A generalized approach to belief learning in repeated games

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 rules of behavior predicted. JEL Classification: C51, C92, C72, D03