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Fast Convergence in Evolutionary Equilibrium Selection

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  • H Peyton Young
  • Gabriel E. Kreindler
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    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.

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    Bibliographic Info

    Paper provided by University of Oxford, Department of Economics in its series Economics Series Working Papers with number 569.

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    Date of creation: 01 Sep 2011
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    Handle: RePEc:oxf:wpaper:569

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    Related research

    Keywords: Stochastic stability; Logit learning; Markov chain; Convergence time;

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    1. Benaim, Michel & Weibull, Jörgen W., 2000. "Deterministic Approximation of Stochastic Evolution in Games," Working Paper Series 534, Research Institute of Industrial Economics, revised 30 Oct 2001.
    2. L. Blume, 2010. "The Statistical Mechanics of Strategic Interaction," Levine's Working Paper Archive 488, David K. Levine.
    3. Lawrence Blume, 1993. "The Statistical Mechanics of Best-Response Strategy Revision," Game Theory and Information 9307001, EconWPA, revised 26 Jan 1994.
    4. Lawrence E. Blume, 1994. "How Noise Matters," Game Theory and Information 9407002, EconWPA, revised 27 Jul 1994.
    5. Glen Ellison, 2010. "Learning, Local Interaction, and Coordination," Levine's Working Paper Archive 391, David K. Levine.
    6. Young, H Peyton, 1993. "The Evolution of Conventions," Econometrica, Econometric Society, vol. 61(1), pages 57-84, January.
    7. Dunia López-Pintado, 2006. "Contagion and coordination in random networks," International Journal of Game Theory, Springer, vol. 34(3), pages 371-381, October.
    8. Duncan J. Watts & Peter Sheridan Dodds, 2007. "Influentials, Networks, and Public Opinion Formation," Journal of Consumer Research, University of Chicago Press, vol. 34(4), pages 441-458, 05.
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    Cited by:
    1. H Peyton Young & Gabriel E. Kreindler, 2012. "Rapid Innovation Diffusion in Social Networks," Economics Series Working Papers 626, University of Oxford, Department of Economics.

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