Balancing Optimal Large Deviations in Sequential Selection

• 成果类型: Article
• 署名作者: Chen, Ye; Ryzhov, Ilya O.
• 署名单位: Virginia Commonwealth University; University System of Maryland; University of Maryland College Park
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2022.4527
• 发表日期: 2023
• 页码: 3457-3473
• 关键词: simulation; ranking and selection; probability of correct selection; large deviations

摘要

In the ranking and selection problem, a sampling budget is allocated among a finite number of designs with the goal of efficiently identifying the best. Allocations of this budget may be static (with no dependence on the random values of the samples) or adaptive (decisions are made based on the results of previous decisions). A popular methodological strategy in the simulation literature is to first characterize optimal static allocations by using large deviations theory to derive a set of optimality conditions, and then to use these conditions to guide the design of adaptive allocations. We propose a new methodology that can be guaranteed to adaptively learn the solution to these optimality conditions in a computationally efficient manner, without any tunable parameters, and under a wide variety of parametric sampling distributions.