In this paper, we propose an iterative computational model-based approach to support adaptive decision-making under deep uncertainty.
and explore thousands of plausible scenarios using simulation models, data mining techniques, and robust optimization. The proposed approach,
and computational models are used commonly to support decision-making and policy-making 1 5. The term‘model'refers here to a representation of the most crucial aspects of a system of interest for extracting usable information 6. The term‘decision-making'is used here for the act or process of making strategies or conscious decisions
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,
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,
i e. in case of‘deep uncertainty',then traditional modeling and model-based policy-making tends to fail. Deep uncertainty pertains according to Lempert et al. 8 to those situations in
which a multiplicity of alternative models could be developed for how (aspects of) systems may work,
many plausible outcomes could be generated with these models, and outcomes could be valued in different ways,
but one is not able to rank order the alternative system models, plausible outcomes, and outcome evaluations in terms of likelihood 16.
all alternative system models, plausible scenarios, and evaluations require consideration, without exception, and none should be treated as the single best model representation, true scenario,
i e. with many different kinds of uncertainties, multiple models, a multiplicity of plausible scenarios and evaluations of these scenarios 17.
In this paper, we propose an iterative model-based approach for designing adaptive policies that are robust under deep uncertainty.
an ensemble of models is developed that explicitly allows for the exploration of the uncertainties. The behavior of the ensemble is analyzed
is proposed and illustrated below. 2. 2. The Adaptive Robust Design approach EMA is a methodology that uses computational experiments to combine plausible models
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
a System Dynamics 50,51 model developed for exploring the dynamics of energy system transitions 3 is used in this study.
Since fast and relatively simple models are needed for EMA the more sustainable technologies (2, 3 and 4) are considered to be generic for the sake of simplicity.
A more detailed explanation of the model can be found in 3 . And the uncertainties taken into consideration
In the model used, at least one preference criterion must be activated (switch value equal to 1) for each run,
Since the way in which economic development is represented in this model creates cyclic behavior, a possible corrective action could be to partly decouple the adoption of new technologies from the economic cyclewith the help of subsidies and additional commissioning of newtechnologies.
Different perspectives, differentworldviews or different mental models of various stakeholders are usually the norm in FTA projects
mental models or quantitative models. That is, EMA could be used to support an inclusive modeling process from the start, where different beliefs about how a system functions,
The models were used here to explicitly explore a plethora of uncertainties in order to assess the implications of these uncertainties for decision-making.
a conceptual basis for uncertainty management in model-based decision support, Integr. Assess. 4 (2003) 5 17.5 G. Yucel,
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
Gloucestershire, 2006.28 A. Faber, K. Frenken, Models in evolutionary economics and environmental policy: towards an evolutionary environmental economics, Technol.
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.
Exploratory Modeling and Analysis, an approach for model-based foresight under deep uncertainty Jan H. Kwakkel, Erik Pruyt Faculty of technology, Policy,
In the second case, EMA is combined with a hybrid model for airport performance calculations to develop an adaptive strategic plan.
EMA is combinedwith an agent-based model to study transition dynamics in the electricity sector
A subset of these approaches relies, at least in part, on mathematical and computer models. The reason for using models might be understood in light of the rise of Newtonian mechanics
and its success in predicting a wide array of different phenomena 2. It brought together the celestial realm and the sublunar realm in a single explanatory framework 2, 3. Moreover,
and are codified into computer models. However, the use of models to make predictions can be seriously misleading
if there are profound uncertainties. The solar system of planets is a relatively small system the sun
The use of predictive models for such systems is problematic. There have been scientists who have realized this.
others say all models are wrong 5, and yet again others qualify arithmetic for such systems as useless 6. Such comments raise the question
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.
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.
structures and models), to method uncertainties (e g. different modeling methods) using computational models as scenario generators.
This paper explores the potential of EMA for FTA. It thus explicitly addresses one of the FTA challenges identified by Porter et al. 1 by assessing how EMA could contribute to adaptive foresight 10 under deep uncertainty.
while using models. A common theme across these fields appears to be a shift away from predictive model use towards more explorative model use 6
11,12. Exploratory Modeling and Analysis (EMA) is a research methodology that uses computational experiments to analyze complex and uncertain systems 12,13.
when relevant information exists that can be exploited by building models, but where this information does not allow specifying a single model that accurately describes system behavior.
In this circumstance models can be constructed that are consistent with the available information, but such models are not unique.
The available information is consistent with a potentially infinite set of plausible models, whose implications for potential decisions may be quite diverse.
A single model run drawn from this set provides a computational experiment that reveals how the world would behave
if the various guesses this single model makes about the various irreducible uncertainties are correct.
By conducting many such computational experiments, one can explore the implications of the various guesses.
EMA is the explicit representation of the set of plausible models the process of exploiting the information contained in such a set through a large number of computational experiments,
and the analysis of the results of these experiments 12,13. EMA is focused not narrowly on optimizing a (complex system to accomplish a particular goal or answer a specific question,
EMA is first and foremost an alternative way of using the available models, knowledge, data, and information.
even where strict model validation is impossible. For example, EMA can be used for existence proofs or hypothesis generation,
by identifying models that generate atypical or counterintuitive behavior. Knowing that a system can exhibit such behavior can change the debate
In this paper, we argue that by using models differently, the challenges associated with decision-making under deep uncertainty can largely be overcome.
the models are used to explore what could happen and what policies would hold across various uncertainties.
These cases differ in application domain, the type of models used, and the purpose of the study.
That is, it serves as an existence proof generator. 3. 1. 1. Model Traditionally, System Dynamics is used for modeling
and high dynamic complexity, a more exploratory use of models is called for 26. Mineral and metal scarcity is characterized by long time horizons
Causal loop diagrams are used often to communicate feedback loop structures included in System Dynamics models.
More details on the model can be found in 18. The model has been implemented as a System Dynamics model using the Vensim software 27.421 J. H. Kwakkel,
E. Pruyt/Technological forecasting & Social Change 80 (2013) 419 431 This small and simplistic System Dynamics model was developed in about one day in close collaboration with a mineral/metal expert
, based on his mental model of the underlying structure of the mineral/metal system 18. The objective of the joint modeling endeavor was twofold:(
At a later stage, the model was used as a scenario generator for EMA, 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
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.
utilizing the Vensim DLL 27,29 to parameterize the model, run the model and extract the results. 3. 1. 3. Analysis of results Fig. 2 shows the dynamics for 5 different outcomes of interest.
It illustrates clearly the wide variety of dynamics that can occur. It also shows that under specific conditions cyclical behavior can emerge,
This table shows the wide variety of behaviors that the model can generate by sampling across the various uncertainties.
That is, crisis behavior is generated in this model by‘perfect storm'type combinations of parameters. This finding is troublesome to decision-making,
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
a single fast and simple model, utilizing existing tools for aspects of airport performance calculations has been developed 15.
Table 3 gives an overview of the components that make up this fast and simple model.
This model is a general purpose model, by parameterizing it for the specifics of a particular airport (runway locations, etc.
This figure Table 3 Tools integrated in the fast and simple model for airport performance analysis. Airport performance aspect Tool Capacity FAA Airfield Capacity Model (FCM
Noise Area Equivalent Method (AEM) a model that approximates Integrated Noise Model results 40. Emissions Emission Dispersion Modeling System (EDMS) the FAA required tool for emission analysis 41.
and their conditions for occurring. 3. 3. 1. Model Electtrans is based an agent simulation model,
Four groups of end-users are represented in the model which are industrial users, commercial users, horti-/agricultural users, and households.
There are two grid-based options in the model: gray electricity and green electricity. Various distributed generation options are also available, such as wind turbines and gas engine CHPS.
then the model will be initialized with an investment cost of 100×0.8=80. To be more precise,
and implications for FTA This paper started from the observation that model-based decision support under conditions of deep uncertainty is problematic.
The second case used an ensemble of hybrid models to facilitate the design of a good plan for shaping
The third case illustrated how EMA can be combined with agent-based models. In the case, we investigated the transition patterns that could occur and
while in particular the first and third case demonstrate how this can be combined with nonlinear dynamic models (System Dynamics and Agent Based Modeling respectively),
EMA offers practitioners a model-based method for handling such situations. Rather than developing a single or a small number of model-based estimates for a phenomenon of interest,
EMA allows practitioners to develop an inclusive ensemble of models that captures the breath and richness of the multiplicity of worldviews,
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.
An Appraisal for Policy makers and Planners, Johns hopkins university Press, Baltimore, 1978.5 J. D. Sterman, All models are wrong:
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:
and Management, Delft University of Technology, Delft, 2008.14 J. H. Miller, Active nonlinear tests (ANTS) of complex simulation models, Manag.
a generic exploratory system dynamics model, in: T. H. Moon (Ed.),The 28th International Conference of the System Dynamics Society, 2010, Seoul, Korea. 19 E. Alonso, J. Gregory, F. Field
An Exploratory System Dynamics Model and Analysis of the Global Copper System in The next 40 Years, Delft University of Technology, Delft, 2011.22 J. H. Kwakkel, W
Airfield Capacity Model, Federal Aviation Administration, Washington D c, 1981.40 FAA, FAA Aerospace Forecast Fiscal years 2008 2025, U s. Department of transportation, Federal Aviation
. S. Paterson, A Crash Location Model for Use in the Vicinity of Airports, National Air Traffic Services Ltd.
Thesis, 2004.50 S. J. Heblij, R. A a. Wijnen, Development of a runway allocation optimisation model for airport strategic planning, Transportation Planning and Technology 31 (2
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,
The traditional concepts and models of innovation are not always adequate to embrace the complexity for addressing the grand challenges 10,15.
Innovation in the 21st century differs from the model embraced in the last century (i e. profit-oriented
not only deal with the collection of data and models; they also involve the interaction of the stakeholders, their ideas, values and capacities for social change.
Rijkens-Klomp, D. S. Rothman, J. Rotmans, Cloudy Crystal Balls, An Assessment of Recent European and Global Scenario Studies And Models, Experts'Corner Report:
traditional versus participatory model building, Interdiscip. Sci. Rev. 32 (2007) 1 20.74 S. Funtowicz, J. Ravetz, Science for the post-normal age, Futures 25 (1993) 735 755.75 A. Jetter, W
which can be characterized as a model of linear and science-driven innovation. In this model, technology results from research whereas society has to adapt to technology to make its applications successful.
For its part, the government's role is to improve and accelerate the uptake of technology through funding, education and awareness-raising.
the democratic virtues of the consensus conference model, Public Underst. Sci. 17 (2008) 329 348.25 R. Zimmer, R. Hertel, G.-F. Böl, Bfr Consumer Conference Nanotechnology, Federal Institute for Risk assessment, Berlin
A wide variety of hybrid value creation models with novel configurations of innovation actors emerged. We explain the approach
Early models saw innovation processes as a linear sequence of functional activities distinguishing only between technology push
In the case of scenario building the model-based approach is in widespread use in Europe,
A Model of industrial systems in which all waste materials are reincorporated productively in new production and use phases. 462 E. Schirrmeister, P. Warnke/Technological forecasting & Social Change 80 (2013) 453 466 (2) Participation:
Similar tendencies are visible in investigator-driven Research funding models in most countries evolved but only slowly towards accommodating more interdisciplinary thematic approaches.
While there is as yet no clear methodological answer to the identification issue there have been some institutional responses and new organisational models of FTA.
They use an analytical framework that they call the‘‘Cyclic Innovation Model (CIM)''to make the case for the convergent development of innovation
Consequently, there was less time to learn from the foresight study in a strategic 1 Particular issues arise in the case of quantitative forecasting models,
In these cases, contradictory information may indeed emerge as a consequence of different assumptions across models.
In the local cases, policy-makers concluded that one of the key challenges with respect to organisational embedding is to find appropriate operational models
(i e. research areas and research topics) that are expected, according to the selected models, to enable the IMS2020 Vision to become a reality.
joint application of integrated management model and roadmapping, Technological forecasting and Social Change 71 (2004) 27 65.6 O. Saritas, Systems thinking for Foresight,(Ph d. thesis), Manchester
. Sriram, Developing a Sustainability Manufacturing Maturity Model. The IMS Summer School Manufacturing Strategy First Edition 2010:
or supply chains 5. However, many of these models display important weaknesses. In particular, they fail to tackle efficiently the communication of the strategy across all organisational levels 6 10,
when used in conjunction with other models such as the BSC, the Quality Function Deployment 26 and the GBN method 27.
in order to efficiently respond to such changes. 4 The fourth phase (strategic learning) is based on the model proposed by Kaplan and Norton 12,
For the former the Cyclic Innovation Model (CIM) is utilized as analytical framework and applied to three cases.
by applying the Cyclic Innovation Model (CIM) and by analyzing foresight activities therein in terms of type, scope,
In this paper, this relationship is investigated by applying the Cyclic Innovation Model to three cases. Moreover, activities observable in the three cases are Table 1 Generations of innovation management and futures research (based on van der Duin 3
and new theories, models and solutions 42. Case study research is recommended therefore for exploratory qualitative research characterized by scant previous knowledge 43 45.
and openness of the three networks we applied the Cyclic Innovation Model as an analytical framework.
the link of future orientation, futures research and the network is analyzed by connecting the CIM analysis with the character of the foresight activities. 3. 2. Analytical framework 3. 2. 1. The Cyclic Innovation Model The main
principles of the Cyclic Innovation Model are (1) that innovating is predominantly a cyclic interaction between different actors who exchange knowledge
image of the future, process model and transition path. For instance, the transition path aims at realizing the 2 Critics argue that the active involvement in day-to-day work creates bias in the participant-observers in that they may partly
Thus, RWS is continuously searching for innovations in their Fig. 1. Level 1 of the Cyclic Innovation Model:
and knowledge exchange. 2. Beneficiaries of networked foresight activities are the network partners within the predefined project settings. 3. For developing the process model,
When recalling the application of the Cyclic Innovation Model to the three cases at least three issues are noticeable:
and inside-out) information flow from the perspective of the partners it is an inside-out information flow. 6. Conclusions This paper aimed at exploring futures research in innovation networks by applying the Cyclic Innovation Model as analytical framework to three cases
The application of the Cyclic Innovation Model shows that the envisioned and practiced openness of the three networks differs substantially.
and (2) to adjust the process models and eventually the transition path. Doing this with the networks'partners promises to sharpen the results by including additional perspectives
curve modelling, leading indicators, envelope curves, long wave models Expert opinion Survey, Delphi, focus groups, participatory approaches Modelling and simulation Innovations systems descriptions
The scenarios revealed an EID lifecycle model, which helps to understand how technology can be used to combat EID at every stage of their lifecycle.
develop a model for continuous data sharing and comparison; compare working methods and methodologies used by the different horizon scans
which aim to support the formattio of policy strategies and associated governance models. Among others, he has been involved in the Europeea projects FISTERA and EPIS, both dealing with the future of the information society.
What are the critical differences in national foresiigh program models? Please provide examples. Structure and organization?
Phase 2 of the first study focused the interviews on deriving a deeper understanding of the models
Mcluhan Tetrad Model) Define priority areas for technology policy Survey national technological development Stimulate development in priority areas of technology development and research;
there were many consistent comments in the interviews that provides the beginnings of a model on
Applying the critical success factors to Canada's foresight program Our studies have identified eight critical success factoors The strength of any model is its ability to assist
The model Was developed in Roadmapping I Developed in Roadmapping II Figure 3. Structure of technology roadmaps Biosecurity and emerging infectious diseases in Asia-pacific Science and Public policy February 2010 46 proposed at the workshop
) According to the model, technological approaches can be used to combat EIDS at every stage of their life cycle, from preventive measures such as vacciine to biosensors for surveillance, bioassays for detection, drugs for treatment,
has proposed a decision model to identify and evaluate an optimum mix of interventions and measures for a specific disease, such as improvements in health infrastructure,
The model will take into account the existing situation on the ground, evidencebaase metrics of coverage and efficacy, financial requirements,
The life cycle model can be linked to six significaan technology domains: vaccines, diagnostics, ubiquitous computing, tracking, modeling and drugs.
the availability of realistic models can assist policy-makers in developing options for coping with outbreaks but they cannot be used in real time
The‘people factors'are crucial features of disease management through all phases of the life cycle model from detection to response.
regression with fixed effects or‘difference in differencces'selection models and matching methods based on direct comparisons of the participating
Evaluation methodologies, econometric models: microeconometric models. In RTD Evaluatiio Tool Box: Socioeconomic Evaluation of Public RTD Policies (EPUB), W Polt, J. Rojo, A Tübke, G Fahrenkrog and K Zinöcker (eds.
pp 101 118. Vienna: European commission. Arvanitis, S, H Hollenstein and S Lenz 2002. The effectiveness of government promotion of advanced manufacturing technologiie (AMT:
the experience of international organisations established to provide a supranational mechanism for addressing such issues suggests that these models are incapable of engaging with such issues.
>References Brummer, H. L. 2005)‘ A dynamic competitive analysis model for global mining firms',Doctor of commerce thesis, University of South africa.
We argue that such a transformation of policy models is also underway, blending the traditional focus on large-scale missions with a pluralist funding of individual projects and scientific institutions,
The emergence of a new priority-setting model has been driven by growing internal criticism of what are described as cumbersome and opaque allocation models.
In an article in People's Daily in August 2010, prominent academmic complained that the current S&t system is overfunnde but institutionally weak (Zhao et al. 2010.
and which of these models may be suited best to fulfill the goal of making China a global scientific superpoowe (see also Hao 2008).
Instead, China seems to be forging its own way with an evolving mixture of planning, decentralization and deliberation. 1. 1 Trends in setting priorities Explicit models for science policy priority-setting devellope late and with great tensions.
In most‘mature'research systems in Western societies, several models for priority-setting exist side by side: floor funding to universities,
Which interests do the funding model and the mixture of allocation streams reflect?.How is the funding model related to current trends of‘coordinated decentralization'in science policy?
Beginning with the founding of People's republic of china in 1949, a Socialist centralized S&t system was built in the 1950s by adding the Soviet model of centralized planning onto the S&t system that had emerged in the Republic of china (e g.
Thus China has continued on the Soviet model of using plans (jihua, or guihua) to drive the development of S&t,
In order to conduct a systematic analysis of the strengths and weaknesses of different organisational models of FTA,
Drawing on recent experiences with alternative models of FTA systems, solutions will be identified based on a combinattio of social
What kinds of models for FTA systems exist? How can they be systematised in conceptual terms?.
What kinds of developments can we observe in terms of how these models are used in practice?.What do these findings suggest with regard to the future direction to take for organising FTA ACTIVITIES?
and the types of organisational models and governance contexts that make up FTA systems. Section 3 will draw primarily on recent empirical research presented at the FTA 2011 Conference, 1
The analysis will clarify the potential of different institutional models for tackling different types of future requirements.
Different types of grand challenges call for different transformation models and policy strategies. The distinction between disrupptiv and recognised grand challenges referred to in the European Science Foundation report (European Science Foundation 2010) highlights the fact that areas of disruptiiv grand challenges can be exogenous
three ideal-type organisational models for FTA can be identified, taking into account the speed of change
Setting up dedicated and temporary FTA projects or programmes has been a very common model over the past two decades.
A third model, more accessible to countries and organisations with limited resources, is the network model
However, which of these three basic organisatioona models best fits the requirements is also a matter of the governance mode (co-existence, competition, cooperattio or integration)
organisational model and governance mode need to be compatible with each other. 3. Diversity of FTA systems in practice Against this backdrop,
is a change in both governance and organisational models. A much higher degree of policy coordination seems to be needed to address societal challennge as well as a much more continuous and‘embedded'approach to FTA.
which FTA is embedded and the organisational Table 2. Framework for analysis of FTA systems Dimensions Transformation types Organisational models of FTA Governance modes Sub-categories.
and consequent models on organising FTA ACTIVITIES (see Table 4). Our analysis of the selected papers indicates an increasing emphasis in FTA objectives on improved understanndin of transformations.
2011) have analysed types of Table 3. Changing rationales for FTA APPROACHES on FTA systems Dimensions Transformation types and consequent challenges Governance modes Organisational models of FTA Traditional
Tiits and Kalvet (2011) learned from recent foresight exercises in Estonia that the Table 4. Diversity of FTA systems in practice Approaches in FTA systems Transformation types Governance modes Organisational models
forms and types of transformations, modes of governance and organisational models. A number of crosscutting observations can be drawn on the current evolution of FTA, on emerging requirements and possible responses to them. 3. 2. 1 Observation 1:
and challenges can be addressed by combinations of governance contexts and appropriate organisational models of FTA. 3. 2. 2 Observation 2:
Different models of FTA systems can be complementary in many respects. Service providers as well as FTA instituttion need to be able to draw on networks for many purposes,
Whether a specific model of FTA is appropriate for a transformative problem or not strongly depends on the wider institutional and organisatioona environment in
an intelligent combinaatio of FTA models needs to be put at the disposal of decision-makers,
In turn three organisation models of FTA are identified: short-term projects and programmes, dedicated embedded FTA units,
The complementarity between models of FTA is apparent with service providers and FTA units drawing on networks, blurring the divide between the two.
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