Individual Di®erences in EWA Learning with Partial Payo® Information

We extend EWA learning to games in which only the set of possible foregone payo®s from unchosen strategies are known. We assume players estimate unknown foregone payo®s from a strategy, by substituting the last payo® actually received from that strategy, or by clairvoyantly guessing the actual foregone payo®. Either assumption improves predictive accuracy of EWA. Learning parameters are also estimated separately for each player. Players cluster into two separate subgroups, which do not correspond to traditional special cases of reinforcement or belief learning.