Tightness Without Counterexamples: A New Approach and New Results for Prophet Inequalities

• 成果类型: Article; Early Access
• 署名作者: Jiang, Jiashuo; Ma, Will; Zhang, Jiawei
• 署名单位: Hong Kong University of Science & Technology; Columbia University; New York University
• 刊物名称: MATHEMATICS OF OPERATIONS RESEARCH
• ISSN/ISSBN: 0364-765X
• DOI: 10.1287/moor.2023.0221
• 发表日期: 2025

摘要

Prophet inequalities consist of many beautiful statements that establish tight performance ratios between online and offline allocation algorithms. Typically, tightness is established by constructing an algorithmic guarantee and a worst-case instance separately, whose bounds match as a result of some ingenuity. In this paper, we instead formulate the construction of the worst-case instance as an optimization problem, which directly finds the tight ratio without needing to construct two bounds separately. Our analysis of this optimization problem involves identifying structure in a new Type Coverage dual problem. It can be seen as akin to the celebrated Magician and OCRS (Online Contention Resolution Scheme) problems, except more general, in that it can also provide tight ratios relative to the optimal offline allocation, whereas the earlier problems only establish tight ratios relative to the ex ante relaxation of the offline problem. Through this analysis, our paper provides a unified framework that derives new prophet inequalities and recovers existing ones, with our principal results being twofold. First, we show that the oblivious method of setting a static threshold due to Chawla et al., surprisingly, is best-possible among all static threshold algorithms, under any number k of selection slots. We emphasize that this result is derived without needing to explicitly find any counterexample instances. This implies the tightness of the asymptotic convergence rate of 1 - O(root log k/k) for static threshold algorithms from Hajiaghayi et al. Turning to the independent and identically distributed setting, our second principal result is to use our framework to characterize the tight guarantee (of adaptive algorithms) under any number k of selection slots and any fixed number of agents n.