Technical Note—Fairness-Aware Online Price Discrimination with Nonparametric Demand Models
研究如何在动态定价中融入公平性约束，确保不同客户群体间的价格差异在指定范围内，并分析了公平性带来的额外复杂性，为约束下的最优定价学习提供了新的下界技术。
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.