针对买家策略性伪装身份的问题，提出公平动态定价算法，兼顾价格公平与防作弊。
Fairness-aware Contextual Dynamic Pricing with Strategic Buyers
- 设计动态定价策略，同时约束价格公平性并抑制买家伪造身份行为。
- 理论证明在 $T$ 期下最大损失为 $O(\sqrt{T} + H(T))$，可降至 $O(\sqrt{T})$。
- 实证显示相比基准策略减少 35.06% 损失，且发现贷款中存在种族歧视现象。
上下文动态定价在在线零售中广泛应用，卖家根据商品属性和买家特征调整价格。然而，当特定群体（如性别或种族）间出现显著价格差异时，可能引发公平性质疑，甚至触犯法律。此外，价格差异也可能激励弱势群体策略性地伪装身份以获取更低价格。本文研究在买家群体身份私密不可观测下的公平性约束动态定价问题，提出一种能同时实现价格公平并抑制策略行为的定价策略。该策略在 $T$ 个时间周期内的上界后悔值为 $O(\sqrt{T}+H(T))$，其中 $H(T)$ 来自买家对定价政策公平性的学习感知。当买家能学习公平性时，上界可降至 $O(\sqrt{T})$。我们还证明了任意定价策略的下界为 $Ω(\sqrt{T})$。实验验证了策略有效性，在真实数据中发现贷款申请仍存在种族歧视，所提方法相较基线策略降低 35.06% 的后悔值。
原文摘要 · Abstract (English)
Contextual pricing strategies are prevalent in online retailing, where the seller adjusts prices based on products' attributes and buyers' characteristics. Although such strategies can enhance seller's profits, they raise concerns about fairness when significant price disparities emerge among specific groups, such as gender or race. These disparities can lead to adverse perceptions of fairness among buyers and may even violate the law and regulation. In contrast, price differences can incentivize disadvantaged buyers to strategically manipulate their group identity to obtain a lower price. In this paper, we investigate contextual dynamic pricing with fairness constraints, taking into account buyers' strategic behaviors when their group status is private and unobservable from the seller. We propose a dynamic pricing policy that simultaneously achieves price fairness and discourages strategic behaviors. Our policy achieves an upper bound of $O(\sqrt{T}+H(T))$ regret over $T$ time horizons, where the term $H(T)$ arises from buyers' assessment of the fairness of the pricing policy based on their learned price difference. When buyers are able to learn the fairness of the price policy, this upper bound reduces to $O(\sqrt{T})$. We also prove an $Ω(\sqrt{T})$ regret lower bound of any pricing policy under our problem setting. We support our findings with extensive experimental evidence, showcasing our policy's effectiveness. In our real data analysis, we observe the existence of price discrimination against race in the loan application even after accounting for other contextual information. Our proposed pricing policy demonstrates a significant improvement, achieving 35.06% reduction in regret compared to the benchmark policy.
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