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The Evolution of Algorithmic Learning Rules : A Global Stability Result

Author

Listed:
  • Anderlini, L
  • Sabourian, H

Abstract

This paper considers the dynamic evolution of algorithmic (recursive) learning rules in a normal form game. It is shown that the system - the population frequencies - is globally stable for any arbitrary N-player normal form game, if the evolutionary process is algorithmic and the "birth process" guarantees that an appropriate set of "smart" rules is present in the population.

Suggested Citation

  • Anderlini, L & Sabourian, H, 1996. "The Evolution of Algorithmic Learning Rules : A Global Stability Result," Economics Working Papers eco96/05, European University Institute.
    • Handle: RePEc:eui:euiwps:eco96/05
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    References listed on IDEAS

    as
    1. Blume, Lawrence & Easley, David, 1992. "Evolution and market behavior," Journal of Economic Theory, Elsevier, vol. 58(1), pages 9-40, October.
    2. Megiddo, Nimrod, 1989. "On computable beliefs of rational machines," Games and Economic Behavior, Elsevier, vol. 1(2), pages 144-169, June.
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    Cited by:

    1. Josephson, Jens, 2008. "A numerical analysis of the evolutionary stability of learning rules," Journal of Economic Dynamics and Control, Elsevier, vol. 32(5), pages 1569-1599, May.

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    More about this item

    Keywords

    ECONOMETRICS ; LEARNING;

    JEL classification:

    • C70 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - General
    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • C79 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Other
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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