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Learning with bounded memory in games

Daniel Monte ()

Games and Economic Behavior, 2014, vol. 87, issue C, 204-223

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.

Keywords: Bounded memory; Incomplete information games; Repeated games (search for similar items in EconPapers)
JEL-codes: C72 D82 D83 (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1016/j.geb.2014.03.005

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