Dynamic Pricing with Fairness Constraints
成果类型：
Article; Early Access
署名作者：
Cohen, Maxime C.; Miao, Sentao; Wang, Yining
署名单位：
McGill University; University of Colorado System; University of Colorado Boulder; University of Texas System; University of Texas Dallas
刊物名称：
OPERATIONS RESEARCH
ISSN/ISSBN：
0030-364X
DOI：
10.1287/opre.2023.0123
发表日期：
2025
关键词：
摘要：
Following the increasing popularity of personalized pricing, there is a growing concern from customers and policymakers regarding fairness considerations. This paper studies the problem of dynamic pricing with unknown demand under two types of fairness constraints: price fairness and demand fairness. For price fairness, the retailer is required to (i) set similar prices for different customer groups (called group fairness) and (ii) ensure that the prices over time for each customer group are relatively stable (called time fairness). We propose an algorithm based on an infrequently changed upper confidence bound (UCB) method, which is proven to yield a near-optimal regret performance. We then leverage this method to address the extension of nonstationary demand, which is particularly relevant for time fairness, to prevent price gouging practices. For demand fairness, the retailer is required to satisfy the condition that the resulting demand from different customer groups is relatively similar (e.g., the retailer offers a lower price to students to increase their demand to a level similar to that of nonstudents). In this case, we design an algorithm adapted from a primal-dual learning framework and prove that our algorithm also achieves a near-optimal regret performance.