In matching markets such as kidney exchanges and freight exchanges, delayed matching has been shown to improve overall market efficiency. The benefits of delay are highly sensitive to participants' sojourn times and departure behavior, and delaying matches can impose significant costs, including longer waiting times and increased market congestion. These competing effects make fixed matching policies inherently inflexible in dynamic environments. We propose a learning-based Hybrid framework that adaptively combines immediate and delayed matching. The framework continuously collects data on user departures over time, estimates the underlying departure distribution via regression, and determines whether to delay matching in the subsequent period based on a decision threshold that governs the system's tolerance for matching efficiency loss. The proposed framework can substantially reduce waiting times and congestion while sacrificing only a limited amount of matching efficiency. By dynamically adjusting its matching strategy, the Hybrid framework enables system performance to flexibly interpolate between purely greedy and purely patient policies, offering a robust and adaptive alternative to static matching mechanisms.

翻译：在肾脏交换和货运交换等匹配市场中，延迟匹配已被证明能够提高整体市场效率。延迟的收益高度依赖于参与者的停留时间和离开行为，而延迟匹配可能带来显著成本，包括更长的等待时间和加剧的市场拥堵。这些相互竞争的影响使得固定匹配策略在动态环境中本质上缺乏灵活性。我们提出一种基于学习的混合框架，能够自适应地结合即时匹配与延迟匹配。该框架持续收集用户随时间离开的数据，通过回归估计潜在的离开分布，并根据控制系统对匹配效率损失容忍度的决策阈值，决定是否在后续时段延迟匹配。所提出的框架能够显著减少等待时间和拥堵，同时仅牺牲有限的匹配效率。通过动态调整其匹配策略，混合框架使系统性能能够在纯贪婪策略与纯耐心策略之间灵活插值，为静态匹配机制提供了一种鲁棒且自适应的替代方案。