Self-tuning experience weighted attraction (EWA) is a one-parameter theory of learning in
games. It addresses a criticism that an earlier model (EWA) has too many parameters, by
fixing some parameters at plausible values and replacing others with functions of experience
so that they no longer need to be estimated. Consequently, it is econometrically simpler
than the popular weighted fictitious play and reinforcement learning models.
The functions of experience which replace free parameters “self-tune” over time, adjusting
in a way that selects a sensible learning rule to capture subjects’ choice dynamics. For
instance, the self-tuning EWA model can turn from a weighted fictitious play into an averaging
reinforcement learning as subjects equilibrate and learn to ignore inferior foregone
payoffs. The theory was tested on seven different games, and compared to the earlier parametric
EWA model and a one-parameter stochastic equilibrium theory (QRE). Self-tuning
EWA does as well as EWA in predicting behavior in new games, even though it has fewer
parameters, and fits reliably better than the QRE equilibrium benchmark