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Learning with bounded memory in games

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  • Monte, Daniel

Abstract

We study learning with bounded memory in zero-sum repeated games with one-sided incomplete information. The uninformed player has only a fixed number of memory states available. His strategy is to choose a transition rule from state to state, and an action rule, which is a map from each memory state to the set of actions. We show that the equilibrium transition rule involves randomization only in the intermediate memory states. Such randomization, or less frequent updating, is interpreted as a way of testing the opponent, which generates inertia in the player's behavior and is the main short-run bias in information processing exhibited by the bounded memory player.

Suggested Citation

  • Monte, Daniel, 2014. "Learning with bounded memory in games," Games and Economic Behavior, Elsevier, vol. 87(C), pages 204-223.
  • Handle: RePEc:eee:gamebe:v:87:y:2014:i:c:p:204-223
    DOI: 10.1016/j.geb.2014.03.005
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    Cited by:

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    2. Brocas, Isabelle & Carrillo, Juan D., 2016. "A neuroeconomic theory of memory retrieval," Journal of Economic Behavior & Organization, Elsevier, vol. 130(C), pages 198-205.

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    More about this item

    Keywords

    Bounded memory; Incomplete information games; Repeated games;
    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
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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