A generalized approach to belief learning in repeated games

Christos Ioannou and Julian Romero

Games and Economic Behavior, 2014, vol. 87, issue C, 178-203

Abstract: We propose a methodology that is generalizable to a broad class of repeated games in order to facilitate operability of belief-learning models with repeated-game strategies. The methodology consists of (1) a generalized repeated-game strategy space, (2) a mapping between histories and repeated-game beliefs, and (3) asynchronous updating of repeated-game strategies. We implement the proposed methodology by building on three proven action-learning models. Their predictions with repeated-game strategies are then validated with data from experiments with human subjects in four, symmetric 2×2 games: Prisoner's Dilemma, Battle of the Sexes, Stag-Hunt, and Chicken. The models with repeated-game strategies approximate subjects' behavior substantially better than their respective models with action learning. Additionally, inferred rules of behavior in the experimental data overlap with the predicted rules of behavior.

Keywords: Adaptive models; Belief learning; Repeated-game strategies; Finite automata; Prisoner's Dilemma; Battle of the Sexes; Stag-Hunt; Chicken (search for similar items in EconPapers)
JEL-codes: C51 C72 C92 D03 (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (22)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:gamebe:v:87:y:2014:i:c:p:178-203

DOI: 10.1016/j.geb.2014.05.007

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