Experience-Weighted Attraction Learning in Coordination Games: Probability Rules, Heterogeneity, and Time-Variation

In earlier research we proposed an "experience-weighted attraction (EWA) learning" model for predicting dynamic behavior in economic experiments on multiperson noncooperative normal-form games. We showed that EWA learning model fits significantly better than existing learning models (choice reinforcement and belief-based models) in several different classes of games. The econometric estimation in that research adopted a representative agent approach and assumed that learning parameters are stationary across periods of an experiment. In addition, we used the logit (exponential) probability response function to transform attraction of strategies into choice probability. This paper allows for nonstationary learning parameters, permits two "segments" of players with different parameter values in order to allow for some heterogeneity, and compares the power and logit probability response functions. These specifications are estimated using experimental data from weak-link and median-action coordination games. Results show that players are heterogeneous and that they adjust their learning parameters over time very slightly. Logit probability response functions never fit worse than power functions, and generally fit better.