We study a dynamic matching setting where homogeneous agents arrive at random according to a Poisson process and randomly form edges yielding a sparse market. Agents stay in the market according to a certain sojourn time and wait to be matched with a compatible agent by a matching algorithm. When their maximum sojourn time is reached, they perish unmatched. The primary objective is to maximize the number of matched agents. Our main result is to show that a uniformly guaranteed sojourn time suffices to get almost optimal performance of instantaneous matching. Interestingly, this matching policy essentially keeps as few agents in the market as possible. Hence, in contrast to the common paradigm that market thickness is the crucial property for obtaining strong matching performance, we show that the agents' sojourn behavior can be an equally powerful factor. In addition, instantaneous matching is close to optimal with respect to minimizing waiting time. We develop new techniques for proving our results going beyond commonly adopted methods for Markov processes.
翻译:我们研究一个动态匹配场景,其中同质智能体按照泊松过程随机到达,并随机形成边,从而构成一个稀疏市场。智能体根据特定的停留时间停留在市场中,等待匹配算法将其与兼容的智能体匹配。当达到其最大停留时间时,它们将未被匹配而退出。主要目标是最大化被匹配智能体的数量。我们的主要结果表明,均匀保证的停留时间足以使即时匹配获得近乎最优的性能。有趣的是,这种匹配策略本质上尽可能减少市场中停留的智能体数量。因此,与市场厚度是获得强大匹配性能的关键属性的常见范式相反,我们表明智能体的停留行为可以成为同等重要的因素。此外,即时匹配在最小化等待时间方面接近最优。我们开发了新的技术来证明我们的结果,超越了马尔可夫过程常用的方法。