2012/06/27 by Salvatore Corrente, Corrente, Salvatore, Salvatore Greco +4
Computer Science · Decision Sciences · Mathematics · #Computer science #Decision maker #Econometrics #FOS: Mathematics #Fuzzy Logic and Control Systems #Mathematical optimization #Mathematics #Multi-Criteria Decision Making #Multiple-criteria decision analysis #Operations research #Optimization and Control (math.OC) #Ordinal data #Ordinal regression #Preference #Preference elicitation #Rough Sets and Fuzzy Logic #Set (abstract data type) #Statistics #math.OC
paper · pdf · doi:10.48550/arxiv.1206.6317
41 pages, 9 figures
arxiv created 2012/06/27 · openalex publication_date 2012/06/27 · arxiv updated 2012/06/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Robust Ordinal Regression (ROR) is a way of dealing with Multiple Criteria Decision Aiding (MCDA), by considering all sets of parameters of an assumed preference model, that are compatible with preference information given by the Decision Maker (DM). As a result of ROR, one gets necessary and possible preference relations in the set of alternatives, which hold for all compatible sets of parameters or for at least one compatible set of parameters, respectively. In this paper, we extend the MCDA methods based on ROR, by considering one important aspect of decision problems: imprecise evaluations. To deal with imprecise evaluations of some alternatives on particular criteria, we extend the set of considered variables to define necessary and possible preference relations taking into account this imprecision. In consequence, the concepts of necessary and possible preference represent not only all compatible sets of parameters, but also all possible values of the imprecise evaluations of alternatives.