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On the Role of the Group Composition for Achieving Optimality

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  • Antonio Morales

Abstract

We show the inability of any pure strategy imitation rule for leading a decision maker towards optimality for given and fixed population behaviour. The intuition is that a pure strategy state space is too small to deal with a large variety of environments. This result helps to understand the optimality result obtained by Schlag (1998), where the population behaviour is let to evolve over time. The intuition is that the group composition provides an additional state space in which information about the environment can be accumulated. Copyright Springer Science + Business Media, Inc. 2005

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  • Antonio Morales, 2005. "On the Role of the Group Composition for Achieving Optimality," Annals of Operations Research, Springer, vol. 137(1), pages 387-397, July.
    • Handle: RePEc:spr:annopr:v:137:y:2005:i:1:p:387-397:10.1007/s10479-005-2268-1
    DOI: 10.1007/s10479-005-2268-1
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    References listed on IDEAS

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    1. Ed Hopkins, 2002. "Two Competing Models of How People Learn in Games," Econometrica, Econometric Society, vol. 70(6), pages 2141-2166, November.
    2. Schlag, Karl H., 1998. "Why Imitate, and If So, How?, : A Boundedly Rational Approach to Multi-armed Bandits," Journal of Economic Theory, Elsevier, vol. 78(1), pages 130-156, January.
    3. Borgers, Tilman & Sarin, Rajiv, 1997. "Learning Through Reinforcement and Replicator Dynamics," Journal of Economic Theory, Elsevier, vol. 77(1), pages 1-14, November.
    4. Schlag, Karl H., 1998. "Why Imitate, and If So, How?, : A Boundedly Rational Approach to Multi-armed Bandits," Journal of Economic Theory, Elsevier, vol. 78(1), pages 130-156, January.
    5. Binmore, Ken, 1987. "Modeling Rational Players: Part I," Economics and Philosophy, Cambridge University Press, vol. 3(2), pages 179-214, October.
    6. John G. Cross, 1973. "A Stochastic Learning Model of Economic Behavior," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 87(2), pages 239-266.
    7. Hopkins, Ed & Posch, Martin, 2005. "Attainability of boundary points under reinforcement learning," Games and Economic Behavior, Elsevier, vol. 53(1), pages 110-125, October.
    8. Drew Fudenberg & David K. Levine, 1998. "The Theory of Learning in Games," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262061945, April.
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