Sequential Bayes-Optimal Policies for Multiple Comparisons with a Known Standard

Jing Xie () and Peter I. Frazier ()
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Jing Xie: School of Operations Research and Information Engineering, Cornell University, Ithaca, New York 14853
Peter I. Frazier: School of Operations Research and Information Engineering, Cornell University, Ithaca, New York 14853

Operations Research, 2013, vol. 61, issue 5, 1174-1189

Abstract: We consider the problem of efficiently allocating simulation effort to determine which of several simulated systems have mean performance exceeding a threshold of known value. Within a Bayesian formulation of this problem, the optimal fully sequential policy for allocating simulation effort is the solution to a dynamic program. When sampling is limited by probabilistic termination or sampling costs, we show that this dynamic program can be solved efficiently, providing a tractable way to compute the Bayes-optimal policy. The solution uses techniques from optimal stopping and multiarmed bandits. We then present further theoretical results characterizing this Bayes-optimal policy, compare it numerically to several approximate policies, and apply it to applications in emergency services and manufacturing.

Keywords: multiple comparisons with a standard; sequential experimental design; dynamic programming; Bayesian statistics; value of information (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (3)

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