The knowledge-gradient stopping rule for ranking and selection

Authors:
Peter Frazier;Warren B. Powell
Affiliations:
Princeton University, Olden St. Princeton, N.J.;Princeton University, Olden St. Princeton, N.J.
Venue:
Proceedings of the 40th Conference on Winter Simulation
Year:
2008

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Abstract

We consider the ranking and selection of normal means in a fully sequential Bayesian context. By considering the sampling and stopping problems jointly rather than separately, we derive a new composite stopping/sampling rule. The sampling component of the derived composite rule is the same as the previously introduced LL1 sampling rule, but the stopping rule is new. This new stopping rule significantly improves the performance of LL1 as compared to its performance under the best other generally known adaptive stopping rule, EOC Bonf, outperforming it in every case tested.