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...

We modify the self-tuning Experience Weighted Attraction (EWA-lite) model of Camerer, Ho, and Chong (2007) and use it as a computer testbed to study the likely performance of a set of twostate automata in four symmetric 2 x 2 games. The model suggested allows for a richer specification of...