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Stochastic Evolution with Slow Learning

Author

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  • Alan Beggs

Abstract

This paper studies the extent to which diffusion approximations provide a reliable guide to equilibrium selection results in finite games. It is shown that they do for a class of finite games with weak learning provided that limits are taken in a certain order. The paper also shows that making mutation rates small does not in general select a unique equilibrium but making selection strong does.

Suggested Citation

  • Alan Beggs, 2000. "Stochastic Evolution with Slow Learning," Economics Series Working Papers 33, University of Oxford, Department of Economics.
    • Handle: RePEc:oxf:wpaper:33
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    File URL: https://ora.ox.ac.uk/objects/uuid:400df858-2bd1-4cd4-aa0e-3db05037df7c
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    Cited by:

    1. Dai, Darong, 2010. "一般化Moran过程中的合作演化 [The Evolution of Cooperation in a Generalized Moran Process]," MPRA Paper 40261, University Library of Munich, Germany.
    2. Dai, Darong, 2010. "The Evolution of Cooperation in a Generalized Moran Process," MPRA Paper 40511, University Library of Munich, Germany.
    3. Sandholm,W.H., 1999. "Markov evolution with inexact information," Working papers 15, Wisconsin Madison - Social Systems.
    4. Dai, Darong, 2012. "Learning Nash Equilibria," MPRA Paper 40040, University Library of Munich, Germany.
    5. Beggs, A.W., 2007. "Large deviations and equilibrium selection in large populations," Journal of Economic Theory, Elsevier, vol. 132(1), pages 383-410, January.
    6. Izquierdo, Luis R. & Izquierdo, Segismundo S. & Gotts, Nicholas M. & Polhill, J. Gary, 2007. "Transient and asymptotic dynamics of reinforcement learning in games," Games and Economic Behavior, Elsevier, vol. 61(2), pages 259-276, November.
    7. Sandholm, William H., 2003. "Evolution and equilibrium under inexact information," Games and Economic Behavior, Elsevier, vol. 44(2), pages 343-378, August.

    More about this item

    Keywords

    equilibrium selection; diffusion approximation; evolutionary game theory; risk dominance;
    All these keywords.

    JEL classification:

    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games

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