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Stochastic adaptation in finite games played by heterogeneous populations

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  • Josephson, Jens

Abstract

We analyze stochastic adaptation in finite n-player games played by heterogeneous populations containing best repliers, better repliers, and imitators. Individuals select strategies by applying a personal learning rule to a sample from a finite history of past play. We give sufficient conditions for convergence to minimal closed sets under better replies and selection of a Pareto dominant such set. Finally, we demonstrate that the stochastically stable states are sensitive to the sample size by showing convergence to the risk-dominant equilibrium for sufficiently small sample size and to the Pareto-dominant equilibrium for sufficiently large sample size in 2x2 coordination games.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Economic Dynamics and Control.

Volume (Year): 33 (2009)
Issue (Month): 8 (August)
Pages: 1543-1554

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Handle: RePEc:eee:dyncon:v:33:y:2009:i:8:p:1543-1554

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Keywords: Heterogeneous agents Markov chain Stochastic stability Pareto dominance Risk dominance;

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Cited by:
  1. Dziubiński, Marcin & Roy, Jaideep, 2012. "Popularity of reinforcement-based and belief-based learning models: An evolutionary approach," Journal of Economic Dynamics and Control, Elsevier, Elsevier, vol. 36(3), pages 433-454.
  2. Peter Duersch & Joerg Oechssler & Burkhard Schipper, 2011. "Once Beaten, Never Again: Imitation in Two-Player Potential Games," Working Papers 1112, University of California, Davis, Department of Economics.

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