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

J R W Merrick, D Morrice and J C Butler

Journal of Simulation, 2015, vol. 9, issue 3, 238-248

Abstract: 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.

Date: 2015
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DOI: 10.1057/jos.2014.34

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