Multiattribute decision analysis using strict preference relations

To identify multiattribute utility functions under the assumption of the mutual utility independence, a decision maker must specify indifference points and subjective probabilities precisely. By relaxing this rigorous evaluation, multiattribute value models with incomplete information have been developed. In this paper, we present a new method finding the best alternative through strict preference relations derived from the decision maker by asking questions that are relatively easy to answer compared to the indifference questions. In our method, alternatives consistent with the derived strict preference relations are obtained by solving mathematical programming problems with constraints representing the preference relations, and eventually we can find the most preferred alternative by deriving additional preference relations.