Dynamic Pricing and Demand Learning in Nonstationary Environments

For a seller operating in a nonstationary demand setting, a key question is how to collect and filter data to find the optimal prices for its products. In this chapter, we discuss the commonly used frameworks for dynamic pricing and demand learning in nonstationary demand settings. For exogenously changing demand settings, we provide an overview of the recent dynamic pricing studies that expand the antecedent literature on statistical filtering theory. In the case of endogenously changing demand settings, we review different approaches on how to manage intertemporal dependencies between price and demand. We also provide a few possible directions for future research.