Confusion and reinforcement learning in experimental public goods games

Abstract

We use a limited information environment to mimic the state of confusion in an experimental public goods game. We show that learning leads to dynamics similar to those observed in standard public goods games. The decay can't be fully explained by learning though. Learning only accounts for 41 percent of the decay in contributions in standard public goods games. The contribution dynamics of subjects, who are identified as conditional cooperators, differ from the learning dynamics, while a learning model estimated from the limited information treatment tracks behavior for subjects, who cannot be classified as conditional cooperators, reasonably well.