Learning under limited information

Abstract

We study how human subjects learn under extremely limited information. We use Chen's (forthcoming) data on cost sharing games, and Van Huyck et al.'s (1996, Manuscript) data on coordination games to compare three payoff-based learning models. Under the serial mechanism and coordination games, the payoff-assessment learning model (Sarin and Vahid, 1999, Games Econ. Behav. 28, 294–309) tracks the data the best, followed by the experience-weighted attraction learning model (Camerer and Ho, 1999, Econometrica, 67, 827–874), which in turn, is followed by a simple reinforcement learning model. Under the average cost pricing mechanism, however, none of the learning models tracks the data well.