We extend experience-weighted attraction (EWA) learning to games in which only the set of possible
foregone payoffs from unchosen strategies are known, and estimate parameters separately for each
player to study heterogeneity. We assume players estimate unknown foregone payoffs from a strategy,
by substituting the last payoff actually received from that strategy, by clairvoyantly guessing the actual
foregone payoff, or by averaging the set of possible foregone payoffs conditional on the actual
outcomes. All three assumptions improve predictive accuracy of EWA. Individual parameter estimates
suggest that players cluster into two separate subgroups (which differ from traditional reinforcement
and belief learning)
