Introduction
Exploratory Modeling and Analysis (EMA) is a research methodology that uses computational experiments to analyze complex and uncertain systems (Bankes 1993, 1994). EMA can be understood as searching or sampling over an ensemble of models that are plausible given a priori knowledge, or are otherwise of interest. This ensemble may often be large or infinite in size. Consequently, the central challenge of exploratory modeling is the design of search or sampling strategies that support valid conclusions or reliable insights based on a limited number of computational experiments.
EMA can be contrasted with the use of models to predict system behavior, where models are built by consolidating known facts into a single package (Hodges 1991). When experimentally validated, this single model can be used for analysis as a surrogate for the actual system. Examples of this approach include the engineering models that are used in computer-aided design systems. Where applicable, this...
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References
Agusdinata, D. B. (2008). Exploratory modeling and analysis: A promising method to deal with deep uncertainty. Ph.D. Thesis, Delft University of Technology, Delft, The Netherlands.
Agusdinata, D. B., van der Pas, J. W. G. M., Marchau, V. A. W. J., & Walker, W. E. (2009). Multi-criteria analysis for evaluating the impacts of intelligent speed adaptation. Journal of Advanced Transportation, 43(4), 413–454.
Amram, M., & Kulatilaka, N. (1999). Real options: Managing strategic investment in an uncertain world. Boston, MA: Harvard Business School Press.
Bankes, S. (1993). Exploratory modeling for policy analysis. Operations Research, 41, 435–449.
Bankes, S. (1994). Exploring the foundations of artificial societies: Experiments in evolving solutions to N-player prisoner’s dilemma. In R. Brooks & P. Maes (Eds.), Artificial life IV. Cambridge, MA: MIT Press.
Bankes, S., & Margoliash, D. (1993). Parametric modeling of the temporal dynamics of neuronal responses using connectionist architectures. Journal of Neurophysiology, 69, 980–991.
Breiman, L., Friedman, J. H., Olshen, C. J., & Stone, C. J. (1984). Classification and regression trees. Monterey, CA: Wadsworth.
Brooks, A., Bennett, B., & Bankes, S. (1999). An application of exploratory analysis: The weapon mix problem. Military Operations Research, 4(1), 67–80.
Bryant, B. P., & Lempert, R. (2010). Thinking inside the box: A participatory computer assisted approach to scenario discovery. Technological Forecasting and Social Change, 77, 34–49.
Campbell, D., Crutchfield, J., Farmer, D., & Jen, E. (1985). Experimental mathematics: The role of computation in nonlinear science. Communications of the ACM, 28, 374–384.
Dixon, L., Lempert, R. J., LaTourrette, T., & Reville, R. T. (2007). The federal role in terrorism insurance: Evaluating alternatives in an uncertain world, MG-679-CTRMP. Santa Monica, CA: RAND.
Friedman, J. H., & Fisher, N. I. (1999). Bump hunting in high-dimensional data. Statistics and Computing, 9, 123–143.
Groves, D. G., & Lempert, R. (2007). A new analytic method for finding policy-relevant scenarios. Global Environmental Change, 17, 73–85.
Hamarat, C., & Pruyt, E. (2011a). Energy transitions: Adaptive policy making under deep uncertainty. Proceedings of The 4th International Seville Conference on Future-Oriented Technology Analysis (FTA), Seville, Spain.
Hamarat, C., & Pruyt, E. (2011b). Exploring the future of wind-powered energy. Proceedings of The 29th International Conference of the System Dynamics Society, Washington, DC
Hodges, J. S. (1991). Six (or so) things you can do with a bad model. Operations Research, 39, 355–365.
Hodges, J. S., & Dewar, J. A. (1992). Is it you or your model talking? R-4114. Santa Monica, CA: RAND.
Kohonen, T. (2001). Self-organizing maps (3rd ed.). London: Springer.
Kwakkel, J. H., Walker, W. E., & Marchau, V. A. W. J. (2010). Assessing the efficacy of adaptive airport strategic planning: Results from computational experiments, world conference on transport research (pp. 11–15). Porto, Portugal, July 2010.
Lempert, R. J., Bryant, B. P., & Bankes, S. C. (2008). Comparing algorithms for scenario discovery, WR-557-NSF. Santa Monica, CA: RAND.
Lempert, R. J., Popper, S. W., & Bankes, S. C. (2003). Sha** the next one hundred years: New methods for quantitative long-term strategy analysis, MR-1626-RPC. Santa Monica, CA: RAND.
McGrath, R. G., & MacMillan, I. C. (1995). Discovery driven planning. Harvard Business Review, 73(4), 44–54.
McGrath, R. G., & MacMillan, I. C. (2009). Discovery driven growth: A breakthrough process to reduce risk and seize opportunity. Boston, MA: Harvard Business.
Park, G., & Lempert, R. (1998). The class of 2014: Preserving access to California higher education, MR-971. Santa Monica, CA: RAND.
Pilkey, O. H., & Pilkey-Jarvis, L. (2007). Useless arithmetic: Why environmental scientists can’t predict the future. New York: Columbia University Press.
Pruyt, E.. & Hamarat C. (2010a). The concerted run on the DSB bank: An exploratory system dynamics approach, In Proceedings of the 28th International Conference of the System Dynamics Society. Seoul, Korea.
Pruyt, E., & Hamarat, C. (2010b). The influenza A(H1N1)v pandemic: An exploratory system dynamics approach. Proceedings of the 28th International Conference of the System Dynamics Society. Seoul, Korea.
van der Maaten, L. J. P., & Hinton, G. E. (2008). Visualizing data using t-SNE. Journal of Machine Learning Research, 9, 2579–2605.
Vapnik, V. N. (1995). The Nature of Statistical Learning Theory. Springer.
Walker, W. E., Rahman, S. A., & Cave, J. (2001). Adaptive policies, policy analysis, and policymaking. European Journal of Operational Research, 128(2), 282–289.
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Bankes, S., Walker, W.E., Kwakkel, J.H. (2013). Exploratory Modeling and Analysis. In: Gass, S.I., Fu, M.C. (eds) Encyclopedia of Operations Research and Management Science. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1153-7_314
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