Learning across games

This paper studies the learning process carried out by two agents who are involved in many games. As distinguishing all games can be too costly (require too much reasoning resources) agents might partition the set of all games into analogy classes. Partitions of higher cardinality are more costly. A process of simultaneous learning of actions and partitions is presented and equilibrium partitions and action choices characterized. The model is able to explain deviations from subgame perfection that are sometimes observed in experiments for arbitrarily small reasoning costs. Furthermore it is shown that learning across games can destabilize strict Nash equilibria and stabilize equilibria in weakly dominated strategies as well as mixed equilibria in 2 × 2 Coordination games under certain conditions. JEL Classification: C70, C72, C73.