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Two Notes on Replication in Evolutionary Modelling

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  • Riechmann, Thomas

Abstract

Replicator dynamics and replication as used in evolutionary algorithms are, due to their most basic forms, structurally the same. This short note will prove this thesis. Although this finding is clear cut and easy to show, it is of great importance for the not yet united families of game theorists on the one hand and evolutionary programmers on the other, meaning that it is perfectly legal and correct to mutually use the tools and findings of each other.

Suggested Citation

  • Riechmann, Thomas, 2001. "Two Notes on Replication in Evolutionary Modelling," Hannover Economic Papers (HEP) dp-239, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    • Handle: RePEc:han:dpaper:dp-239
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    File URL: http://diskussionspapiere.wiwi.uni-hannover.de/pdf_bib/dp-239.pdf
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    References listed on IDEAS

    as
    1. Fudenberg, Drew & Levine, David, 1998. "Learning in games," European Economic Review, Elsevier, vol. 42(3-5), pages 631-639, May.
    2. Arifovic, Jasmina, 1994. "Genetic algorithm learning and the cobweb model," Journal of Economic Dynamics and Control, Elsevier, vol. 18(1), pages 3-28, January.
    3. Binmore, K. & Samuelson, L., 1993. "An Economist's Perspective on the Evolution of Norms," Working papers 9323, Wisconsin Madison - Social Systems.
    4. Riechmann, Thomas, 2001. "Genetic algorithm learning and evolutionary games," Journal of Economic Dynamics and Control, Elsevier, vol. 25(6-7), pages 1019-1037, June.
    5. Jack Hirshleifer & Juan Carlos Martinez Coll, 1992. "Selection, Mutation, and the Preservation of Diversity in Evolutionary Games," UCLA Economics Working Papers 648, UCLA Department of Economics.
    6. Thomas Riechmann, 2001. "Evolutionary Learning in the Ultimatum Game," Computing in Economics and Finance 2001 91, Society for Computational Economics.
    7. Gale, John & Binmore, Kenneth G. & Samuelson, Larry, 1995. "Learning to be imperfect: The ultimatum game," Games and Economic Behavior, Elsevier, vol. 8(1), pages 56-90.
    8. Thomas Riechmann, 1999. "Learning and behavioral stability An economic interpretation of genetic algorithms," Journal of Evolutionary Economics, Springer, vol. 9(2), pages 225-242.
    9. Drew Fudenberg & David K. Levine, 1998. "The Theory of Learning in Games," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262061945, December.
    10. Binmore, K. & Samuelson, L. & Gale, J., 1993. "Learning to be Imperfect: The Ultimatum Game," Working papers 9325, Wisconsin Madison - Social Systems.
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    Cited by:

    1. Ian McCarthy, 2008. "Simulating Sequential Search Models with Genetic Algorithms: Analysis of Price Ceilings, Taxes, Advertising and Welfare," Caepr Working Papers 2008-010, Center for Applied Economics and Policy Research, Economics Department, Indiana University Bloomington.

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    More about this item

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games

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