Balancing Optimal Large Deviations in Sequential Selection

Ye Chen () and Ilya O. Ryzhov ()
Additional contact information
Ye Chen: Statistical Sciences and Operations Research, Virginia Commonwealth University, Richmond, Virginia 23284
Ilya O. Ryzhov: Robert H. Smith School of Business, University of Maryland, College Park, Maryland 20742

Management Science, 2023, vol. 69, issue 6, 3457-3473

Abstract: 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.

Keywords: simulation; ranking and selection; probability of correct selection; large deviations (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:69:y:2023:i:6:p:3457-3473

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