Impact of Decision and Allocation Rules on Selection Decisions

The quality of a selection decision is a function of the decision rule used and the data collected to support the decision. When physical measurements are the basis of the decision data, the measurement sampling scheme controls measurement uncertainty and influences decision quality. Faced with a fixed experimental budget for measurement collection over multiple attributes, a decision-maker must decide how to allocate this budget to best support the selection decision. We expand on previous work in this area of sample allocation in multiple attribute selection decisions to compare the quality of allocation rules derived under various estimation and decision rules. The decision rules considered in this work include the selection of the best (expected value) and multinomial selection with allocation rules developed based on maximum likelihood and Bayesian inference. We derive allocation rules for each of these cases and illustrate their performance through computational experiments.