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Paying attention to payoffs in analogy-based learning

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  • Topi Miettinen

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Abstract

This paper introduces the payoff-confirming analogy-based expectation equilibrium (PCABEE) as a way to refine the set of analogy-based equilibria and the associated admissible analogy partitions. In addition to the actions of others, own payoff history provides information about others’ strategies but, yet, non-Bayesian Nash equilibria may exist both with an incorrect and a correct prior. We provide general conditions when this happens. Two stylized employer-employee interactions, one with a correct and one with an incorrect prior, are provided illustrating how PCABEE can be used to analyze robust stereotypes and how incorrect such stereotypes may lead to discrimination.
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Suggested Citation

  • Topi Miettinen, 2012. "Paying attention to payoffs in analogy-based learning," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 50(1), pages 193-222, May.
  • Handle: RePEc:spr:joecth:v:50:y:2012:i:1:p:193-222
    DOI: 10.1007/s00199-010-0565-7
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    References listed on IDEAS

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    Cited by:

    1. Miettinen, Topi, 2009. "The partially cursed and the analogy-based expectation equilibrium," Economics Letters, Elsevier, vol. 105(2), pages 162-164, November.
    2. Christoph March, 2011. "Adaptive social learning," PSE Working Papers halshs-00572528, HAL.
    3. Miettinen, Topi, 2008. "Analogy-based Expectations and the Partially Cursed Equilibrium," SSE/EFI Working Paper Series in Economics and Finance 708, Stockholm School of Economics, revised 12 Dec 2008.
    4. Christoph March, 2011. "Adaptive social learning," Working Papers halshs-00572528, HAL.

    More about this item

    Keywords

    Analogy expectations; Bounded rationality; Learning; Stereotypes; Winner’s curse; C72; D82;

    JEL classification:

    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design

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