Dr. Ningyuan Chen is currently an assistant professor at the Department of Management at the University of Toronto Mississauga and cross-appointed 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. 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 applications including revenue management. His research is supported by the UGC of Hong Kong and the Discovery Grants Program of Canada.
| Name of Speaker | Dr Chen Ningyuan |
| Schedule | Friday 17 September, 10am |
| Link to Register | https://nus-sg.zoom.us/meeting/register/tZ0td-ysrz8uGtLavCw5BEWFm37wQmWdiVsx |
| Title | Model-Free Assortment Pricing with Transaction Data |
| Abstract | We study the problem when a firm sets prices for products based on the transaction data, i.e., 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 individual valuation 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 show that the optimal prices in this setting can be approximated at any arbitrary precision by solving a compact mixed-integer linear program. Moreover, we study the single-product case and relate it to the traditional model-based approach. We also design three approximation strategies that are of low computational complexity and interpretable. 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. |
