Dynamic Pricing with Fairness Constraints
Maxime C. Cohen, Sentao Miao, Yining Wang
Operations Research · 2025 · Volume 73 · Issue 6
Abstract
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. Funding: We thank M. C. Cohen’s Scale AI Chair for the generous research funding. S. Miao thanks the Natural Sciences and Engineering Research Council of Canada (NSERC) for funding received when he was at McGill University [NSERC RGPIN-2022-03247]. Supplemental Material: All supplemental materials, including the code, data, and files required to reproduce the results, are available at https://doi.org/10.1287/opre.2023.0123 .
Keywords
Markets, Platforms, and Revenue Management, dynamic pricing, demand learning, fairness, revenue management
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