Using multiattribute utility theory to avoid bad outcomes by focusing on the best systems in ranking and selection

When making decisions under uncertainty, it seems natural to use constraints on performance to avoid the selection of a particularly bad system. However that intuition has been shown to impair good recommendations as demonstrated by some well-known results in the stochastic optimization literature. Our work on multiattribute ranking and selection procedures demonstrates that Pareto and constraint-based approaches could be used as part of a successful decision process; but a tradeoff-based approach, like multiattribute utility theory, is required to identify the true best system in all but a few special cases. We show that there is no guaranteed strategic equivalence between utility theory and constraint-based approaches when constraints on the means of the performance measures are used in the latter. Hence, a choice must be made as to which is appropriate. In this paper, we extend well-known results in the decision analysis literature to ranking and selection.