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Empirical Calibration of Simulation Models

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Author Info
Claudia Werker
Thomas Brenner ()

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Abstract

This 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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Paper provided by Max Planck Institute of Economics, Evolutionary Economics Group in its series Papers on Economics and Evolution with number 2004-10.

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Length: 16 pages
Date of creation: May 2004
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Handle: RePEc:esi:evopap:2004-10

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Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. 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.
  2. 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.
  3. 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. [Downloadable!] (restricted)
  4. 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.
  5. Kydland, Finn E & Prescott, Edward C, 1996. "The Computational Experiment: An Econometric Tool," Journal of Economic Perspectives, American Economic Association, vol. 10(1), pages 69-85, Winter. [Downloadable!] (restricted)
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  6. 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. [Downloadable!] (restricted)
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(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. 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. [Downloadable!]
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  2. 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. [Downloadable!] (restricted)
    Other versions:
  3. Garavaglia, C., 2004. "History friendly simulations for modelling industrial dynamics," ECIS Working Papers 04.19, Eindhoven Centre for Innovation Studies, Eindhoven University of Technology. [Downloadable!]
  4. T. Brenner & C. 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.
  5. 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. [Downloadable!]
  6. 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, Springer, vol. 30(3), pages 195-226, October. [Downloadable!] (restricted)
  7. Thomas Brenner & Claudia Werker, 2007. "A Taxonomy of Inference in Simulation Models," Computational Economics, Springer, vol. 30(3), pages 227-244, October. [Downloadable!] (restricted)
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