The promised future situation contains sequencing of genes, characterisation of proteins, databases, dynamic models and so on.
The role of technological expectations in a mixed model of international diffusion of process innovations:
Macro forces and their likely evolution are described in BASF‘Global economy Scenarios',where econometric models elaborate basic data in both qualitative and quantitative terms,
A model for uncertainty and strategic foresight In the prior sections, we sketched the strategic foresight approaches that emerged from our data through
and what responses it could adopt (e g. postponement of the launch of new models: response uncertainty.
and a model of how companies create enduring continuity needed for sustainable development (Brundtland 1987). This paper suggests a dynamic framework of continual learning to enable businesses to anticipate
In this context, Section 2 outlines that current models responsible for moulding a business's competitive advantage sustainably are weak in the nature of stakeholders'involvement in strategic partnerships.
Figure 1 depicts the business's Activities Model and shows the main value-creating activities that a business needs to sustain it in the long term.
Theactivities Model (Figure 1) is based on the quantum leaps model devised by Shelton (1997), which describes the necessary capabilities needed to transform our organisations
firms should work out their own model that brings new opportunities through dialogue and interaction, being transparent and accountable to stakeholders (Brinch-Pedersen 2003).
The Maturity Model is an attempt to build FTA's contributions to a structure heavily dependent on the flow of ideas, data and information into a business and its network decision-making in its place in society.
The aim of the Maturity Model is to shape a possible business path towards sustainable developmment outlining how the network value activities ought to evolve in time to shape business sustainability.
The Maturity Model suggested in Table 3 (Cagnin 2005) uses the notion of evolution in
The design of the Sustainability Maturity Model is founded on universal principles as well as the maturity of behaviours that can lead to the development of a mature business throughout its network of relationships (Cagnin 2005)
As a reminder, the model seeks to enable a common strategy and/or strategies aligned across the network,
and support decision-making effectively by using models, such as sympoiesis, that emphasise the creative aspect of living systems which,
legal Run the business Implementing the vision of sustainability Business Sustainability Maturity Model Business Path to Sustainability Comparing present performance (as it is) with the business
Project management process maturity (PM) 2 model. Journal of Management in Engineering, 18, no. 3: 150 5. Larsen, A. H. 2003.
Mathematical and Computer Modelling 30, no. 9: 179 92. Losada, M. 2001. The art of business coaching.
Model Model Model Framework Framework Framework SIGMA SIGMA SIGMA LCA LCA LCA NS NS NS ISO 14000 ENAS SA800 CP
Benchmarking was accomplished by comparing the visualizations with the mental models of IA leads experts who have used,
if large differences were found between them and the leaders'mental models of their areas.
and add detail to their mental models. After completion of the benchmarking activity with Sandia-specific visualizations
one of the purposes of generating a Sandia-specific map of IAS was to benchmark the map against the mental models of IA leads.
about his mental model of:(1) the CIS area, and (2) perceived overlaps between CIS and the other four areas,
A graphic describing the CIS IA lead's mental model of the overlap between CIS
and correspond well to the mental model's view of unique space. Significant overlaps between CIS and ES at the bottom of the main CIS cluster
These two areas, ES and MST, show the Fig. 2. CIS IA leader's mental model of CIS overlaps.
which correlates well with the mental model of Fig. 2. EP shows two small areas of overlap with CIS,
This also matches the mental model quite well. One area of particular interest on the map is that found in cluster at the lower middle of the graph.
and climate modeling, labeled A b, and C, respectively in the figure. Some of these were anticipated by the CIS investment team in that the FY2005 calls (issued in March 2004) reflected an increased interest in informatics,
integrating them into their mental models, and foreseeing how overlaps, collaborations, and new opportunities can benefit the return on investment to their IAS,
In a parallel effort, we plan to investigate different models of impact and join the best of those to our visualizations to answer questions related to return on investment 11.
roadmapping comes quite close to system dynamic modelling techniques, yet roadmapping is still more of a technique for strategic focussing Downloaded by University of Bucharest at 05:05 03 december 2014 826 T. Ahlqvist et al.
In order to realise this, we propose a model that separates roadmap knowledge spaces from the roadmap scope.
Our model separates roadmaps with R&d scope and roadmaps with systemic scope. Figure 3 shows an ideal model of roadmap knowledge spaces.
In the figure we have singled out four knowledge spaces that are important in the context of RTOS (see also Table 1). The model combines the four knowledge spaces with three basic temporal scales (past, present, and futures.
Thus, our model presumes that there is a‘scale continuum'on which technological development can be interpreted: at one end of the continuum there is technology as a mere object (a solution),
In our model, the knowledge space that analyses these wider socio-technical constellations is the strategy space.
Our model starts with a presupposition that in the technology and social/actor spaces the exploration of the more radical futures is restricted usually by the overaal need to identify certain actions in the present.
In our ideal model, we have depicted, for example, disruptive futures (phenomena that change the name of the game),
The integrative methodology rested on the model of expansive learning (Engeström 2001. In the process, two practical methods were added to the model of expansive learning.
First, impact evaluation was used to gain a systematic view of the past (see Halonen, Kallio, and Saari 2010.
for example, through the application of life cycle analysis. The second aspect was to open the field towards more efficient use of ICTS in the processes, such as solutions for distance-based monitoring, the use of building information models,
We presented a model of a process-based roadmap with four knowledge spaces, which extends the horizons of roadmappiing We also presented four case examples the Building Service Roadmap, SSB Network, Construction Machinery Roadmap,
These include innovation system modelling, text mining of Science, Technology & Innovation(‘ST&I')information resources, trend analyses, actor analyses,
2. 3. Innovation system conceptual modelling A variety of approaches aim to capture the systemic processes by
We note several innovation system conceptual modelling efforts pertaining particularly to energy technology, given our case focus on solar cells.
Wenk and Kuehn (1977) advance TDS as a form of socio-technical system conceptual modelling to help identify the pivotal elements involved in innovation.
TDS models can serve to identify the key institutional actors, spelling out enterprise requirements, and spotlighting leverage points to affect the prospects of successful commercialisation.
we favour TDS modelling to do this compactly and informatively. Stage 2, in contrast, is heavily empirical.
3. successful model for healthy-aging society; and 4. secure life. 2. 2 Delphi Delphi is characterized by repeated questions for the collective convergence of opinions,
Cagnin and Loveridge (2011) discuss challenges as well as detailed models and processes. They describe how a business can become more and more receptive to foresight results,
which established models and methods are suitable for such dialogues. Welp et al. 2006) have investigated this area for science-based stakeholder dialogues.
2006) with relevance to strategic dialogues Model/framework Relevance to strategic dialogues Rational actor paradigm (RAP) The RAP assumes that all individuals maximize their personal benefit without communication with,
since they are answerable to the members of the trade body Bayesian learning This model describes the beliefs an individual has about the world
reservations and sensitivities but need to do so without assuming these can necessarily be influenced The process of beliefs being updated that is captured by the model of Bayesian learning applies in several contexts during a strategic dialogue.
Strategic dialogues need to be flexible enough to cope with this kind of change Organizational learning In such models,
5. 3 Strategic dialogue to develop a model for public private partnerships A third example of a successful strategic dialogue was the definition of a novel type of innovation cluster across academia and industry implemented as public private partnerships.
A range of views on numerous aspects of cooperation models was collected from selected stakeholders from academia
and modelling work on areas such as health. Table III List of thematic groups, drivers and trends identified Theme Drivers and trends Global governance and political economy Rise of the BRICS Global trade falters The emergence of new
middle classes Uncertain results for banking regulation A challenge to liberal democracy models Conflict follows geopolitical shifts Terrorism continues to pose a threat to security A multi-polar governance system Religion
mobility and higher education Learning as a lifelong behaviour Vocational skills gaps Technological development Converging technologies The increasing pace of technological change Technology platforms Open innovation models Death of intellectual property?
models Cloud computing Peak oil Energy security Ageing populations ICT in education Banking regulation? Table V Challenges identified with their potential research implications Examples of challenges identified Potential research implications Energy Ireland is dependent on external sources of energy supplies at present
and where the traditional political and governance models are being disrupted. How can Ireland maintain its standing
The IPC, established by the Strasbourg Agreement of 1971, provides for a hierarchical system of language-independent symbols for the classification of patents and utility models according to the different areas of technology to
For example, simulation models can explore the repercussions of changes in major (external) parameters, as well as the outcome of policy options and other actions.
in contrast, can establish casual relations (without which models can be misleading), and identify major discontinuities in trends and/or new ones.
Both Hamarat et al. 11 and Kwakkel and Pruit 12 apply an approach to forecasting that uses an ensemble of different models to explore a multiplicity of plausible futures (Exploratory Modelling
they propose a model to calculate the TLC for a technology based on multiple patent-related indicators.
In this context, the proposed model focuses on devising and assessing patent-based TLC indicators using a Nearest Neighbour Classifier,
and future research should take this into account to test the validity of the proposed model.
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.
EMA is an iterative model-driven approach for designing dynamic adaptive policies and it deals with uncertainties by using an ensemble of different models to explore a multiplicity of plausible futures (or scenarios.
Policy options across the future world ensemble are calculated and compared in an iterated process until the suggested policy provides satisfying results.
Kwakkel and Pruit 12 present three applications of EMA, using different modelling approaches, in three different technical domains and related to three different grand challenges, grounded in a system perspective.
These modelling efforts are aimed at: i) understanding plausible dynamics for mineral and metal scarcity, ii) developing a hybrid model for airport performance calculations to underpin an adaptive strategic plan,
and iii) identifying crucial factors that affect a transition towards more sustainable functioning of the electricity sector.
in order to better understand the systemic and structural transformations of complex systems, ii) inclusion of a multiplicity of perspectives, worldviews, mental models or quantitative models,
Results indicate that a wide variety of hybrid value creation models with novel configurations of innovation actors emerged.
while there is as yet no clear methodological answer to the identification issue there has been some institutionalised responses and new organisational models of FTA,
12 J. H. Kwakkel, E. Pruyt, Exploratory Modeling and Analysis, an approach for model-based foresight under deep uncertainty, Technol.
Tuomi 14 suggests that the ontological unpredictability4 of innovative process cannot be removed by more accurate data or incremental improvements in existing predictive models.
the development of online models accessible to a whole community, or the engagement of a wider group of participants in data analysis (for the latter, see Cooke and Buckley 7). 3 In this regard,
and predictive models when disruptive and downstream innovation become more frequent, 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./
who see FTA EXERCISES as attempts to collect knowledge about‘posits'or possible futures, their plausibility and limits, their internal consistency and conformity with models and data, their consistency with expert judgement,
Qualitative data can provide additional evidence to quantitative models by inclusion of new indicators created from quantified expert judgments.
Cunningham and van der Lei 28 use such an approach for models providing support to decision-making on the selection of new technologies and discuss the issue of providing equilibrium between different groups of experts and stakeholders.
The mapping identified only three quantitative methods (bibliometrics, modelling and simulation, trend extrapolation), highlighting that they were combined with literature review, scenarios and expert panels.
linking ecosystem change and human well-being by combining qualitative storyline development and quantitative modelling through several iterations between both parts 44.
especially the marrying of quantitative modelling and foresight seems to be unexplored rather. The idea that one can forecast
Firstly, it is assumed often that models belong exclusively to the quantitative domain and have objective predictive power.
Models are simplified a representation of reality and can be quantitative or qualitative, depending on the type of data they rely on.
The latter, in the case of quantitative models, take the form of numbers with associated probability distributions
and confidence intervals (depending on the model). However, in the case of long-term horizons, such informed estimates have limited only predictive value.
the value of models is not so much in their ability to tell us with a degree of certainty what will happen to society,
Therefore, a model is more valuable as an analytical rather than a predictive tool. This means that both quantitative and qualitative tools and techniques should be judged not so much against the accuracy of their prediction on the future
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,
and thus tailoring foresight phases to different foresight functions. 15 Typically, quantitative models present higher credibility for shorter time horizons,
An exception is the International Futures Model, which can be used to examine long-term and interacting global development issues 73.393 K. Haegeman et al./
including PASHMINA (Paradigm Shifts Modelling and Innovative Approaches) and EFONET (Energy Foresight Network) and is rapporteur of the EC Working group Global Europe 2030 2050.
In this paper, we build a model to calculate the TLC for an object technology based on multiple patent-related indicators.
The model includes the following steps: first, we focus on devising and assessing patent-based TLC indicators.
and fitting of growth models to project possible future trends 5. Most trend projection is naïve i e.,
A research team from MIT 11 studied the development trends of power transmission technology and aero-engine technology by S-curve modelling.
and hope that would help decision makers estimate its future development trends. 2. Methodology The model that we build to calculate the TLC for an object technology includes the following steps:
but also monitoring and intelligence, matrices (analogies), modelling, and a hint of roadmapping. More importantly, we suggest that TLC would be complemented by informal
and tries to answer the question on the validity of evolutionary models of technological change. After some introductory thoughts in the first part, it is tried in the second part to summarize in five points some of the still missing pieces to complete the puzzle to developing a firmly based Evolutionary theory of technological change (ETTC.
making six fundamental theoretical considerations that were accounted not yet for in formal models and/or simulations of technological systems stand out.
complex networks, simulation modeling of CAS and the search of vast databases. Such convergence has conducted to a rejuvenation and growth in FTA METHODS and practice,
what has delayed the entrenching of evolutionary economics as a powerful alternative to other current economic models.
some important modeling attempts were undertaken along with the last decades and I think that some of the above mentioned points are hindering the development of working computational algorithms to simulate technological evolution.
and models were advanced. The mathematical tools that began to be employed in economics (as well as in technological forecasting) starting in the 1970s had been developed by mathematical biologists in the 1920s
More recently, Devezas and Corredine 12 proposed a generalized diffusion-learning model to explain the succession of long waves in the techno-economic world,
With this short collection of ideas I wish to suggest that a firmly conceptually based danthropology of techniquet is still lacking in the current attempts of model building and formal theorizing of an ETTC.
but there is relatively few work proposing formal models and using simulation methods in this field. Among the reasons for this lack of practical-oriented works we have referred to:
for short) that uses so-called dsoft computingt models of complex adaptive systems (CAS) that encompasses several methods of simulation
Theoretically and methodologically this approach makes possible the construction of models from the level of processes that are immediately and empirically observable, namely the local interactions of single units (agents) governed by local rules.
The formal mathematical models developed in the past two decades and most often used are (mentioning only some important publications for each approach):
Percolation models. It is a numerical simulation method for the search of complex technology spaces based on percolation theory,
In technology and science GAS have been used as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems,
and that until now were considered not suitably in previous modeling attempts. It is presented below a short resume of these missing fundamental considerations:
As a first step toward a research agenda for future development of TFA I propose the realization of an international seminar in this field (Evolutionary theory of technological change) bringing together specialists in evolutionary model building and digital Darwinism to discuss the existing approaches
model of technological change, Technol. Forecast. Soc. Change 3 (1971) 75 88.10 C. Marchetti, Society as a learning system:
Change 71 (2004) 881 896.35 G. Silverberg, B. Verspagen, A percolation model of innovation in complex technology spaces, J. Econ.
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,
Model-based decision support Deep uncertainty Adaptive policy-making Exploratory Modeling and Analysis 1. Introduction Conceptual, formal, 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,
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,
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
(1) the appropriate conceptual models which describe the relationships among the key driving forces that shape the long-term future,(
2) the probability distributions used to represent uncertainty about key variables and parameters in the mathematical representations of these conceptual models,
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
A possible quantitative approach for operationalizing the Adaptive Policy-making Framework is by using Exploratory Modeling and Analysis 36 38.
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,
or which aspects of a problem are important, are taken explicitly into account and assessed for their consequences. 5. Conclusions We have proposed an iterativemodel-based approach for developing adaptive policies under uncertainty.
The models were used here to explicitly explore a plethora of uncertainties in order to assess the implications of these uncertainties for decision-making.
Exploratory Modeling, Real Options analysis and Policy design which is supported by The next Generation Infrastructures (NGI) Foundation.
a conceptual basis for uncertainty management in model-based decision support, Integr. Assess. 4 (2003) 5 17.5 G. Yucel,
the actor-options framework for modelling socio-technical systems, in: Policy analysis, Delft University of Technology, Delft, 2010.6 P. Eykhoff, System Identification:
the first decade of global modelling, John Wiley & Sons, Chichester, 1982.15 E. A. Eriksson, K. M. Weber, Adaptive foresight:
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.
Exploratory modeling and analysis: a promising method to deal with deep uncertainty, in: Technology policy and Management, Delft University of Technology, Delft, 2008, p. 285.37 E. Pruyt, J. Kwakkel, A bright future for system dynamics:
The 30th International Conference of the System Dynamics Society, St gallen, Switzerland, 2012.38 S. Bankes, Exploratory modeling for policy analysis, Oper.
Systems thinking and Modeling for a Complex World, Mcgraw-hill, 2000.52 G. Van Rossum, Python Reference manual, CWI, Amsterdam, 1995.53 Ventana Systems Inc.,Vensim Reference manual, Ventana
In his Phd research, he focuses on long term decision-making under deep uncertainty using the Exploratory Modeling and Analysis method.
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,
Received 14 may 2011 Received in revised form 12 july 2012 Accepted 27 august 2012 Available online 29 october 2012 Exploratory Modeling
using different modeling approaches, in three different technical domains. In the first case, EMA is combined with System Dynamics (SD) to study plausible dynamics for mineral and metal scarcity.
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
Future-oriented technology analysis Exploratory Modeling and Analysis Deep uncertainty System dynamics Adaptive policymaking Agent-based modeling 1. Introduction Future-oriented technology analysis (FTA) is understood as an umbrella label for various approaches
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.
The RAND Corporation developed a technique called Exploratory Modeling and Analysis (EMA) tailored to this.
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.
Section 5 contains the conclusions. 2. Exploratory modeling and analysis Various scientific fields including the environmental sciences, transportation research, economics,
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.
Porter et al. 1, in their agenda setting paper on FTA, explicitly mention EMA as being of potential interest to FTA.
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:(
i) to explore plausible dynamics of mineral/metal abundance/scarcity, and (ii) to identify, describe and visualize interesting scarcity scenarios for the client.
Both objectives were achieved at first by means of traditional System Dynamics modeling and manual exploration of the influence of key assumptions, changing one assumption at a time.
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.
Third party risk Methodology developed by the National Air Traffic Services (NATS) for third-party risk 42,43 the NATS methodology has been extended to apply to multiple runways 49,50.
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.
These cases differed in the modeling paradigm that was used, in the application domain, and in the type of problem being investigated.
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,
and discussed have shown that EMA can be used to handle diverse types of uncertainties in combination with three quite distinct modeling approaches.
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:
Prediction, Island Press, Washington, D c, 2000.12 S. Bankes, Exploratory modeling for policy analysis, Oper. Res. 4 (1993) 435 449.13 D. B. Agusdinata, Exploratory Modeling and Analysis:
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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,
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