Fairness-Aware Online Price Discrimination with Nonparametric Demand Models

Incorporating Fairness into Online Price Discrimination In the paper “Fairness-aware online price discrimination with nonparametric demand models,” Xi Chen, Jiameng Lyu, Xuan Zhang, and Yuan Zhou explore how fairness can be integrated into dynamic pricing strategies. The authors propose a model that enforces price fairness constraints, ensuring that price differences between customer groups remain within a specified range. Their approach introduces a novel regret lower bound, which contrasts with the typical regret seen in traditional pricing algorithms. This shift underscores the added complexity of optimizing revenue while maintaining fairness. The study not only advances the understanding of fairness-aware dynamic pricing but also enriches the dynamic pricing literature by offering new lower-bound techniques. These insights may be useful for deriving lower bounds in other problems related to learning optimal prices under constraints. Their work contributes significantly to the growing need for ethical pricing practices in data-driven markets.