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.JEL Numbers: C73,C91, D83, L13.

This paper analyses dynamic pricing in markets with network externalities. Network externalities imply demand inertia, because the size of a network increases the usefulness of the product for consumers. Because past sales increase current demand, firms have an incentive to set low introductory prices to be able to increase prices as their networks grow. However, in reality we observe decreasing prices. This could be due to other factors dominating the network effects. We use an experimental duopoly market with demand inertia to isolate the effect of network externalities. We find that experimental prices are consistent with real world observations rather than with theoretical predictions.