Personalized prices can boost revenue, but they increasingly draw fire for hidden discrimination. A new study, “Dynamic Pricing with Fairness Constraints,” by Maxime C. Cohen, Sentao Miao, and Yining Wang, shows that firms can learn demand while staying fair at the same time. The authors embed two complementary notions of fairness into the classic learning-and-earning problem. The first, price fairness, limits price gaps across customer groups and over time, whereas the second, demand fairness, keeps realized demand shares balanced. To enforce price fairness, the authors design FaPU, an infrequently updated upper confidence bound algorithm that respects both group and temporal limits while securing near-optimal regret and matching lower bounds. For demand fairness, they propose FaPD, a primal-dual learner that meets aggregate demand quotas with high probability and the same near-optimal regret rate. Beyond providing tight theoretical analyses, the paper quantifies the “price of fairness” and outlines extensions to non-stationary markets, offering regulators and practitioners evidence that equity and profitability can coexist in algorithmic pricing.

This paper examines how data-driven personalized decisions can be made while preserving consumer privacy. Our setting is one in which the firm chooses a personalized price based on each new customer’s vector of individual features; the true set of individual demand-generating parameters is unknown to the firm and so must be estimated from historical data. We extend the existing personalized pricing framework by requiring also that the firm’s pricing policy preserve consumer privacy, or (formally) that it be differentially private: an industry standard for privacy preservation. We develop privacy-preserving personalized pricing algorithms and show that they achieve near-optimal revenue by deriving theoretical (upper and lower) performance bounds. Our analyses further suggest that, if the firm possesses a sufficient amount of historical data, then it can achieve a certain level of differential privacy almost “for free.” That is, the revenue loss due to privacy preservation is of smaller order than that due to estimation. We confirm our theoretical findings in a series of numerical experiments based on synthetically generated and online auto lending (CPRM-12-001) data sets. Finally, motivated by practical considerations, we also extend our algorithms and findings to a variety of alternative settings, including multiproduct pricing with substitution effect, discrete feasible price set, categorical sensitive features, and personalized assortment optimization. This paper was accepted by Vishal Gaur, operations management. Funding: R. Momot acknowledges financial support from the HEC Paris Foundation and the Agence Nationale de la Recherche (French National Research Agency) “Investissements d’Avenir” [Grant LabEx Ecodec/ANR-11-LABX-0047] during the initial stages of this project. Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2023.4925 .

Efficient Learning Algorithms for Dynamic Inventory Allocation in Multiwarehouse Multistore Systems with Censored Demand Motivated by collaboration with a prominent fast-fashion retailer in Europe, the researchers focus their attention on the one-warehouse multistore (OWMS) inventory control problem, specifically addressing scenarios in which the demand distribution is unknown a priori. The OWMS problem revolves around a central warehouse that receives initial replenishments and subsequently distributes inventory to multiple stores within a finite time horizon. The objective lies in minimizing the total expected cost. To overcome the hurdles posed by the unknown demand distribution, the researchers propose a primal-dual algorithm that continuously learns from demand observations and dynamically adjusts inventory control decisions in real time. Thorough theoretical analysis and empirical evaluations highlight the promising performance of this approach, offering valuable insights for efficient inventory allocation within the ever-evolving retail industry.