Preference Programming – Multicriteria Weighting Models under Incomplete Information

Useful decision recommendations can often be provided even if the model parameters are not exactly specified. The recognition of this fact has spurred the development of multicriteria methods which are capable of admitting and synthesizing incomplete preference information in hierarchical weighting models. These methods share similarities in that they (i) accommodate incomplete preference information through set inclusion, (ii) offer decision recommendations based on dominance concepts and decision rules, and (iii) support the iterative exploration of the decision maker’s preferences. In this Chapter, we review these methods which are jointly referred to by the term ‘preference programming’. Specifically, we discuss the potential benefits of using them, and provide tentative guidelines for their deployment.