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Aligning Simulation Models: A Case Study and Results

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Author Info

  • Robert Axtell
  • Robert Axelrod
  • Joshua M. Epstein
  • Michael D. Cohen

Abstract

This paper develops the concepts and methods of a process we will call "alignment of computational models" of "docking" for short. Alignment is needed to determine whether two models can produce the same results, which in turn is the basis for critical experiments and for tests of whether one model can subsume another. We illustrate our concepts andmethods using as a target a model of cultural transmission built by Axelrod. For comparison we use the Sugarscape model developed by Epstein and Axtell. The two models differ in many ways and, to date, have been employed with quite different aims. The Axelrod model has been used principally for intensive experimentation with parameter variation, and includes only one mechanism. In contrast, the Sugarscape model has been used primarily to generate rich "artificial histories," scenarios that display stylized facts of interest, such as cultural differentiation driven by many different mechanisms including resource availability, migration, trade, and combat. The Sugarscape model was modified so as to reproduce the results of the Axelrod cultural model. Among the questions we address are: what does it mean for two models to be equivalent, how can different standards of equivalence be statistically evaluated, and how do subtle differences in model design affect the results? After attaining a "docking" of the two models, the richer set of mechanisms of the Sugarscape model is used to provide two experiments in sensitivity analysis for the cultural rule of Axelrod's model. Our generally positive experience in this enterprise has suggested that it could be beneficial if alignment and equivalence testing were more widely practiced among computational modellers.

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Bibliographic Info

Paper provided by Santa Fe Institute in its series Working Papers with number 95-07-065.

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Date of creation: Jul 1995
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Handle: RePEc:wop:safiwp:95-07-065

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References

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  1. Lane, David A, 1993. "Artificial Worlds and Economics, Part I," Journal of Evolutionary Economics, Springer, vol. 3(2), pages 89-107, May.
  2. Joshua M. Epstein & Robert L. Axtell, 1996. "Growing Artificial Societies: Social Science from the Bottom Up," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262550253, December.
  3. Robert Axelrod, 1995. "The Convergence and Stability of Cultures: Local Convergence and Global Polarization," Working Papers 95-03-028, Santa Fe Institute.
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Cited by:
  1. Fontana Magda, 2005. "Computer simulations, mathematics and economics," CESMEP Working Papers 200506, University of Turin.
  2. Radax, Wolfgang & Rengs, Bernhard, 2009. "Replication of the Demographic Prisoner’s Dilemma," MPRA Paper 14419, University Library of Munich, Germany.
  3. Brian Tivnan & Matthew Koehler & Matthew McMahon & Matthew Olson & Neal Rothleder & Rajani Shenoy, 2011. "Adding to the Regulator's Toolbox: Integration and Extension of Two Leading Market Models," Papers 1105.5439, arXiv.org.
  4. Pasquale Cirillo & Mauro Gallegati, 2012. "The Empirical Validation of an Agent-based Model," Eastern Economic Journal, Palgrave Macmillan, vol. 38(4), pages 525-547.
  5. Robert Marks, 2007. "Validating Simulation Models: A General Framework and Four Applied Examples," Computational Economics, Society for Computational Economics, vol. 30(3), pages 265-290, October.
  6. Ferrari Gianluigi & Fontana Magda, 2006. "Managing Knowledge in Agent-based Models: Theoretical and Methodological Issues," Department of Economics and Statistics Cognetti de Martiis. Working Papers 200603, University of Turin.
  7. Shah Jamal Alam & Armando Geller & Ruth Meyer & Bogdan Werth, 2010. "Modelling Contextualized Reasoning in Complex Societies with "Endorsements"," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 13(4), pages 6.
  8. Joshua M. Epstein, 2007. "Agent-Based Computational Models and Generative Social Science
    [Generative Social Science Studies in Agent-Based Computational Modeling]
    ," Introductory Chapters, Princeton University Press.
  9. Pasquale Cirillo & Carlo Bianchi & Mauro Gallegati & Pietro Vagliasindi, 2006. "Validating and Calibrating Agent-based Models: a Case Study," Computing in Economics and Finance 2006 277, Society for Computational Economics.
  10. Leigh Tesfatsion, 2002. "Agent-Based Computational Economics," Computational Economics 0203001, EconWPA, revised 15 Aug 2002.
  11. Wolfgang Radax & Bernhard Rengs, 2010. "Prospects and Pitfalls of Statistical Testing: Insights from Replicating the Demographic Prisoner's Dilemma," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 13(4), pages 1.
  12. Bruce Edmonds, 2010. "Bootstrapping Knowledge About Social Phenomena Using Simulation Models," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 13(1), pages 8.
  13. Carl Chiarella & Corrado Di Guilmi, 2010. "The Financial Instability Hypothesis:a Stochastic Microfoundation Framework," Research Paper Series 273, Quantitative Finance Research Centre, University of Technology, Sydney.
  14. Robert L. Axtell, 2000. "Effect of Interaction Topology and Activation Regime in Several Multi-Agent Systems," Working Papers 00-07-039, Santa Fe Institute.

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