Dynamic Pricing with Forward-looking Social Learners: The Case of the US Video Games Industry

Shen Hui is PhD candidate in Economics from Department of Economics, University of Maryland at College Park. His research interests includes industrial organization, marketing, applied econometrics.

This paper develops a model for quantifying the role of social learning in consumers’ dynamic demand and finding optimal intertemporal prices for profit maximizing firms in a market populated by forward-looking social learners. Optimal prices are a result of a Markov perfect equilibrium played between the firm and the consumers. Nested in the market equilibrium is a demand equilibrium played among consumers who make the “right” purchase/wait decisions given endogenously produced product information. The empirical exercises are conducted in two steps. The first step estimates demand parameters, including those associated with social learning. Endogeneity of prices is remedied with a pseudo pricing policy function of relevant state variables. In the second step, optimal prices are found by the Mathematical Programming with Equilibrium Constraints (MPEC) approach. The model is applied to the US video games industry with sales data of PlayStation 3 games. The results reveal that (1) compared to static social learning, forward-looking social learning reduces equilibrium profits of games in the sample by $5.2M (28.4%) on average; (2) an incorrect belief of consumers’ forward-looking behavior reduces a firm’s profits by a maximum of 29.92%. These results indicate great value for researches on consumers’ forward-looking social learning behavior.