Preference Programming September 16 , 2004

Methods for dealing with incomplete preference information in hierarchical weighting models have continued to attract attention in the literature on multi-criteria decision analysis (MCDA). In this paper, we give a structured overview of several such methods which (i) accommodate incomplete preference information, (ii) offer dominance concepts and decision rules for the generation of decision recommendations and (iii) support the iterative exploration of the decision maker’s preferences. By doing so, we synthesize much of the relevant literature and provide an integrative perspective on these methods which are here subsumed under the term ‘preference programming’. We then demonstrate that these methods may outperform conventional decision analyses when the costs of preference elicitation are high and, moreover, provide guidelines for responsible uses of preference programming. We conclude by outlining topics for future research.