Selecting a best multiattribute alternative with partial information about attribute weights

Abstract

Use of approximate weights would greatly simplify decision analysis under certainty since detailed weight elicitation could be avoided. This paper examines the degree to which rank order information about weights can be used to identify a best alternative, or falling uniqueness prescribes an easily implemented rule for selecting a ‘best’ alternative. The prescribed rule uses as weights the centroid of the feasible region defined by the rank order information. In conjunction with the rule, the value of the rank order information can be determined using an ‘expected gain from weight precision’ (EGWP) measure, analogous to ‘expected value of perfect information’ in decision analysis under uncertainty.