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Attributes provide critical information about the alternatives that adecision-maker is considering. When their magnitudes are uncertain, thedecision-maker may be unsure about which alternative is truly the best, someasuring the attributes may help the decision-maker make a better decision.This paper considers settings in which each measurement yields one sample ofone attribute for one alternative. When given a fixed number of samples tocollect, the decision-maker must determine which samples to obtain, make themeasurements, update prior beliefs about the attribute magnitudes, and thenselect an alternative. This paper presents the sample allocation problem formultiple attribute selection decisions and proposes two sequential, lookaheadprocedures for the case in which discrete distributions are used to model theuncertain attribute magnitudes. The two procedures are similar but reflectdifferent quality measures (and loss functions), which motivate differentdecision rules: (1) select the alternative with the greatest expected utilityand (2) select the alternative that is most likely to be the truly bestalternative. We conducted a simulation study to evaluate the performance of thesequential procedures and hybrid procedures that first allocate some samplesusing a uniform allocation procedure and then use the sequential, lookaheadprocedure. The results indicate that the hybrid procedures are effective;allocating many (but not all) of the initial samples with the uniformallocation procedure not only reduces overall computational effort but alsoselects alternatives that have lower average opportunity cost and are moreoften truly best.

Lookahead and Hybrid Sample Allocation Procedures for Multiple Attribute Selection Decisions