School of Public policy, Georgia Institute of technology, Atlanta, GA 30332-0345, USA d College of Computer science & Technology, Huaqiao University, Xiamen, 361021, PR China e
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Policy 6 (1/2/3)( 2010) 36 45. Lidan Gao is an Associate professor of Chengdu Library of The Chinese Academy of Sciences.
and of Public policy, at Georgia Tech, where he remains Co-director of the Technology policy and Assessment Center.
or policies, that automatically adapt to changing conditions is called adaptive decision-making, respectively adaptive policy-making. In this paper, we propose an iterative computational model-based approach to support adaptive decision-making under deep uncertainty.
This case demonstrates how the performance of a policy can be improved iteratively by exploring its performance across thousands of plausible scenarios,
which the adaptive policy needs to be extended until a satisfying dynamic adaptive policy is found for the entire ensemble of plausible scenarios.
and‘policy-making'for the act or process of designing policies by those in charge of designing (public policy.
consistently refer to‘policy-making'and‘policies, 'for our work mainly focuses on policy-making and the case we use to illustrate the approach here relates to policy-making for stimulating energy transitions.
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. adaptive policies, and to perform acceptably well in all circumstances, 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, and the analysis of wild cards and weak signals 11.
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,
In this paper, we propose an iterative model-based approach for designing adaptive policies that are robust under deep uncertainty.
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,
addressing one of the shortcomings of many traditional approaches and practices, i e. the poor utilization of the potential to be prepared for uncertainties and surprises of future developments 18.
predictive approaches are likely to result in policies that perform poorly. In response, an alternative policy-making paradigm has emerged.
Dewey 25 put forth an argument proposing that policies be treated as experiments, with the aim of promoting continual learning and adaptation in response to experience over time 26.
Policy learning is also a major issue in evolutionary economics of innovation 27 29. Early applications of adaptive policies are also found in the field of environmental management 30,31,
where policies are designed fromthe outset to test clearly formulated hypotheses about the behavior of an ecosystem being changed by human use 32.
A similar attitude is advocated also by Collingridge 33 with respect to the development of new technologies. Given ignorance about the possible side effects of technologies under development, he argues that one should strive for correctability of decisions, extensive monitoring of effects, and flexibility.
Walker et al. 24 advocate that policies should be adaptive: one should take only those actions that are non-regret
and a pre-specification of responseswhen specific trigger values are reached should complement a basic policy.
The resulting policy is flexible and adaptive to the future as it unfolds. 409 C. Hamarat et al./
This basic policy is made more robust through four types of actions, which are specified in Step III,
namely by mitigating actions to reduce the certain adverse effects of a policy; hedging actions to spread
or reduce the negative impacts of uncertain adverse effects of a policy; seizing actions to profit from opportunities;
or event that could make the policy fail will occur, or to increase the chance that an external condition
or event that could make the policy succeed will occur. Even with the actions taken in Step III,
there is still the need to monitor the performance of the policy and take action if necessary.
in order to determine whether the policy is progressing toward success. Critical values of signpost variables (triggers) are chosen,
beyond which actions should be implemented to ensure that the policy keeps moving the system at a proper speed in the right direction.
defensive actions are taken to reinforce the basic policy, preserve its benefits, or meet outside challenges in response to specific triggers that leave the basic policy unchanged;
corrective actions are adjustments to the basic policy; capitalizing actions aim at taking advantage of opportunities that improve the outcomes of the basic policy;
and a reassessment of the policy is initiated when the analysis and assumptions critical to the policy's success have lost validity.
In a recent special issue of Technological forecasting and Social Change on adaptivity in decision-making, the guest editors conclude that Adaptive policy-making is a way of dealing with deep uncertainty that falls between too much precaution and acting too late.
While the need for adaptation is acknowledged increasingly, it is still a developing concept, and requires the further development of specific tools and methods for its operationalization 7. More specifically,
for adaptive policy-making to become a useful policy-making approach, it is necessary to specify in more depth how the various steps could be carried out and
support the development of long-term strategic policies under deep uncertainty, and test policy robustness over. EMA could also be used to develop adaptive policies under deep uncertainty
since it allows for generating and exploring a multiplicity of plausible scenarios by sweeping multidimensional uncertainty space.
EMA could then be used to identify vulnerabilities and opportunities present in this ensemble of scenarios, paving the way for designing targeted actions that address vulnerabilities
The efficacy of the resulting policies could then be tested over the entire ensemble of scenarios.
Moreover, EMA could be used to identify conditions under which changes in a policy are required. That is
as well as the main causes of these troublesome and promising regions,(6) the design informed by the analysis in Step 5 of policies for turning troublesome regions into unproblematic regions,(7) the implementation of the candidate policies
in the models,(8) the generation of all plausible scenarios, subject to the candidate policies, (9) the exploration and analysis of the ensemble of scenarios obtained in Step 8
Steps 5 8 should be iterated until an adaptive policy emerges with robust outcomes (see in Fig. 2). The identification of troublesome
then they are addressed typically best in the basic policy or through actions aimed at enhancing the robustness of the basic policy,
while uncertainties of relevance only in particular regions are handled typically better through monitoring and associated corrective, defensive,
and hence, for developing specific adaptive actions for adaptive policies. PRIM has been used in combination with EMA by other authors 40 42.
Here, the troublesome and promising regions identified with PRIM are used directly for designing adaptive policies and the corresponding monitoring systems.
The approach for developing adaptive policies as presented here shares characteristics with‘Robust Decision making (RDM)' 8
we emphasize the iterative character of policy formation. However, by connecting this to a particular framework for the design of adaptive policies,
our approach is more specific on the various ways in 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./
Adaptive Robust Design in case of energy transitions In order to illustrate how the ARD approach helps in designing adaptive policies,
we present an illustrative case study about developing an adaptive policy for stimulating the transition of the electric power generation sector toward a more sustainable one.
and when trying to influence them by means of adaptive policies. Table 1 Overview of the uncertainties.
and their corresponding ranges are displayed in Table 1. 3. 2. The ARD process illustrated 3. 2. 1. No policy
a‘no policy'ensemble of 10,000 simulations was generated. In the model used, at least one preference criterion must be activated (switch value equal to 1) for each run,
Fig. 3 shows the results of 1000 randomly selected cases out of the remaining 9349 runs in the post-processed‘no policy'ensemble.
and requires policy intervention. Hence, we use PRIM to identify relatively large regions in the uncertainty space that generate relatively high concentrations of undesirable results,
therefore seems to be a promising basic policy, i e. a policy that will be implemented in any case from the start. 3. 2. 2. Basic policy Shortening the lifetime of Technology 1 could be achieved by increasing its decommissioning,
for as long as the fraction of new technologies remains below a particular target fraction, say 0. 8,
To assess the performance of this basic policy, the same 9349 experiments used for exploring the no policy case are executed now with the basic policy.
Fig. 4 displays the envelopes spanning the upper and lower limits of the total Fig. 3. Total fraction of new technologies for the‘no policy'ensemble. 413 C. Hamarat et al./
/Technological forecasting & Social Change 80 (2013) 408 418 fraction of new technologies for the no policy ensemble (in blue) and the basic policy ensemble (in green) as well as the KDES of the end states of all
runs in the respective ensembles. The upward shift of the sustainable fraction in Fig. 4 means that the need for new capacity resulting from the additional decommissioning of Technology 1 is to a large extent filled by new technologies.
Hence, the basic policy stimulates the transition from Technology 1 to new technologies, at least to some extent.
For this reason, we applied PRIM once more with the same classification rule in order to identify troublesome regions for the basic policy.
The basic policy aimed at increasing the decommissioning of the dominant technology, since all PRIM boxes indicated decreasing the negative effect of the lifetime of Technology 1 would help to increase the fraction of new technologies.
The second iteration PRIM results show there are three very different troublesome regions in the basic policy ensemble:
and improve the basic policy, it is necessary to analyze the characteristics of the PRIM regions to identify the vulnerabilities that generate the undesirable outcomes.
The pointwhere the performance of Technology 3 or 4 equals the performance of Technology 2 could be the trigger for Table 2 PRIM results for the no policy ensemble.
Fig. 4. Comparison of no policy and basic policy for total fraction of new technologies. 414 C. Hamarat
So, we modified our basic policy by adding the monitoring and corrective actions and reran the experiments.
To further address this vulnerability, we also add a hedging action to the basic policy in the form of additional commissioning of Technologies 3 and 4 in their early years.
The adoption of the new technologies in later years is also higher than under the basic policy,
To improve the performance of the adaptive policy even further, the triggers used for adaptivity were optimized using robust optimization 57 59.
Using the trigger values optimized over the entire ensemble for the actions previously discussed significantly improves the adaptive policy.
Fig. 5 shows a comparison in terms of the total fraction of new technologies of the‘no policy'ensemble, the‘basic policy'ensemble,
and this‘adaptive policy'ensemble over the same uncertainty space, i e. using the same experimental design.
It shows that the‘adaptive policy'ensemble, although hardly improving the extremes, outperforms the‘basic policy'and‘no policy'ensembles on this key performance indicator:
the adaptive policy is a better guarantee for a successful transition under deep uncertainty, without forcing a transition to new technologies upon situations that do not require a transition to take place (e g. in case of a cheap and environmentally friendly dominant technology) or for
which a transition would be overly expensive. Fig. 5. Comparison of no policy, basic policy and adaptive policy for total fraction of new technologies. 415 C. Hamarat et al./
/Technological forecasting & Social Change 80 (2013) 408 418 4. Discussion and implications for Future-oriented technology analysis (FTA) In this paper we proposed an iterative computational approach for designing adaptive policies that are robust
under deep uncertainty. The proposed approach has been illustrated on an energy transition case. Several of our findings warrant further discussion.
and robust policies for socioeconomic and technological changes (i e. energy transitions). This study illustrated the potential of EMA for FTA as suggested by Porter et al. 17.
This paper shows a way in which EMA can be utilized to support the iterative development and refinement of adaptive policies in light of a clear exploration of the multiplicity of plausible futures.
and assessed for their consequences. 5. Conclusions We have proposed an iterativemodel-based approach for developing adaptive policies under uncertainty.
adaptive and robust policies for grand societal transformations. Furthermore, this study has shown that Exploratorymodeling and Analysis can be utilized successfully in the context of adaptive policy-making.
The iterative approach for designing robust adaptive policies helps to identify and address both vulnerabilities and opportunities,
resulting in a dynamic adaptive policy that improves the extent towhich the energy system transits to a more sustainable functioning.
Exploratory Modeling, Real Options analysis and Policy design which is supported by The next Generation Infrastructures (NGI) Foundation.
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/Technological forecasting & Social Change 80 (2013) 408 418 Caner Hamarat is a Phd researcher at the Faculty of technology, Policy and Management of Delft University of Technology.
His applied interests include climate change/energy issues, public health and health policies, financial crisis and energy systems. His current research interests are adaptive policy making and the use of optimization in policy-making.
Jan Kwakkel received a Ph d. from Delft University of Technology. His research focused on the treatment of uncertainty in long-term airport planning.
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.
Exploratory Modeling and Analysis, an approach for model-based foresight under deep uncertainty Jan H. Kwakkel, Erik Pruyt Faculty of technology, Policy,
and for designing robust policies and plans, which are key activities of FTA. 2012 Elsevier Inc. All rights reserved.
such as Under what circumstances would this policy do well? Under what circumstances would it fail? and what is the range of plausible future dynamic developments of a phenomenon of interest?
and can support the development of adaptive plans or policies. EMA is first and foremost an alternative way of using the available models, knowledge, data, and information.
In making policy or planning decisions about complex and uncertain problems, EMA can provide new knowledge,
the debate can then shift to the development of policies or plans that produce satisfying results across the alternative sets of data.
E. Pruyt/Technological forecasting & Social Change 80 (2013) 419 431 that policy or planning debates can often be served even by the discovery of thresholds, boundaries,
what policies would hold across various uncertainties. In this way, decision-making can proceed despite the presence of deep uncertainty,
political uncertainty about future CO2 abatement policies such as emission trading; and socioeconomic uncertainty about fuel prices, investment decisions of suppliers,
in order to develop and test structural policies 24, 25. Under conditions of deep uncertainty, long time horizons, and high dynamic complexity, a more exploratory use of models is called for 26.
political uncertainty about future CO2 abetment policies such as emission trading; and socioeconomic uncertainty about fuel prices, investment decisions of suppliers,
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.
FTA intends to guide policy and decision-making by helping in anticipating and shaping future developments. The second case demonstrates how EMA can be used for guiding decision-making on plans that shape the long-term development of an airport.
Future research avenues include elaborating on the use of EMA for designing dynamic adaptive policies and the use of EMA for 429 J. H. Kwakkel, E. Pruyt/Technological forecasting & Social Change 80
Another major avenue of research is on the communication of EMA that results to policy-makers and FTA practitioners.
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Erik Pruyt is Assistant professor of System Dynamics and Policy analysis at the Faculty of technology, Policy and Management of Delft University of Technology.
These grand challenges require policy-makers to address a variety of interrelated issues, which are built upon yet uncoordinated and dispersed bodies of knowledge.
and by widening the perspectives and knowledge base of researchers, policy-makers and business decision-makers. -be useful in creating a common language and understanding between the various interest groups.
and social policies 9. Grand challenges are interrelated usually and operating at a global scale 10. Often it is not clear
and different policy options are competing, causing shifts in problem perception and priority setting. 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 governments deal with international scientific developments in different ways through the policies they pursue 14.
This analysis indicates that policy and systems of innovation are shaped by social, cultural and political power as well as by technological rationalism and such indeterminism makes systemic approaches to innovation policy far from linear or predictable.
Grand challenges require that policy-makers address a variety of interrelated issues, which are built upon as yet uncoordinated and dispersed bodies of knowledge.
Section 3 describes how we conceptualize inspiring issues and paradigms from different scientific disciplines such as business and innovation research, futures studies, sociology and policy analysis.
/Technological forecasting & Social Change 80 (2013) 432 443 experiments in the policy process, new concepts and sustainable solutions can be found to grand challenges.
i e. scenarios intended to provide a guiding vision of the future for policy-makers 46. Scenario building and planning was developed further for management purposes, for example through the works of Pierre Wack
and then works backwards to identify policies and programs that will connect the future to the present.
This technique is used often in national foresights to guide innovation and national research policies 58 60. All the above describe approaches to futures thinking during which (potential) inputs for scenarios can be produced.
We found that crystallizing concrete policy initiatives for innovation from long-term future images, i e. beyond twenty years, can be difficult.
The foreseen outcome of long-term investments or policies, for example, can be visualized and confronted with a changing environment.
Roadmaps directed towards a single target are likely to be inappropriate where policy intervention may direct technology towards a different trajectory altogether 70,71.
In order to avoid surprises the policy or strategy process should be able to open the scope of observation for periphery incidents and early,
and the scientific communities'involvement in policy discourse. 4. 3. Empowering stakeholders Developing and using future scenarios to inspire innovation do
Using a policy perspective, different groups of practice have been revealed. In the next section we will discuss our findings against the initial key question of how futures thinking
Following a policy perspective, however, there is lack of exploitation of innovative solutions for orienting innovation in itself along more sustainable pathways 15.
By questioning representation from a policy perspective and deconstructing future scenario practice, we were able to (re) construct findings to the above questions:(
Table 2 Linking groups of future scenario practice from a policy perspective with modes of future thinking.
Policy perspective (representation) Scenario practice (most characteristic) Types of futures (main focus) Techniques (example) Modes of futures thinking Window of opportunity (sense of urgency) Using scenarios Shaped by surprise
i e. using a policy perspective for doing an ex-post analysis of future scenario practice. Innovation systems are complex and dynamic
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His professional challenge is connecting science and policy. On a broad range of regional and EU projects, involving foresight and integrated assessment,
offering scenarios and integrated solutions to support policy-makers. Currently Peter works at the Research centre of the Flemish Government where he is in charge of foresight and sustainability assessment.
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