Learning with bounded memory in games
Daniel Monte ()
Games and Economic Behavior, 2014, vol. 87, issue C, 204-223
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)
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3) Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:eee:gamebe:v:87:y:2014:i:c:p:204-223
Access Statistics for this article
Games and Economic Behavior is currently edited by E. Kalai
More articles in Games and Economic Behavior from Elsevier
Bibliographic data for series maintained by Haili He ().