Abstract

We describe a general model, `experience-weighted attraction' (EWA) learning, which includes reinforcement learning and a class of weighted fictitious play belief models as special cases. In EWA, strategies have attractions which reflect prior predispositions, are updated based on payoff experience, and determine choice probabilities according to some rule (e.g., logit). A key feature is a parameter delta which weights the strength of hypothetical reinforcement of strategies which were not chosen according to the payoff they would have yielded. When delta = 0 choice reinforcement results. When delta = 1, levels of reinforcement of strategies are proportional to expected payoffs given beliefs based on past history. Another key feature is the growth rates of attractions. The EWA model controls the growth rates by two decay parameters, phi and rho, which depreciate attractions and amount of experience separately. When phi = rho, belief-based models result; when rho = 0 choice reinforcement results. Using three data se...

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