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Stochastic Evolution of Rules for Playing Finite Normal Form Games


  • Fabrizio Germano



The evolution of boundedly rational rules for playing normal form games is studied within stationary environments of stochastically changing games. Rules are viewed as algorithms prescribing strategies for the different normal form games that arise. It is shown that many of the “folk resultsâ€\x9D of evolutionary game theory, typically obtained with a fixed game and fixed strategies, carry over to the present environments. The results are also related to some recent experiments on rules and games. Copyright Springer Science+Business Media, LLC 2007

Suggested Citation

  • Fabrizio Germano, 2007. "Stochastic Evolution of Rules for Playing Finite Normal Form Games," Theory and Decision, Springer, vol. 62(4), pages 311-333, May.
  • Handle: RePEc:kap:theord:v:62:y:2007:i:4:p:311-333 DOI: 10.1007/s11238-007-9032-8

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    References listed on IDEAS

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    Cited by:

    1. Mengel, Friederike, 2012. "Learning across games," Games and Economic Behavior, Elsevier, vol. 74(2), pages 601-619.
    2. Spiliopoulos, Leonidas, 2012. "Interactive learning in 2×2 normal form games by neural network agents," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(22), pages 5557-5562.
    3. Burkhard Schipper, 2015. "Strategic teaching and learning in games," Working Papers 151, University of California, Davis, Department of Economics.
    4. Rabah Amir & Igor Evstigneev & Klaus Schenk-Hoppé, 2013. "Asset market games of survival: a synthesis of evolutionary and dynamic games," Annals of Finance, Springer, vol. 9(2), pages 121-144, May.
    5. Spiliopoulos, Leonidas, 2009. "Neural networks as a learning paradigm for general normal form games," MPRA Paper 16765, University Library of Munich, Germany.
    6. Schipper, Burkhard C., 2008. "On An Evolutionary Foundation Of Neuroeconomics," Economics and Philosophy, Cambridge University Press, vol. 24(03), pages 495-513, November.

    More about this item


    bounded rationality; evolutionary dynamics; learning; normal form games; rules; stochastic dynamics; C72; C73; D81; D83;

    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
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness


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