Explaining Price Dispersion and Dynamics in Laboratory Bertrand Markets

This paper develops a quantal-response adaptive learning model which combines sellers' bounded rationality with adaptive belief learning in order to explain price dispersion and dynamics in laboratory Bertrand markets with perfect information. In the model, sellers hold beliefs about their opponents’ strategies and play quantal best responses to these beliefs. After each period, sellers update their beliefs based on the information learned from previous play. Maximum likelihood estimation suggests that when sellers have full past price information, the learning model explains price dispersion within periods and the dynamics across periods. The fit is particularly good if one allows for sellers being risk averse. In contrast, Quantal Response Equilibrium does not organize the data well.