宽限期就是一切：收益管理中无收入损失的个体公平性
Grace Period is All You Need: Individual Fairness without Revenue Loss in Revenue Management
AI总结：
本文提出收益管理中的个体公平性概念及可嵌入传统算法的Grace Period模块，通过将随机性从个体决策转移到客户总数层面，在不增加遗憾、不损失收入的情况下保证个体公平。
摘要：
想象一下，你和朋友在同一家商店购买相同的商品，但只有你的朋友获得了折扣。你朋友的折扣是否让你觉得受到了商店的不公平对待？你是否会因此不太愿意将来再从该商店购买？基于我们在Prolific平台上开展的一项大规模在线调查，上述问题的答案是肯定的。因此，在向不同客户分配资源时，卖家应同时考虑总奖励和个体公平性。受这些发现的启发，本文提出了在线收益管理中个体公平性的概念，以及一个算法模块（称为“Grace Period（宽限期）”），该模块可以嵌入传统收益管理算法中并保证个体公平性。具体来说，我们展示了如何将Grace Period嵌入五种常见的收益管理算法中，包括带概率分配的确定性线性规划（Deterministic Linear Programming with Probabilistic Assignment）、带概率分配的重解确定性线性规划（Resolving Deterministic Linear Programming with Probabilistic Assignment）、静态竞价价格控制（Static Bid Price Control）、预订限额（Booking Limit）和嵌套（Nesting），从而覆盖随机和对抗性客户到达两种场景。嵌入Grace Period不会给这些算法带来额外的遗憾（regret）。这一发现表明，在渐近机制下，卖家最大化其收益与保证每位客户感到被公平对待之间不存在权衡。Grace Period背后的核心直觉是，对每位客户进行独立的随机决策往往会导致不公平的结果。然而，我们无法消除随机性，因为它在最大化利润中起着至关重要的作用。Grace Period通过将随机性从个体决策中转移出来，转而将其应用于获得特定决策的客户总数来解决这一问题。这种方法在保留收益潜力的同时缓解了公平性问题。
英文摘要：
Imagine you and a friend purchase identical items at a store, yet only your friend received a discount. Would your friend's discount make you feel unfairly treated by the store? And would you be less willing to purchase from that store again in the future? Based on a large-scale online survey that we ran on Prolific, it turns out that the answers to the above questions are positive. Therefore, when allocating resources to different customers, sellers should consider both the total reward and individual fairness. Motivated by these findings, in this work we propose a notion of individual fairness in online revenue management and an algorithmic module (called ``Grace Period'') that can be embedded in traditional revenue management algorithms and guarantee individual fairness. Specifically, we show how to embed the Grace Period in five common revenue management algorithms including Deterministic Linear Programming with Probabilistic Assignment, Resolving Deterministic Linear Programming with Probabilistic Assignment, Static Bid Price Control, Booking Limit, and Nesting, thus covering both stochastic and adversarial customer arrival settings. Embedding the Grace Period does not incur additional regret for any of these algorithms. This finding indicates that, in an asymptotic regime, there is no tradeoff between a seller maximizing their revenue and guaranteeing that each customer feels fairly treated. The core intuition behind the Grace Period is that independent randomized decisions for each customer often lead to unfair outcomes. However, we cannot eliminate the randomness, as it plays a crucial role in maximizing profit. The Grace Period addresses this by shifting randomness away from individual decisions and applying it instead to the total number of customers receiving a particular decision. This approach preserves revenue potential while mitigating fairness issues.
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