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Genetic Algorithms and Economic Evolution

Author

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  • Thomas Riechmann

    (University of Hannover)

Abstract

This paper tries to connect the theory of genetic-algorithm (GA) learning to evolutionary game theory. It is shown that economic learning via genetic algorithms can be described as a specific form of evolutionary game. It will be pointed out that GA learning results in a series of near Nash equilibria, which, during the learning process, build up finally to reach a neighborhood of an evolutionarily stable state. In order to clarify this point, a concept of evolutionary stability of genetic populations is developed. It then becomes possible to explain the reasons for the dynamics of standard GA learning models as well as those of extensions to this standard.

Suggested Citation

  • Thomas Riechmann, 1999. "Genetic Algorithms and Economic Evolution," Computing in Economics and Finance 1999 1011, Society for Computational Economics.
    • Handle: RePEc:sce:scecf9:1011
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    Cited by:

    1. Boldea Bogdan Ion & Boldea Costin-Radu & Stanculescu Mircea, 2009. "An Adaptative Evolutionary Model Of Financial Investors," Annals of Faculty of Economics, University of Oradea, Faculty of Economics, vol. 4(1), pages 897-901, May.
    2. Jie-Shin Lin & Chris Birchenhall, 2000. "Learning And Adaptive Artificial Agents: An Analysis Of Evolutionary Economic Models," Computing in Economics and Finance 2000 327, Society for Computational Economics.

    More about this item

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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