论文标题
双重公平的动态定价
Doubly Fair Dynamic Pricing
论文作者
论文摘要
我们研究了两种公平限制的在线动态定价的问题：“程序公平”，要求拟议的价格在不同群体之间的预期相等，并且“实质性公平”要求公认的价格在不同群体之间的期望值平等。同时进行程序和实质性公平的政策称为“双重公平”。我们表明，双重公平的政策必须是随机的，才能比将相同价格分配给不同群体的最佳琐碎政策更高。在两组环境中，我们为达到$ \ tilde {o}（\ sqrt {t}）$遗憾的两组定价问题提出了一种在线学习算法，零过程不公平和$ \ tilde {o}（\ tilde {o}（\ sqrt {t} {t}）我们还证明了两个下界，表明这些结果是遗憾和不公平性的，这都是从理论上到迭代对数因素的最佳信息。据我们所知，这是第一个学会定价的动态定价算法，同时满足两个公平限制。
We study the problem of online dynamic pricing with two types of fairness constraints: a "procedural fairness" which requires the proposed prices to be equal in expectation among different groups, and a "substantive fairness" which requires the accepted prices to be equal in expectation among different groups. A policy that is simultaneously procedural and substantive fair is referred to as "doubly fair". We show that a doubly fair policy must be random to have higher revenue than the best trivial policy that assigns the same price to different groups. In a two-group setting, we propose an online learning algorithm for the 2-group pricing problems that achieves $\tilde{O}(\sqrt{T})$ regret, zero procedural unfairness and $\tilde{O}(\sqrt{T})$ substantive unfairness over $T$ rounds of learning. We also prove two lower bounds showing that these results on regret and unfairness are both information-theoretically optimal up to iterated logarithmic factors. To the best of our knowledge, this is the first dynamic pricing algorithm that learns to price while satisfying two fairness constraints at the same time.
