Fairness-Aware Online Price Discrimination with Nonparametric Demand Models
成果类型：
Article; Early Access
署名作者：
Chen, Xi; Lyu, Jiameng; Zhang, Xuan; Zhou, Yuan
署名单位：
New York University; Fudan University; University of Illinois System; University of Illinois Urbana-Champaign; Tsinghua University
刊物名称：
OPERATIONS RESEARCH
ISSN/ISSBN：
0030-364X
DOI：
10.1287/opre.2022.0292
发表日期：
2025
关键词：
摘要：
Price discrimination, which refers to the strategy of setting different prices for different customer groups, has been widely used in online retailing. Although it helps boost the collected revenue for online retailers, it might create serious concerns about fairness, which even violates regulations and laws. This paper studies the problem of dynamic discriminatory pricing under a relative price fairness constraint in the pricing literature. We first establish a regret lower bound of ohm(T4=5) under the price fairness constraint. Then, we propose an optimal dynamic pricing policy with a regret upper bound of O(T4=5), which enforces the strict price fairness constraint. The separation between our Theta(T4=5)-type optimal regret and the usual T -type optimal regret in the dynamic pricing literature illustrates the intrinsic difficulty from the information-theoretical perspective raised by the fairness constraint. Our technical tools to establish the lower-bound result enrich the lower-bound techniques in dynamic pricing literature and may provide insights for deriving lower bounds for other problems related to learning-constrained optimal prices.