various components, functionalities, etc.”
(Kappoth, 2007). As a technology moves from a rigid mode to a flexible mode, the
system can have greater functionality and can adapt more easily to changing parameters.
 Shortening of energy flow path. The energy flow path can become shorter when energy
changes form (for example, thermal energy is transformed into mechanical energy) or
 when other energy parameters change. The transmission of information also follows this
trend (Fey and Rivin, 2005). An example is the transition from physical transmission of
text (letters, newspapers, magazines, and books), which requires many transformational
and processing stages, to its electronic transmission (tweets, blogs, cellular phone text
messaging, e-mail, Web sites, and e-books), which requires few if any transformational
or processing stages.
 Transition from macro- to microscale. System components can be replaced by smaller
components and microstructures. The original ENAIC, built in 1946 with subsystems
based on vacuum tubes and relays, weighed 27 tons and had only a fraction of the power
of today’s ultralight laptop computers, which have silicon-based subsystems and weigh
less than 3 pounds.

The TRIZ method is applied in the following stages (Kucharavy and De Guio, 2005):6

 Analysis of system evolution. This stage involves studying the history of a technology to
determine its maturity. It generates curves for metrics related to the maturity level such as
the number of related inventions, the level of technical sophistication, and the S-curve,
describing the cost/benefit ratio of the technology. Analysis of these curves can help to
predict when one technology is likely to be replaced by another.

 Roadmapping. This is the application of the above laws to forecast specific changes
(innovations) related to the technology.
 Problem formulation. The engineering problems that must be addressed to realize the
evolutionary changes predicted in the roadmapping stage are then identified. It is in this
stage that technological breakthroughs needed to realize future technologies are specified.
 Problem solving. Many forecasts would terminate in the problem formulation stage since
it is generally not the purpose of a forecast to produce inventions. In spite of this, TRIZ
often continues. This last stage involves an attempt to solve the engineering problems
associated with the evolution of a technology. Although the attempt might not result in an
actual invention, it is likely to come up with valuable information on research directions
and the probability of eventual success in overcoming technological hurdles.
Models

These methods are analogous to developing and solving a set of equations describing some
physical phenomenon. It is assumed sufficient information is available to construct and solve a
model that will lead to a forecast at some time in the future; this is sometimes referred to as a
“causal” model. The use of computers enables the construction and solution of increasingly
complex models, but the complexity is tempered by the lack of a theory describing
socioeconomic change, which introduces uncertainty. The specific forecast produced by the
model is not as important as the trends it reveals or its response to different inputs and
assumptions.

The following sections outline some model-based techniques that may be useful for forecasting
disruptive technology. Some of them were used in the past for forecasting technology, with
varying success.

Theory of Increasing Returns

Businesses that produce traditional goods may suffer from the law of diminishing returns, which
holds that as a product becomes more commonplace, its marginal opportunity cost (the cost of
foregoing one more unit of the next best alternative) increases proportionately. This is especially
true when goods become commoditized through increased competition, as has happened with
DVD players, flat screen televisions, and writable compact discs. Applying the usual laws of
economics is often sufficient for forecasting the future behavior of markets. However, modern
technology or knowledge-oriented businesses tend not to obey these laws and are instead
governed by the law of increasing returns (Arthur, 1996), which holds that networks encourage
the successful to be yet more successful. The value of a network explodes as its membership
increases, and the value explosion attracts more members, compounding the results (Kelly,
1999). A positive feedback from the market for a certain technological product is often rewarded
with a “lock-in.” Google, Facebook, and Apple’s iPhone and iPod are examples of this. A better
product is usually unable to replace an older product immediately unless the newer product
offers something substantially better in multiple dimensions, including price, quality, and
convenience of use. In contrast, this does not happen in the “goods” world, where a slightly
cheaper product is likely to threaten the incumbent product.

Although the law of increasing returns helps to model hi-tech knowledge situations, it is still
difficult to predict whether a new technology will dislodge an older product. This is because
success of the newer product depends on many factors, some not technological. Arthur mentions
that people have proposed sophisticated techniques from qualitative dynamics and probability
theory for studying the phenomenon of increasing returns and, thus, perhaps to some extent,
disruptive technologies.

Chaos Theory and Artificial Neural Networks

Clement Wang and his colleagues propose that there is a strong relationship between chaos
theory and technology evolution (Wang et al., 1999). They claim that technology evolution can
be modeled as a nonlinear process exhibiting bifurcation, transient chaos, and ordered state.
What chaos theory reveals, especially through bifurcation patterns, is that the future performance
of a system often follows a complex, repetitive pattern rather than a linear process. They further
claim that traditional forecasting techniques fail mainly because they depend

on the assumption that there is a logical structure whereby the past can be extrapolated to the
future. The authors then report that existing methods for technology forecasting have been shown
to be very vulnerable when coping with the real turbulent world (Porter et al., 1991).

Chaos theory characterizes deterministic randomness, which indeed exists in the initial stages of
technology phase transition.7 It is during this transition that a technology will have one of three
outcomes: It will have a material impact, it will incrementally improve the status quo, or it will
fail and go into oblivion. A particular technology exists in one of three states: (1) static
equilibrium, (2) instability or a state of phase transition (bifurcation), and (3) bounded
irregularity (chaos state). Bifurcations model sudden changes in qualitative behavior in
technology evolution and have been classified as pitchfork bifurcation (gradual change, such as
the switch from disk operating system [DOS] to Windows or the uniplexed information and
computing system [UNIX]); explosive bifurcation (dramatic change, such as the digital camera
disrupting the analog camera); and reverse periodic-adding bifurcation (dominant design, such as
the Blu-ray Disc superseding the high-definition digital video disc [HD-DVD] format).

Wang and colleagues propose a promising mathematical model to aid in forecasting that uses a
neural network to perform pattern recognition on available data sets. This model is useful
because chaos is very sensitive to initial conditions. The basic methodology for applying
artificial neural networks to technology forecasting follows these high-level steps: (1)
preprocessing the data to reduce the analytic load on the network, (2) training the neural
network, (3) testing the network, and (4) forecasting using the neural network. The proposed
neural network model contains input and output processing layers, as well as multiple hidden
layers connected to the input and output layers by several connections with weights that form the
memory of the network. These are trained continuously when existing and historical data are run
on the neural networks, perhaps enabling the detection of some basic patterns of technology
evolution and improvement of the quality of forecasts.

It is possible to imagine utilizing some of these approaches for modeling disruptive technology
evolution and forecasting future disruptions. They require, however, a great deal of validated
data, such as the data available from the Institute for the Future (IFTF), to produce credible
results.8 Besides needing large and diverse validated training sets, artificial neural networks
require significant computational power. Users of these networks should be mindful that these
automated approaches could amplify certain biases inherent in the algorithms and to the data set
for training. These networks can reinforce bias patterns found in the data and users, causing the
network to produce unwarranted correlations and pattern identifications. It is important that both
the algorithms and the data are reviewed frequently to reduce bias.

Influence Diagrams

An influence diagram (ID) is a compact graphical and mathematical representation of a decision

situation.9 In this approach, the cause-effect relationships and associated uncertainties of the key
factors contributing to a decision are modeled by an interconnection graph, known as an
influence diagram (Howard and Matheson, 1981). Such diagrams are generalizations of Bayesian
networks and therefore useful for solving decision-making problems and probabilistic inference
problems. They are considered improved variants of decision trees or game trees.

The formal semantics of an influence diagram are based on the construction of nodes and arcs,
which allow specifying all probabilistic independencies between key factors that are likely to
influence the success or failure of an outcome. Nodes in the diagram are categorized as (1)
decision nodes (corresponding to each decision to be made), (2) uncertainty or chance nodes
(corresponding to each uncertainty to be modeled), or (3) value or utility nodes (corresponding to
the assigned value or utility to a particular outcome in a certain state. Arcs are classified as (1)
functional arcs to a value node, indicating that one of the components of the additively separable
utility function is a function of all the nodes at their tails; (2) conditional arcs to an uncertainty
node, indicating that the uncertainty at their heads is probabilistically conditioned on all the
nodes at their tails;

FIGURE 2-2 Example of an influence diagram. SOURCE: Adapted from Lee and Bradshaw
(2004).

(3) conditional arcs to a deterministic node, indicating that the uncertainty at their heads is
deterministically conditioned on all the nodes at their tails; and (4) informational arcs to a
decision node, indicating that the decision at their heads is made with the outcome of all the
nodes at their tails known beforehand.

In an influence diagram, the decision nodes and incoming information arcs model the
alternatives; the uncertainty or deterministic nodes and incoming conditional arcs model the
probabilistic and known relationships in the information that is available; and the value nodes
and the incoming functional arcs quantify how one outcome is preferred over another. The d-
separation criterion of Bayesian networks—meaning that every node is probabilistically
independent of its nonsuccessor nodes in the graph given the outcome of its immediate
predecessor nodes in the graph—is useful in the analysis of influence diagrams.

An influence diagram can be a useful tool for modeling various factors that influence technology
evolution; however, its ability to accurately predict an outcome is dependent on the quality of
values that are assigned to uncertainty, utility, and other parameters. If data can be used to help
identify the various nodes in the influence diagrams and polled expert opinion can be funneled
into the value assignments for uncertainty and utility, then a plethora of tools known to the
decision theoretic community could be used to forecast a certain proposed event in future. The
error bars in such forecasting will obviously depend on the quality of the values that capture the
underlying interrelationships between the different factors and decision subprocesses.

Influence diagrams are an effective way to map and visualize the multiple pathways along which
technologies can evolve or from which they can emerge. Forecasters can assess the conditions
and likelihoods under which a technology may emerge, develop, and impact a market or system.
By using the decision nodes and informational arcs as signposts, they can also use influence
diagrams to help track potentially disruptive technologies.

Scenarios and Simulations

Scenarios are tools for understanding the complex interaction of a variety of forces that come
together to create an uncertain future. In essence, scenarios are stories about alternative futures
focused on the forecasting problem at hand. As a formal methodology, scenarios were first used
at the RAND Corporation in the early days of the cold war. Herman Kahn, who later founded the
Hudson Institute, pioneered their implementation as he thought through the logic of nuclear
deterrence. His controversial book On Thermonuclear War was one of the first published
applications of rigorous scenario planning (Kahn, 1960). The title of the follow-up edition,
Thinking About the Unthinkable, suggests the value of the method—that of forcing oneself to
think through alternative possibilities, even those which at first seem unthinkable (Kahn, 1962).
It was a methodology particularly well suited to a surprise-filled world. Indeed, Kahn would
often begin with a surprise-free scenario and take it from there, recognizing along the way that a
surprise-free future was in fact quite unlikely.

In technology forecasting, scenarios have been used to explore the development paths of
technologies as well as how they roll out into the world. The first case, using scenarios to
anticipate development paths, entails a study of the fundamental science or engineering and its
evolution, the tools needed to develop the technology, and the applications that will drive its
economics. To forecast the future of the semiconductor while the technology was in its infancy
required an understanding of solid state physics, silicon manufacturing, and the need for small
guidance systems on U.S. missiles, all of which were critical to the early days of the microchip.

Using scenarios to forecast how technologies will play out in the real world calls for
understanding the potential performance of the new technology and how users and other
stakeholders will apply the technology. Early efforts to forecast the future of new technologies
such as the personal computer, the cell phone, and the Internet missed the market because
forecasters did not imagine that falling prices and network effects would combine to increase the
value of the technology. These failed forecasts resulted in enormous business losses. IBM
ultimately lost the personal computer market, and AT&T, despite its early dominance, never
controlled the cell phone or the Internet market because it could not imagine the potential of the
new technology. Xerox never capitalized on its pioneering work in graphical user interfaces
(GUIs) because the company’s management never saw how GUI could be combined with the
revolution in personal computing to reshape the core of its document business

In another approach, “backcasting,” planners envision various future scenarios and then go back
to explore the paths that could lead them there from the present, 10 examining the paths, decisions,
investments, and breakthroughs along the way. Backcasting is a unique form of forecasting.
Unlike many forecasting methods that begin by analyzing current trends and needs and follow
through with a projection of the future, backcasting starts with a projection of the future and
works back to the present. The grounding in the future allows the analysis of the paths from the
present to a future to be more concrete and constrained.

Forecasting centering on backcasting would start by understanding the concerns of the

stakeholders and casting those concerns in the context of alternative futures. Scenarios could
then be envisioned that describe what the world would look like then: Sample scenarios could
include a world without oil; wars fought principally by automated forces, drones, and robots; or a
world with a single currency. Forecasters then would work backward to the present and generate
a roadmap for the future scenario. The purpose of the backcast would be to identify signposts or
tipping points that might serve as leading indicators. These signposts could be tracked with a
system that alerts users to significant events much as Google News monitors the news for topics
that interest users.

There are many challenges involved in executing an effective backcast. Matching the range of
potential alternative futures with the range of stakeholder concerns is difficult but extremely
important for making sure the backcast scenarios are relevant. The number of backcasts required
for a forecast can become unwieldy, especially if there are numerous stakeholders with diverse
needs and concerns and multiple pathways for arriving at a particular future. Backcasting
requires an imaginative expert and/or a crowd base to generate truly disruptive scenarios and
signposts.

Dynamic Simulations and War Games

Military leaders have attempted to simulate the coming battle as a training exercise since the
earliest days of organized combat. The modern war games used in technology forecasting are
another way of generating and testing scenarios. The military has war games and simulations to
test new technologies and applications to better understand how they might change military
strategy and tactics. War games are useful tools when human judgment plays an important role in
how a technology is deployed. They test an engineered or technological capability that is then
tested against the response of various actors who are actually played by live people and interact
with one another over time to shape the outcome. For example, Monsanto might wish to simulate
a political situation to anticipate the reaction to genetically modified seeds.11 Careful selection of
stakeholders involved in the simulation might inadvertently have anticipated adverse public
reaction. A significant market opportunity would be lost if the company faces global opposition
to its genetically modified rice.

Other Modern Forecasting Techniques

New capabilities and challenges lead to the creation of new forecasting techniques. For example,
the ability of the Internet to create online markets has opened new ways to integrate judgment
into a prediction market (see below for a definition). Meanwhile, the rapid advance of
engineering in sometimes surprising directions, such as sensing and manipulating matter and
energy at a nanometer scale, opens up major discontinuities in potential forecasts, posing
particularly difficult problems. The committee suggests considering the following techniques for
forecasting disruptive technologies.

Prediction Markets

Prediction markets involve treating the predictions about an event or parameter as assets to be
traded on a virtual market that can be accessed by a number of individuals (Wolfers and
Zitzewitz, 2004).12 The final market

value of the asset is taken to be indicative of its likelihood of occurring. Because prediction
markets can run constantly, they are well suited to a persistent forecasting system. The market
naturally responds to changes in the data on which it is based, so that the event’s probability and
predicted outcome are updated in real time.

Prediction markets use a structured approach to aggregate a large number of individual

predictions and opinions about the future. Each new individual prediction affects the forecasted
outcome. A prediction market automatically recomputes a forecast as soon as a new prediction is
put into the system. One advantage of prediction markets over other forecasting techniques such
as the Delphi method is that participation does not need to be managed. The participants take
part whenever they want, usually when they obtain new information about a prediction or gain an
insight into it.
Prediction markets may benefit from other forecasting techniques that generate a signpost, such
as backcasting and influence diagrams. Describing the signpost that appears when a particular
point has been reached in a prediction market may help to encourage market activity around a
prediction (Strong et al., 2007). One shortcoming of prediction markets is that it is difficult to
formulate some forecasting problems in the context of a market variable. Some believe,
moreover, that predictive markets are also not ideal for long-term disruptive technology
forecasts, where uncertainty is high, probabilistic outcomes are very small (low probability),
signals and signposts are sparse, and disruptions caused by technology and signposts can set in
rapidly and nonlinearly (Graefe and Weinhardt, 2008).

Alternate Reality Games

With the advent of the ability to create simulated worlds in a highly distributed network,
multiplayer system alternate realities have begun to emerge online.

test possibilities that do not yet exist. ARGs bring together scenarios, war games, and computer
simulations in one integrated approach. Progress is made in ARGs when both first-order changes
and second-order13 effects are observed and recorded during game play. ARGs can enhance a
forecast by revealing the impact of potential alternative future scenarios through game play and
role playing.

Online Forecasting Communities

The Internet makes it possible to create online communities like prediction markets engaged in
continuous forecasting activities. Techcast, a forecasting endeavor of George Washington
University, a research project led by William E. Halal, has been online since 1998. It has
developed into a service billing itself as a “virtual think tank” and is providing a range of
services for a fee. Like any such service, its success depends on the quality of the individuals it
brings together and its effectiveness in integrating their combined judgment. The endurance and
continuing refinement of Techcast suggests it is becoming more useful with time.

Obsolescence Forecasting
Occasionally, major technologies are made obsolete by fundamental advances in new
technology. The steam engine, for instance, gave way to electric motors to power factories, and
the steamship eliminated commercial sailing. An interesting question about the future would be
to ask how key current technologies might be made obsolete. Will the electric drive make the
gasoline engine obsolete? Will broadcast television be made obsolete by the Internet? What
would make the Internet, the cell phone, or aspirin obsolete? These are questions whose answers
can already be foreseen. Asking them may reveal the potential for major discontinuities.
Obsolescence forecasting can be considered a form of backcasting, because forecasters envision
a scenario intruded on by an obsolescing technology, then explore how it happened.

Time Frame for Technology Forecasts

It is important to understand that the time frame for a forecast will depend on the type of
technology as well as on the planning horizon of the decision maker. For example, decisions on
software may move much faster than decisions on agricultural genetic research because of
different development times and life cycles. Generally, the longer the time frame covered by a
technology forecast, the more uncertain the forecast.

Short Term

Short-term forecasts are those that focus on the near future (within 5 years of the present) to gain
an understanding of the immediate world based on a reasonably clear picture of available
technologies. Most of the critical underpinning elements are understood, and in a large fraction
of cases these forecasts support implementations. For example, a decision to invest in a
semiconductor fabrication facility is based on a clear understanding of the technologies available
within a short time frame.

Medium Term

Medium-term forecasts are for the intermediate future (typically within 5 to 10 years of the
present) and can be characterized, albeit with some gaps in information, using a fairly well
understood knowledge base of technology trends, environmental conditions, and competitive
environments. These forecasts may emerge from ongoing research programs and can take into
account understandings of current investments in manufacturing facilities or

expected outcomes from short-term R&D efforts. Assumptions derived from understanding the
environment for a particular technology can be integrated into a quantitative forecast for the
industry to use in revenue assessments and investment decisions.

Long Term

Long-term forecasts are forecasts of the deep future. The deep future is characterized by great
uncertainty in how current visions, signposts, and events will evolve and the likelihood of
unforeseen advances in technology and its applications. These forecasts are critical because they
provide scenarios to help frame long-term strategic planning efforts and decisions and can assist
in the development of a portfolio approach to long-term resource allocation.

While long-term forecasts are by nature highly uncertain, they help decision makers think about
potential futures, strategic choices, and the ramifications of disruptive technologies.

Diffusion S-Curves

The S-curve pattern is quite common in the spread of ideas, practices, and technologies, although
it rarely looks quite as pretty. The example below shows "diffusion s-curves" - How a
technology spreads through a population (in this case US households
The positive feedback loop in this case is word of mouth, and the constraints represent
fundamental barriers to certain market segments or growth such as simplicity, usability,
scalability, price, etc.

This creates smaller s-curves around adoption among specific market segments, and larger s-
curves that represent the overall market penetration of the idea, practice, or technology.

CONCLUSION

Modern technological forecasting has only been utilized since the end of WWII. In the last 50
years, technology forecasts have helped decision makers better understand potential
technological developments and diffusion paths. The range of forecasting methods has grown
and includes rigorous mathematical models, organized opinions such as those produced by the
Delphi method, and the creative output of scenarios and war games. While each method has
strengths, the committee believes no single method of technology forecasting is fully adequate
for addressing the range of issues, challenges, and needs that decision makers face today.
Instead, it believes that a combination of methods used in a persistent and open forecasting
system will improve the accuracy and usefulness of forecasts.

Download Free PDF

Technological innovations are key causal agents of surprise and disruption. In the recent past, the
United States military has encountered unexpected challenges in the battlefield due in part to the
adversary's incorporation of technologies not traditionally associated with weaponry.
Recognizing the need to broaden the scope of current technology forecasting efforts, the Office
of the Director, Defense Research and Engineering (DDR&E) and the Defense Intelligence
Agency (DIA) tasked the Committee for Forecasting Future Disruptive Technologies with
providing guidance and insight on how to build a persistent forecasting system to predict,
analyze, and reduce the impact of the most dramatically disruptive technologies. The first of two
reports, this volume analyzes existing forecasting methods and processes. It then outlines the
necessary characteristics of a comprehensive forecasting system that integrates data from diverse
sources to identify potentially game-changing technological innovations and facilitates informed
decision making by policymakers.

The committee's goal was to help the reader understand current forecasting methodologies, the
nature of disruptive technologies and the characteristics of a persistent forecasting system for
disruptive technology. Persistent Forecasting of Disruptive Technologies is a useful text for the
Department of Defense, Homeland Security, the Intelligence community and other defense
agencies across the nation.