to enable business networks 805 Foresight cannot remove the uncertainties any business faces and actually invests in:
FTA cannot remove the uncertainty that surrounds its contribution to or nature of sustainable development in the wider context of its supporting network and society as a whole.
The kind of dialogue supported throughftaprovides a newparadigm able to deal with unpredictability and support decision-making effectively by using models, such as sympoiesis,
In our paper, the concept of TDS recognises the inherent uncertainties of innovation pathways. Ezra (1975) offered a TDS to help explain why solar energy innovation in residential housing applications was not notably successful.
and uncertainty and conflicting approaches to entering such uncharted territory need to be observed and managed.
versus secularism Social values Behaviour lags in sustainable development Digital natives Complexity and uncertainty Social capital brings returns Quality of life
it emerged as a grand challenge reflecting key uncertainties of senior decision-makers operating in a small state as it adapted to changing external conditions in economics and governance.
By doing so, uncertainty can also be reduced, and that is a major benefit for decision-makers, be they directors of research institutes, deans and rectors of universities, business people, or policy-makers.
They analyse whether models can be used at all in decision-making under uncertainty. In this context they claim that Exploratory Modelling
and Analysis (EMA) is a methodology for analysing dynamic and complex systems and supporting long-term decision-making under uncertainty through computational experiments.
and it deals with uncertainties by using an ensemble of different models to explore a multiplicity of plausible futures (or scenarios.
if approached correctly, instead of seeking to manage away uncertainty, FTA can accommodate it. Hence, alignment of approaches and consideration of users'perspectives,
Ignorance and uncertainty: influences on future-oriented technology analysis, Tech. Anal. Strateg. Manag. 24 (8)( 2012) 753 767.3 L. Georghiou, J. C. Harper, Rising to the challenges Reflections on Future-oriented technology analysis, Technol.
on the grounds that quantitative extrapolation from past data is not sufficient to address the uncertainties of the future
or at least understanding the uncertainties that can alter the outcomes of their policies and try to quantify them ex ante.
making us believe that the world is a much more predictable place than it really is. 4 With ontological unpredictability Tuomi refers to the theoretical incompatibility between innovation
based on the argument that we can only retrospectively know what we are talking about, due to the unpredictability of natural, behavioural and social processes that shape innovation. 387 K. Haegeman et al./
and methods of social scanning and prediction markets could be used to improve professional forecasting and foresight in an era of complex phenomena and disruptive events with high level of uncertainties.
In other words, when dealing with issues surrounded by risk, uncertainty, and ignorance typically issues being dealt with by FTA the value of models is (at least) as much in the process as in the output. 8 Another common misconception associates subjectivity and value judgement to qualitative processes,
in order to constrain uncertainty to the point where traditional tools may be used. This is however not what the FTA COMMUNITY is set out to provide.
They should also pay particular attention to validity and uncertainty of its main subjects. Validity should be measured by checking
In turn, uncertainty should be assessedwith the primary aim of differentiating between the intrinsic variability of a given phenomenon that exhibits high sensitivity to small changes (e g. networks congestion)
and the uncertainty that derives from an insufficient knowledge of complex phenomena (e g. climate change). Ultimately, what matters is that methods
and that the intrinsic uncertainties associated with such representation are documented at best. 5. 2. 2. Lack of trust One aspect of trust is that it derives from perceived credibility,
both quantitative and qualitative tools aim at better understanding possible futures and reducing uncertainty and ignorance.
This reduced uncertainty needs to be embraced and managed in order to better shape the future and prepare various actors for it.
Manage. 24 (8)( 2012) 769 782.20 D. Loveridge, O. Saritas, Ignorance and uncertainty: influences on FTA, Technol.
Although some uncertainty, defined here as any type of aberration from utter certainty 4, is taken mostly into account in traditional model-based policy-making,
uncertainty is prevalent in complex systems and policy-making related to complex issues. Policy failures are often attributable to the omission of uncertainties in policy-making 7. Policies that would be optimal for one particular scenario often fail in most other scenarios.
And policies that are optimal for dynamically complex issues at a particular point in time often fail at other moments in time.
Hence, in case of complex issues under uncertainty, there is a strong need for policies that are designed to adapt over time to new circumstances and surprises,
i e. robust adaptive policies 7, 8. In order to develop policies under uncertainty, analysts often use techniques such as exploratory scenarios 9, Delphi surveys 10,
Modeling used for policy-making under uncertainty long faced the same inability to grapple with the long-term's multiplicity of plausible futures.
Although testing parametric uncertainty is a standard practice in modeling, and the importance to present a spectrum of runs under very different hypotheses covering the range of their variation was recognized decades ago 14, p. 149,
If uncertainties are not just parametric but also relate to functional relations, model hypotheses and aspects, model structures, mental and formal models, worldviews, modeling paradigms, the effects of policies on modeled systems,
2) the probability distributions used to represent uncertainty about key variables and parameters in the mathematical representations of these conceptual models,
i e. with many different kinds of uncertainties, multiple models, a multiplicity of plausible scenarios and evaluations of these scenarios 17.
The approach starts from a conceptualization of the decision problem and the identification of the key uncertainties.
an ensemble of models is developed that explicitly allows for the exploration of the uncertainties. The behavior of the ensemble is analyzed
and troublesome or advantageous (combinations of) uncertainties are identified, stimulating policy design. Iteratively, the initial design is fine-tuned until there are no remaining troublesome (combinations of) uncertainties
or the policy is deemed satisfactory based on other grounds. This approach thus explicitly uses the multiplicity of plausible futures for policy design,
i e. the poor utilization of the potential to be prepared for uncertainties and surprises of future developments 18.
and other uncertainties in order to generate a large variety of scenarios that are used in turn to analyze complex uncertain systems,
and exploring a multiplicity of plausible scenarios by sweeping multidimensional uncertainty space. EMA could then be used to identify vulnerabilities
1) the conceptualization of the problem,(2) the identification of uncertainties (and certainties), and (3) the development of an ensemble of models that allows generating many plausible scenarios.
These sub-regions of the uncertainty space represent combinations of uncertainties that either have highly negative or highly positive effects.
If uncertainties have a positive or negative effect across all the regions then they are addressed typically best in the basic policy
while uncertainties of relevance only in particular regions are handled typically better through monitoring and associated corrective, defensive,
we use an adapted version of the Patient Rule Induction Method (PRIM) 39 42 one that can deal with categorical and continuous uncertainties
which allows distilling uncertainty subspaces with high positive match ratios for a pre-specified binary classification function and with high relative masses (above a pre-specified threshold relative to the total scenario space).
PRIM is particularly valuable for discovering troublesome subspaces of the multidimennsiona uncertainty space, and hence, for developing specific adaptive actions for adaptive policies.
which uncertainties can be handled through policies. Related to this, the approach focuses not solely on the negative side of the uncertainties,
but also Fig. 2. Iterative Adaptive Robust Design process. 411 C. Hamarat et al.//Technological forecasting & Social Change 80 (2013) 408 418 explicitly considers the opportunities that uncertainties can present.
Another difference is that RDM relies on the notion of regret and uses a modified version of the expected utility framework 44,
Such developments are characterized typically by non-linearity and uncertainty regarding technological characteristics and market adoption 48
and happens under uncertainty, and construction times are open to surprises affecting the actual completion time.
Other important uncertainties are related to learning effects on costs and technological performance. Costs and technological performance,
These uncertainties play a crucial role and need to be taken into account when analyzing the dynamics of energy transitions
Table 1 Overview of the uncertainties. Uncertainties Description Type Range or categories Initial capacities Starting value of the installed capacity of a technology Parametric Varying between 1 and 16,000 MW for different technologies Lifetimes Expected
lifetime of a technology Parametric Varying between 15 and 50 years for different technologies Delay orders of lifetimes Orders of the decommissioning delays Categorical 1st, 3rd, 10th,
/Technological forecasting & Social Change 80 (2013) 408 418 In order to explore the problem and the uncertainties of energy transitions,
. And the uncertainties taken into consideration and their corresponding ranges are displayed in Table 1. 3. 2. The ARD process illustrated 3. 2. 1. No policy
Hence, we use PRIM to identify relatively large regions in the uncertainty space that generate relatively high concentrations of undesirable results,
and the combinations of uncertainties and their values that lead to these regions. To this end, the end states for the total fraction of new technologies are classified as 1
three troublesome uncertainty subspaces that contain at least 70%of the cases of class 1 are identified.
These regions are characterized by specific combinations of uncertainties: Table 2 shows the full range of the uncertainties (first row),
and the uncertainty ranges for each of these troublesome regions (other rows). Since PRIM seeks for regions in the uncertainty space with specific characteristics,
not all of the uncertainties but only the uncertainties that determine the subspaces are shown. The lower range of the‘lifetime of Technology 1'is relevant for all three subspaces,
i e. the adoption of new sustainable technologies is hampered in combination with the other uncertainties of the subspaces by longer lifetimes of the dominant technology.
Although a low performance of Technology 2 on the‘CO2 avoidance'criterion, a high performance of Technology 1 on the‘expected cost per MWE'criterion
a short lifetime for Technology 3, and a short planning and construction time for Technology 1 also hinder the transition toward sustainability,
none of these uncertainties and their ranges are as unambiguous as the lifetime of Technology 1 (for all regions, not the lower ranges).
and the third region is determined by uncertainties related to economic growth and expected progress. 3. 2. 3. Contingency planning To redesign
this uncertainty subspace consists of acceptable scenarios in terms of CO2 avoidance even though the transition to new technologies is limited.
The second region is driven mainly by uncertainties related to Technology 2. A shorter lifetime, lower performance of CO2 avoidance,
and this‘adaptive policy'ensemble over the same uncertainty space, i e. using the same experimental design.
Uncertainties and surprises are inevitable and intrinsic to FTA projects. The adequate handling of uncertainty is thus of prime importance.
Using FTA for planning for action is one area where the handling of uncertainty is crucial.
Here, the goal should be to aim for plans that are adequate across the multiplicity of plausible future worlds.
and assessed for their consequences. 5. Conclusions We have proposed an iterativemodel-based approach for developing adaptive policies under uncertainty.
There is a growing awareness about the need for handling uncertainty explicitly in decision-making. The recent financial and economic woes have rekindled a wider interest in approaches for handling uncertainty.
However there is also a certain degree of skepticism about the extent towhichmodels can be used for decision-making under uncertainty.
In addition, all the extant forecasting methods contain fundamentalweaknesses and struggle deeply in grapplingwith the long-term'smultiplicity of plausible futures.
despite the presence of awide variety of quite distinct uncertainties and a multiplicity of plausible futures.
and ignoring many uncertainties. The models were used here to explicitly explore a plethora of uncertainties
in order to assess the implications of these uncertainties for decision-making. The presented approach can easily be expanded ormodified For example, we used PRIMFOR the identification of both opportunities and vulnerabilities.
Other rule induction methods, such as decision tree induction, Classification and Regression Trees (CART) 60, could be applied equally well to this task.
Acknowledgments This study is part of the project Dealing with Uncertainties in Infrastructure Planning and Design:
Asselt, P. Janssen, M. Von Krauss, Defining uncertainty: a conceptual basis for uncertainty management in model-based decision support, Integr.
Assess. 4 (2003) 5 17.5 G. Yucel, Analyzing transition dynamics: the actor-options framework for modelling socio-technical systems, in:
and communicating uncertainties in model-based policy analysis, Int. J. Technol. Policy Manag. 10 (2010) 299 315.17 A l. Porter, W. B. Ashton, G. Clar, J. F. Coates, K. Cuhls, S w. Cunningham
and Management, Mcgraw-hill, New york, 2003.22 E s. Schwartz, L. Trigeorgis, Real Options and Investment under Uncertainty:
His research focused on the treatment of uncertainty in long-term airport planning. He currently works as a postdoc on the treatment of uncertainties in model-based decision support for fresh water supply in The netherlands at Delft University of Technology.
Next to this research, he also has an interest in scientometrics and tech-mining. Erik Pruyt is the Assistant professor of System Dynamics and Policy analysis at the Faculty of technology, Policy and Management of Delft University of Technology.
what kinds of surprising dynamics can occur given a variety of uncertainties and a basic understanding of the system.
This paper concludes that EMA is useful for generating foresights and studying systemic and structural transformations despite the presence of a plethora of uncertainties,
if there are profound uncertainties. The solar system of planets is a relatively small system the sun
whether models can be used at all in decision-making under uncertainty. In their agenda setting paper on FTA Porter et al. 1 note that there are many irreducible uncertainties inherent in the forces driving toward an unknown future beyond the short termand predictions need not be assumed to constitute necessary precursors to effective action.
In other literature, this is called deep uncertainty 7, 8, or severe uncertainty 9. It can be understood as a situation where one can incompletely enumerate multiple possibilities without being able
or willing to rank order the possibilities in terms of how likely or plausible they are judged to be 8. There is a need for model-based support for the design of robust strategies across this spectrum of irreducible uncertainties.
The RAND Corporation developed a technique called Exploratory Modeling and Analysis (EMA) tailored to this.
EMA aims at offering decision support even in the face of many irreducible uncertainties, by systematically exploring the consequences of a plethora of uncertainties ranging fromparametric uncertainties (e g. parameters ranges), over structural uncertainties (e g. different
to method uncertainties (e g. different modeling methods) using computational models as scenario generators. This paper explores the potential of EMA for FTA.
In these various fields, people are grappling with the treatment of deep and irreducible uncertainty
if the various guesses this single model makes about the various irreducible uncertainties are correct.
but also disagreement or uncertainty about which data to use. EMA can be used to identify the extent to which the choice of data influences the model outcomes.
or envelopes that decompose the entire space of uncertainties into subspaces with different properties. That is, partial information can inform policymaking
what policies would hold across various uncertainties. In this way, decision-making can proceed despite the presence of deep uncertainty,
By supporting the systematic exploration of the complete space of combinations of uncertainties, EMA addresses one of the often mentioned shortcomings of foresight,
The first case explores uncertainties related to the availability of minerals/metals that are crucial for the sustainable development of all societies.
Future uncertainty is increasing because contextual conditions are less stable, new technical solutions are emerging,
EMA offers a suitable technique to explore the potential implications of these uncertainties and assists in developing a plan that can adapt over time to how uncertainties unfold.
The third case presents an EMA study into transition pathways for the Dutch electricity system.
political uncertainty about future CO2 abatement policies such as emission trading; and socioeconomic uncertainty about fuel prices, investment decisions of suppliers,
and load curves. 3. 1. Mineral scarcity The first case explores uncertainties related to the availability of minerals/metals that are crucial for the sustainable development of all developed and developing societies.
Potential mineral/metal scarcity poses a serious challenge for civil protection in at least three ways 17,18: 1. Many crucial minerals
allowing the automatic and simultaneous exploration of many uncertainties and assumptions. This EMA use of the model is reported below. 3. 1. 2. Uncertainties The future evolution of the extraction
and recycling is intrinsically uncertain. The presented model allows exploring alternative evolutions across the various uncertainties.
Table 1 gives a high level overview of the key uncertainties that are taken into consideration. Note that we explore across both parametric variations
and what typically would be considered more Table 1 High level uncertainties. Name Description Ranges Parametric uncertainties A wide variety of parametric uncertainties are explored,
including the lifetime of mines and recycling facilities, the initial values, and behavioral parameters such as price elasticity and desired profit margins.
Typically plus and minus 50%of the default value Orders of time delays There are various time delays, such as building of new recycling capacity and mines.
This table shows the wide variety of behaviors that the model can generate by sampling across the various uncertainties.
in order to assess whether the cyclical behavior arises out of a particular combination of uncertainties. First, in order to identify crises behavior
Next, we tried to identify subspaces in the overall uncertainty space that show a high concentration of crises runs using the Patient Rule Induction Method 31 33.
but arises out of particular unique combinations of uncertainties. That is, crisis behavior is generated in this model by‘perfect storm'type combinations of parameters.
The purpose of EMA in this case is to help in the development of an adaptive plan for the long-term development of Amsterdam Airport Schiphol that is robust across the wide variety of uncertainties experienced by the airport. 3. 2. 1. Model
The uncertainty airport planners face is located mainly in the external environment affecting the airport. The uncertainty about the internal workings of an airport is comparatively small.
Therefore a single fast and simple model, utilizing existing tools for aspects of airport performance calculations has been developed 15.
2. Uncertainties A wide variety of uncertainties are important in long-term airport planning. Table 4 gives an overview of the major uncertainties that are explored in this case.
For more details on the parametric ranges of the various uncertainties see 15. By picking a functional form for each uncertainty
a scenario generator is specified. In total, 48 different generators are possible. Each generator in turn has its own parametric ranges over
which one can sample. 3. 2. 3. Analysis of results One key challenge for airport planners is to design a plan for guiding the future developments of the airport that is robust with respect to the future 36.
It is clear that this plan has a much narrower bandwidth in expected outcomes across all the uncertainties.
It is thus better able to guide the future developments of the airport in light of the uncertainties.
Table 4 Major uncertainties adapted from 15. Name Description Range Demand Change in demand, the curves can be parameterized in various ways Exponential growth, logistic growth,
political uncertainty about future CO2 abetment policies such as emission trading; and socioeconomic uncertainty about fuel prices, investment decisions of suppliers,
and load curves. Various alternative developments for these uncertainties are specified. The consequences of each of these alternative developments are assessed using an agent-based model 45 of the Dutch electricity system.
The outputs are analyzed using CART 46, a classification tree algorithm, in order to reveal arch-typical transition trajectories
and feasible investment options. 3. 3. 2. Uncertainties Table 6 presents an overview of the uncertainties that are explored in the EMA study.
13 uncertainties are explored across the specified range. Most of uncertainties are multiplier factors that will be used to alter the base value of the corresponding parameters.
For example, assume the investment cost of wind turbine is 100, and the Investment Cost Factor for wind is 0. 8,
suggesting that under most uncertainties a transition towards more sustainable generation does not take place. To provide insight into how the various uncertainties jointly determine outcomes,
a classification tree was made based on the results. Classification trees are employed a frequently data mining technique 46.
we used the uncertainties (Table 6) as attributes. As class we used the terminal value for the fraction of fossil fuel-based generation.
This tree can be used to see how the uncertainties jointly affect the extent of a transition towards more sustainable generation
'and then follow the path from that leaf back to the root to identify which uncertainties jointly produce the cases belong to that particular leaf.
the current policies are not effective enough across a large part of the uncertainties. 4. Results and implications for FTA This paper started from the observation that model-based decision support under conditions of deep uncertainty is problematic.
whether EMA can be used to facilitate the development of robust strategies even in the presence of many Table 6 The major uncertainties and their ranges.
E. Pruyt/Technological forecasting & Social Change 80 (2013) 419 431 irreducible uncertainties inherent in the forces driving toward an unknown future beyond the short term
and the large number of scenarios encompassing the spectrum of those uncertainties. However, no careful assessment of EMA for FTA has taken place yet.
E. Pruyt/Technological forecasting & Social Change 80 (2013) 419 431 some structural uncertainties were taken into account.
The case illustrated how through the use of nonlinear optimization techniques a performance bandwidth could be established across all the uncertainties.
which combinations of uncertainties resulted in which transition pattern. The case thus showed how complex and highly uncertain phenomena
such as transitions, can still be treated despite the presence of uncertainties. Moreover, it illustrates how one can identify the ranges of uncertainties that are favorable to a desirable transition and
which ranges are not. In particular the use of the classification tree in order to create insight into how uncertainties are mapped to classes of outcomes is useful.
These three cases show that EMA can be of use to FTA. FTA aims at offering systemic considerations on future developments for dynamically complex issues.
The comprehensive exploration of the consequences of combinations of uncertainties that can be offered by EMA is an important component of such future-oriented
Theoretically, the potential of EMA to FTA is its ability to cope with a multiplicity of deep and irreducible uncertainties in the analysis of decision-making problems
and discussed have shown that EMA can be used to handle diverse types of uncertainties in combination with three quite distinct modeling approaches.
but in particular the second case, showed how the ability to cope with uncertainties can help in iteratively developing dynamic adaptive strategies that are robust across a large part of the uncertainty space.
Uncertainty is recognized increasingly as being a major problem for the use of models in decision-making. The prime example being the role of uncertainty in relation to models used in the context of climate change debates.
EMA can have profound implications for the way in which uncertainty is treated and models are being used to support decision-making.
Where traditionally, often the uncertainties in the inputs to models are reduced as much as possible, in order to come to a best estimate of model outcomes,
EMA shows how one can embrace the full range of uncertainties on the input side to models.
EMA can also be used in case there is uncertainty about models, while focusing on the consequences decision-makers care about most:
the model outcomes. EMA can for example be used to iteratively reduce the expected bandwidth of model outcomes as in the second case presented.
or the tree can be used to understand which type of behavior pattern emerges under which combination of uncertainties.
for otherwise, the problem of incompletely taking into account uncertainty is being replaced by an information overload problem.
That is, the systematic exploration of a wide variety of uncertainties produces large datasets that need to be analyzed further using machine learning or data mining techniques in order to extract decision relevant information from it.
Porter et al. 1 argued that foresight exercises could not comprehensively explore the full range of scenarios that is encompassed by the many irreducible uncertainties encountered
EMA addresses the problem of deep uncertainty by systematically exploring over the uncertainties, potentially resulting in an information overload.
and communicating uncertainties in model-based policy analysis, Int. J. Technol. Policy Manag. 10 (2010) 299 315.9 Y. Ben-Haim, Information-Gap Decision theory:
Decisions under Severe Uncertainty, 2nd ed. Wiley, New york, 2006.10 E. A. Erikson, K. M. Weber, Adaptive foresight:
Sci. 44 (1998) 820 830.15 J. H. Kwakkel, The Treatment of Uncertainty in Airport Strategic planning, Faculty of technology, Policy and Management, Delft University of Technology, Delft, 2010.16
His research focuses on the treatment of uncertainty in model-based decision support. He has worked on cases in various domains including air transport, port planning, fresh water supply in The netherlands, world water scarcity issues,
One result of the above described complexity is a type of uncertainty about the future, an uncertainty
In addition, uncertainty increases as policy targets move progressively further from the present and it is uncomfortable:
and engineer the future produce increased uncertainties 13. For instance, developments in science and technology have a strong potential to influence social change.
but it is still essentially working in terms of the same basic known unknown dichotomy 8. In contrast, explorative scenarios deal with different kinds of knowledge, ignorance and uncertainty, for example,
The factors chosen for the axes should be high-impact, high-uncertainty, and to ensure that the four spaces defined by their intersection are differentiated clearly.
the strategic discussion has its origin in uncertainty, both in the external environment and within the organization.
but representing extreme uncertainties) can contribute strongly to triggering feelings of surprise and discovery. Responding to this emotive and cognitive disruption requires participants to think in ways that produce innovative and competitive solutions in a changing environment The DP21 scenarios (see Appendix 1) are a good example.
The Prelude scenarios2 are a good example (see Appendix 1). An important input for the scenario work in this group are the comprehensive descriptions of the external drivers for change highlighting the uncertainty of future developments.
This uncertainty is reflected in a distinguished set of possible long-term future images that are derived often from a multi-axes framework of the most important but uncertain drivers of change.
and understand uncertainties. Developing a set of scenarios acknowledges multiple rather than one future, equally plausible,
and innovation to meet necessary change and uncertainties in the agri-food sector facing resource constrains and environmental limits.
Examples of supportive techniques are the use of an uncertainty matrix and the multi-axes method using factors of high uncertainty and high impact.
Based on our reflexive inquiry used to analyze scenario exercises in their context we can then attribute the most characteristic mode of thinking. 4 Innovation is not only about invention, creation,
and confrontation Uncertainty matrix Intuitive Developing scenarios Shaped by convention Consensus (Delphi) Convention Legitimacy for action
and conceptualize future situations where uncertainties are high Allows strong imagination including alternative futures that are competing Weak on acceptance,
and conceptualize future situations for the long-term where uncertainties are expressed differently Allows rigorously exploring boundaries and complexity.
Edward Elgar, Cheltenham, in press. 42 42 K. H. Dreborg, Scenarios and structural uncertainty, explorations in the field of sustainable transport, Doctoral thesis, KTH Infrastructure
Concepts and Practice, Edward Elgar Publishing, Cheltenham, UK, 2008.60 T. Webler, D. Levine, H. Rakel, D. Renn, A novel approach to reducing uncertainty the group
His field of interest is socio-technological aspects such as uncertainty ethics and sustainability, of emerging technologies mainly inside agriculture, food production, biotechnology and bioenergy.
where his interests are focused on how to handle trans-disciplinary conflicts and scientific uncertainty. Currently Kristian is Head of Section in the Department of Management Engineering (DTU Man) at the Technical University of Denmark.
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