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Payoff Information and Learning in Signaling Games

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  • Drew Fudenberg
  • Kevin He

Abstract

We add the assumption that players know their opponents' payoff functions and rationality to a model of non-equilibrium learning in signaling games. Agents are born into player roles and play against random opponents every period. Inexperienced agents are uncertain about the prevailing distribution of opponents' play, but believe that opponents never choose conditionally dominated strategies. Agents engage in active learning and update beliefs based on personal observations. Payoff information can refine or expand learning predictions, since patient young senders' experimentation incentives depend on which receiver responses they deem plausible. We show that with payoff knowledge, the limiting set of long-run learning outcomes is bounded above by rationality-compatible equilibria (RCE), and bounded below by uniform RCE. RCE refine the Intuitive Criterion (Cho and Kreps, 1987) and include all divine equilibria (Banks and Sobel, 1987). Uniform RCE sometimes but not always exists, and implies universally divine equilibrium.

Suggested Citation

  • Drew Fudenberg & Kevin He, 2017. "Payoff Information and Learning in Signaling Games," Papers 1709.01024, arXiv.org, revised Jan 2020.
  • Handle: RePEc:arx:papers:1709.01024
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    References listed on IDEAS

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    Cited by:

    1. Fudenberg, Drew & He, Kevin, 2021. "Player-compatible learning and player-compatible equilibrium," Journal of Economic Theory, Elsevier, vol. 194(C).
    2. Clark, Daniel & Fudenberg, Drew & He, Kevin, 2022. "Observability, dominance, and induction in learning models," Journal of Economic Theory, Elsevier, vol. 206(C).
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    4. Daniel Clark & Drew Fudenberg, 2021. "Justified Communication Equilibrium," American Economic Review, American Economic Association, vol. 111(9), pages 3004-3034, September.
    5. Drew Fudenberg & Giacomo Lanzani & Philipp Strack, 2021. "Limit Points of Endogenous Misspecified Learning," Econometrica, Econometric Society, vol. 89(3), pages 1065-1098, May.

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    More about this item

    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
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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