Balancing Optimal Large Deviations in Sequential Selection
针对排名与选择问题，提出一种无需调参的自适应分配方法，能高效学习最优静态分配条件，适用于多种参数化采样分布。
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. This paper was accepted by Baris Ata, stochastic models and simulation. Supplemental Material: The e-companion and data are available at https://doi.org/10.1287/mnsc.2022.4527.