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Paying Attention to Payoffs in Analogy-Based Learning

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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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  • Miettinen, Topi, 2009. "Paying Attention to Payoffs in Analogy-Based Learning," SITE Working Paper Series 7, Stockholm School of Economics, Stockholm Institute of Transition Economics.
    • Handle: RePEc:hhs:hasite:0007
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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. Jehiel, Philippe & Mohlin, Erik, 2021. "Cycling and Categorical Learning in Decentralized Adverse Selection Economies," Working Papers 2021:11, Lund University, Department of Economics.
    4. 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.

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    More about this item

    Keywords

    analogy expectations; bounded rationality; curse; learning; discrimination; stereotypes;
    All these keywords.

    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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