Individual Differences in Ewa Learning with Partial Payoff Information
نویسندگان
چکیده
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). JFL Classifications: C70, C91
منابع مشابه
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 es...
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