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

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.