Learning about Learning in Games through Experimental Control of Strategic Interdependence

We conduct experiments in which humans repeatedly play one of two games against a computer decision maker that follows either a reinforcement learning or an Experience Weighted Attraction algorithm. Our experiments show these learning algorithms more sensitively detect exploitable opportunities than humans. Also, learning algorithms respond to detected payoff increasing opportunities systematically; however, the responses are too weak to improve the algorithms payoffs. Human play against various decision maker types doesn't significantly vary. These factors lead to a strong linear relationship between the humans and algorithms action choice proportions that is suggestive of the algorithm's best response correspondence.