Systems Analysis Laboratory
Helsinki University of Technology
Managing incomplete information in decision analytic models
Preference programming subsumes several methods which accommodate and synthesize incomplete preference information in multicriteria weighting models. By doing so, these methods respond to the difficulties of managing uncertainty and, moreover, enable interactive decision aiding processes where tentative recommendations can be offered at any stage of the decision support process. This talk reviews the premises and key concepts of preference programming and illustrates them through real-life applications to environmental decision making and the valuation of high-technology firms, among others. In addition, recent advances in the use of preference programming to robust portfolio modelling are outlined in view of case studies on project selection, strategic decision making and ex post evaluation of policy measures.