Social and Information Sciences Laboratory (SISL) Seminar

Abstract: We introduce a robust approach to the study of optimal dynamic pricing in the face of information arrival. As opposed to classical intertemporal pricing models, we consider consumers who learn about their willingness-to-pay for a product over time (which is typical for initially unfamiliar products). A seller commits to a pricing strategy, while buyers arrive exogenously and decide when to make a one-time purchase. The seller does not know how each buyer learns about her value for the product, and seeks to maximize profit against the worst-case information arrival processes. We show that a constant price path delivers the robustly optimal profit, with profit and price both lower than under known values. Thus, under the robust objective, intertemporal incentives do not arise at the optimum, despite the possibility for information arrival to influence the timing of purchases. We delineate whether constant prices remain optimal (or not) when the seller seeks robustness against a subset of information arrival processes. As part of the analysis, we develop new techniques to study dynamic Bayesian persuasion.