Fairness-aware Online Price Discrimination with Nonparametric Demand Models

TL;DR

This paper develops a fairness-aware dynamic pricing strategy for online retail, balancing revenue maximization with fairness constraints across customer groups, and achieves regret bounds of approximately T^{4/5}.

Contribution

It introduces the first optimal online pricing algorithms under strict and soft fairness constraints, extending to multiple customer groups and demand fairness scenarios.

Findings

Achieves regret of O(T^{4/5}) under fairness constraints.

Extends algorithms to demand fairness and multiple groups.

Provides theoretical guarantees for fairness-aware pricing policies.

Abstract

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 the regulation and laws. This paper studies the problem of dynamic discriminatory pricing under fairness constraints. In particular, we consider a finite selling horizon of length $T$ for a single product with two groups of customers. Each group of customers has its unknown demand function that needs to be learned. For each selling period, the seller determines the price for each group and observes their purchase behavior. While existing literature mainly focuses on maximizing revenue, ensuring fairness among different customers has not been fully explored in the dynamic pricing literature. This work adopts the fairness notion from Cohen et al. (2022). For price fairness, we propose an optimal dynamic pricing policy regarding regret, which enforces the strict price fairness constraint. In contrast to the standard $\sqrt{T}$-type regret in online learning, we show that the optimal regret in our case is $\tilde{O}(T^{4/5})$. We further extend our algorithm to a more general notion of fairness, which includes demand fairness as a special case. To handle this general class, we propose a soft fairness constraint and develop a dynamic pricing policy that achieves $\tilde{O}(T^{4/5})$ regret. We also demonstrate that our algorithmic techniques can be adapted to more general scenarios such as fairness among multiple groups of customers.