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Fast Convergence in Evolutionary Equilibrium Selection

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  • H Peyton Young
  • Gabriel E. Kreindler
Abstract
Stochastic learning models provide sharp predictions about equilibrium selection when the noise level of the learning process is taken to zero. The difficulty is that, when the noise is extremely small, it can take an extremely long time for a large population to reach the stochastically stable equilibrium. An important exception arises when players interact locally in small close-knit groups; in this case convergence can be rapid for small noise and an arbitrarily large population. We show that a similar result holds when the population is fully mixed and there is no local interaction. Selection is sharp and convergence is fast when the noise level is 'fairly' small but not extremely small.

Suggested Citation

  • H Peyton Young & Gabriel E. Kreindler, 2011. "Fast Convergence in Evolutionary Equilibrium Selection," Economics Series Working Papers 569, University of Oxford, Department of Economics.
    • Handle: RePEc:oxf:wpaper:569
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    References listed on IDEAS

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    More about this item

    Keywords

    Stochastic stability; Logit learning; Markov chain; Convergence time;
    All these keywords.

    JEL classification:

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

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