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金融工程研究中心学术报告:Model-Free Assortment Pricing with Transaction Data

主讲人:多伦多大学 陈宁远教授

时    间:2021年10月13日(星期三)上午9:30--10:30

地    点: https://meeting.tencent.com/dm/3ufYEhIGGfzt    密码:2021


 

报告摘要: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.

 

 

主讲人简介:

Dr. Ningyuan Chen is an assistant professor of the Department of Management at the University of Toronto, Mississauga. He is also cross-appointed at the Rotman School of Management, University of Toronto. Before joining UofT, he was an assistant professor of IEDA@HKUST. Dr. Chen received his Ph.D. from the Industrial Engineering and Operations Research (IEOR) department at Columbia University in Oct 2015. In research, he is interested in various approaches to making data-driven decisions in the context of revenue management, including online learning and machine learning. His research is also broadly related to statistics.

 

 


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