Empirical calibration of simulation models
AbstractThis paper discusses how the results of simulation models can be made more reliable and the method of simulating therefore more widely applicable. We suggested to calibrate simulation models empirically and developed a methodology based on Critical Realism in order to so. We suggested combining the procedures of two strands of literature: the empirical underpinning of the assumptions (like in microsimulations) and the empirical check of the implications (like in Bayesian inference). Both these strands of literature are mainly concerned with predicting future developments. We, instead, aim to infer statements about causal relations and characteristics of a set of systems or dynamics, such as, e.g., the development of an industry, that have a general validity for this set of systems or dynamics. In other words, instead of deriving probabilistic predictions of the future and statements of the current situation and dynamics of one single system we developed a methodology to gain general statements about the features of systems and dynamics.
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Bibliographic InfoPaper provided by Eindhoven Center for Innovation Studies (ECIS) in its series Eindhoven Center for Innovation Studies (ECIS) working paper series with number 04.13.
Date of creation: 2004
Date of revision:
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Web page: http://ecis.ieis.tue.nl/
Other versions of this item:
- Claudia Werker & Thomas Brenner, 2004. "Empirical Calibration of Simulation Models," Papers on Economics and Evolution 2004-10, Max Planck Institute of Economics, Evolutionary Economics Group.
- Thomas Brenner & Claudia Werker, 2004. "Empirical Calibration of Simulation Models," Computing in Economics and Finance 2004 89, Society for Computational Economics.
- B40 - Schools of Economic Thought and Methodology - - Economic Methodology - - - General
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
This paper has been announced in the following NEP Reports:
- NEP-ALL-2004-07-18 (All new papers)
- NEP-CBE-2004-07-18 (Cognitive & Behavioural Economics)
- NEP-CMP-2004-07-18 (Computational Economics)
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- Finn E. Kydland & Edward C. Prescott, 1994.
"The computational experiment: an econometric tool,"
9420, Federal Reserve Bank of Cleveland.
- Schwerin, Joachim & Werker, Claudia, 2003. "Learning innovation policy based on historical experience," Structural Change and Economic Dynamics, Elsevier, vol. 14(4), pages 385-404, December.
- Johann Peter Murmann & Thomas Brenner, 2003. "The Use of Simulations in Developing Robust Knowledge about Causal Processes: Methodological Considerations and an Application to Industrial Evolution," Computing in Economics and Finance 2003 66, Society for Computational Economics.
- Dominique Foray & Robin Cowan, 2002. "Evolutionary economics and the counterfactual threat: on the nature and role of counterfactual history as an empirical tool in economics," Journal of Evolutionary Economics, Springer, vol. 12(5), pages 539-562.
- Malerba, Franco, et al, 1999. "'History-Friendly' Models of Industry Evolution: The Computer Industry," Industrial and Corporate Change, Oxford University Press, vol. 8(1), pages 3-40, March.
- Franco Malerba & Luigi Orsenigo, 2002. "Innovation and market structure in the dynamics of the pharmaceutical industry and biotechnology: towards a history-friendly model," Industrial and Corporate Change, Oxford University Press, vol. 11(4), pages 667-703, August.
- Cowan, Robin & Jonard, Nicolas, 2006.
"Structural Holes, Innovation and the Distribution of Ideas,"
UNU-MERIT Working Paper Series
039, United Nations University, Maastricht Economic and social Research and training centre on Innovation and Technology.
- Robin Cowan & Nicolas Jonard, 2007. "Structural holes, innovation and the distribution of ideas," Journal of Economic Interaction and Coordination, Springer, vol. 2(2), pages 93-110, December.
- Roberto Leombruni & Matteo Richiardi & Nicole J. Saam & Michele Sonnessa, 2005.
"A Common Protocol for Agent-Based Social Simulation,"
LABORatorio R. Revelli Working Papers Series
47, LABORatorio R. Revelli, Centre for Employment Studies.
- Matteo Richiardi & Roberto Leombruni & Nicole J. Saam & Michele Sonnessa, 2006. "A Common Protocol for Agent-Based Social Simulation," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 9(1), pages 15.
- Garavaglia, C., 2004. "History friendly simulations for modelling industrial dynamics," Eindhoven Center for Innovation Studies (ECIS) working paper series 04.19, Eindhoven Center for Innovation Studies (ECIS).
- Thomas Brenner & Claudia Werker, 2006. "A Practical Guide to Inference in Simulation Models," Papers on Economics and Evolution 2006-02, Max Planck Institute of Economics, Evolutionary Economics Group.
- Giorgio Fagiolo & Alessio Moneta & Paul Windrum, 2007. "A Critical Guide to Empirical Validation of Agent-Based Models in Economics: Methodologies, Procedures, and Open Problems," Computational Economics, Society for Computational Economics, vol. 30(3), pages 195-226, October.
- Thomas Brenner & Claudia Werker, 2007. "A Taxonomy of Inference in Simulation Models," Computational Economics, Society for Computational Economics, vol. 30(3), pages 227-244, October.
- Garavaglia, Christian, 2010. "Modelling industrial dynamics with "History-friendly" simulations," Structural Change and Economic Dynamics, Elsevier, vol. 21(4), pages 258-275, November.
- Stuart Rossiter & Jason Noble & Keith R.W. Bell, 2010. "Social Simulations: Improving Interdisciplinary Understanding of Scientific Positioning and Validity," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 13(1), pages 10.
- John Foster, 2011. "Evolutionary macroeconomics: a research agenda," Journal of Evolutionary Economics, Springer, vol. 21(1), pages 5-28, February.
- Giorgio Fagiolo & Paul Windrum & Alessio Moneta, 2006. "Empirical Validation of Agent Based Models: A Critical Survey," LEM Papers Series 2006/14, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
- Garcia, Rosanna & Rummel, Paul & Hauser, John, 2007. "Validating agent-based marketing models through conjoint analysis," Journal of Business Research, Elsevier, vol. 60(8), pages 848-857, August.
- Paul Windrum & Giorgio Fagiolo & Alessio Moneta, 2007. "Empirical Validation of Agent-Based Models: Alternatives and Prospects," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 10(2), pages 8.
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