Statistical screening, selection, and multiple comparison procedures in computer simulation
Proceedings of the 30th conference on Winter simulation
Selecting and ordering populations: a new statistical methodology
Selecting and ordering populations: a new statistical methodology
An empirical evaluation of several methods to select the best system
ACM Transactions on Modeling and Computer Simulation (TOMACS)
New Two-Stage and Sequential Procedures for Selecting the Best Simulated System
Operations Research
Proceedings of the 35th conference on Winter simulation: driving innovation
New developments in ranking and selection: an empirical comparison of the three main approaches
WSC '05 Proceedings of the 37th conference on Winter simulation
Some topics for simulation optimization
Proceedings of the 40th Conference on Winter Simulation
Sequential Sampling to Myopically Maximize the Expected Value of Information
INFORMS Journal on Computing
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Selection procedures help identify the best of a finite set of simulated alternatives. Most work has measured the quality of a selection with the probability of correct selection, P(CS), but the expected opportunity cost of a potentially incorrect decision makes more sense in business contexts. This paper analyzes the first selection procedures that guarantee an upper bound for the expected opportunity cost, in a frequentist sense, of a potentially incorrect selection. The paper therefore bridges a gap between the indifference-zone approach (with frequentist guarantees, but for the P(CS)) and the Bayesian approach to selection procedures (which has considered the opportunity cost). We also provide "unexpected" expected opportunity cost guarantees for several existing indifference-zone selection procedures that were originally designed to provide P(CS) guarantees.