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The Evolution of Algorithmic Learning Rules: A Global Stability Result

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Author Info

  • Luca Anderlini

    (St. John's College, Cambridge)

  • Hamid Sabourian

    (King's College, Cambridge)

Abstract

This paper consider 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. The result is independent of the nature of the evolutionary process; in particular it does not require the dynamics of the system to be `monotonic in payoffs' - those rules which do better in terms of payoffs grow faster than those who do less well. The paper also demonstrates that any limit point of the distribution of actions in such an evolutionary process corresponds to a Nash equilibrium (pure or mixed) of the underlying game if the dynamics of the system are continuous and monotonic in payoffs.

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Bibliographic Info

Paper provided by EconWPA in its series Game Theory and Information with number 9510001.

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Length: 53 pages
Date of creation: 12 Oct 1995
Date of revision:
Handle: RePEc:wpa:wuwpga:9510001

Note: Type of Document - LaTex/PostScript; prepared on EmTex; to print on PostScript; pages: 53 ; figures: included
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Web page: http://128.118.178.162

Related research

Keywords: Evolution; Learning Rules; Computability; Monotonic Dynamics;

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References

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