Experience-weighted attraction learning in sender-receiver signaling games

We apply Camerer and Ho's experience-weighted attraction (EWA) model of lea rning to extensive-form signaling games. Since these games often have many equilibria, logical 'refinements' have been used to predict which equilibri um will occur. Brandts and Holt conjectured that belief formation could lea d to less refined equilibria, and confirmed their conjecture experimentally . Our adaptation of EWA to signaling games includes a formalization of the Brandts-Holt belief formation idea as a special case. We find that the Bran dts-Holt dynamic captures the direction of switching from one strategy to a nother, but does not capture the rate at which switching occurs. EWA does b etter at predicting the rate of switching land also forecasts better than r einforcement models). Extensions of EWA which update unchosen signals by di fferent functions of the set of unobserved foregone payoffs further improve predictive accuracy.