Functional EWA: A One-parameter Theory of Learning in Games

Abstract

Functional experience weighted attraction (fEWA) is a one-parameter theory of learning in games. It approximates the free parameters in an earlier model (EWA) with functions of experience. The theory was originally tested on seven different games and compared to four other learning and equilibrium theories, then three more games were added. Generally fEWA or parameterized EWA predict best out-of-sample, but one kind of reinforcement learning predicts well in games with mixed-strategy equilibrium. Of the learning models, belief learning models fit worst but fit better than noisy (quantal response) equilibrium models. The economic value of a theory is measured by how much more subjects would have earned if they followed the theory's recommendations. Most learning theories add value (though equilibrium theories often subtract value) and fEWA and EWA usually add the most value.

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