Model-Free Assortment Pricing with Transaction Data

信息管理与工程学院102室
上海财经大学（第三教学楼西侧）

上海市杨浦区武东路100号

Dr. Ningyuan Chen is currently an associate professor at the Department of Management at the University of Toronto, Mississauga and at the Rotman   School of Management, University of Toronto. Before joining the University of Toronto, he was an assistant professor at the Hong Kong University of Science and Technology. Prior to that, he was a postdoctoral fellow at the Yale   School of Management. He received his Ph.D. from the Industrial Engineering and Operations Research (IEOR) department at Columbia University in 2015. He is interested in various approaches to making data-driven decisions in business applications such as revenue management. His studies have been published in Management Science, Operations Research, Annals of Statistics,   NeurIPS and other journals and proceedings. His research is supported by the   UGC of Hong Kong and the Discovery Grants Program of Canada. He is the recipient of the Roger Martin Award for Excellence in Research and the IMI   Research Award.

We study the problem when a firm sets prices for products based on the transaction data, that is, which product past customers chose from an assortment and what were the historical prices that they observed. Our approach does not impose a model on the distribution of the customers’ valuations and only assumes, instead, that purchase choices satisfy incentive-compatible constraints. The uncertainty set of potential valuations of each past customer can then be encoded as a polyhedral set, and our approach maximizes the worst case revenue, assuming that new customers’ valuations are drawn from the empirical distribution implied by the collection of such polyhedra. We study the single-product case analytically and relate it to the traditional model-based approach. Then, we show that the optimal prices in the general case can be approximated at any arbitrary precision by solving a compact mixed-integer linear program. We further design three approximation strategies that are of low computational complexity and interpretable. In particular, the cutoff pricing heuristic has a competent provable performance guarantee. Comprehensive numerical studies based on synthetic and real data suggest that our pricing approach is uniquely beneficial when the historical data has a limited size or is susceptible to model misspecification.