Measuring Policy Performance in Online Pricing with Offline Data:Worst-case Perspective and Bayesian Perspective

作     者：Yue Wang Zeyu Zheng Yue Wang;Zeyu Zheng

作者机构：Department of Computational MedicineUniversity of CaliforniaLos AngelesCA 90095USA Institut des Hautes Etudes ScientifiquesBures-sur-YvetteEssonne 91440France Department of Industrial Engineering and Operations ResearchUniversity of CaliforniaBerkeleyCA 94720USA 

出 版 物：《Journal of Systems Science and Systems Engineering》 (系统科学与系统工程学报（英文版）)

年 卷 期：2023年第32卷第3期

页      面：352-371页

核心收录：

学科分类：0711[理学-系统科学] 08[工学] 0811[工学-控制科学与工程] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 081202[工学-计算机软件与理论] 

基　　金：The authors would like to thank the anonymous referees for providing helpful comments that improve the quality of this paper. 

主　　题：Online pricing offline data performance measure worst-case approach Bayesian approach 

摘      要：The problems of online pricing with offline data,among other similar online decision making with offline data problems,aim at designing and evaluating online pricing policies in presence of a certain amount of existing offline data.To evaluate pricing policies when offline data are available,the decision maker can either position herself at the time point when the offline data are already observed and viewed as deterministic,or at the time point when the offline data are not yet generated and viewed as stochastic.We write a framework to discuss how and why these two different positions are relevant to online policy evaluations,from a worst-case perspective and from a Bayesian perspective.We then use a simple online pricing setting with offline data to illustrate the constructions of optimal policies for these two approaches and discuss their differences,especially whether we can decompose the searching for the optimal policy into independent subproblems and optimize separately,and whether there exists a deterministic optimal policy.