Action Learning versus Strategy Learning ∗

This paper seeks to ascertain whether the strategy-learning model of Hanaki, Sethi, Erev, and Peterhansl (2003) better accounts for observed behavior than do the various action-learning models. It does so by measuring the goodness-of-fit of the models’ predictions against published experimental results for such games as Coordination, Prisoner’s Dilemma, and Chicken. The fit is measured via the mean squared deviation (MSD) between the observed behavior and the one predicted by the model. The results show that, for Chicken, the strategy-learning model fits the observed data much better than do the action-learning models. The best action-learning model, on the other hand, fits the observed data well in Coordination. Overall, the strength of the strategy-learning model is best shown in games where alternations between the two stage-game Nash equilibria are often observed in the laboratory experiments