title: A satisfactory-oriented approach to multiexpert decision-making with linguistic assessments.
abstract: This paper proposes a multiexpert decision-making (MEDM) method with linguistic assessments, making use of the notion of random preferences and a so-called satisfactory principle. It is well known that decision-making problems that manage preferences from different experts follow a common resolution scheme composed of two phases: an aggregation phase that combines the individual preferences to obtain a collective preference value for each alternative; and an exploitation phase that orders the collective preferences according to a given criterion, to select the best alternative/s. For our method, instead of using an aggregation operator to obtain a collective preference value, a random preference is defined for each alternative in the aggregation phase. Then, based on a satisfactory principle defined in this paper, that says that it is perfectly satisfactory to select an alternative as the best if its performance is as at least "good" as all the others under the same evaluation scheme, we propose a linguistic choice function to establish a rank ordering among the alternatives. Moreover, we also discuss how this linguistic decision rule can be applied to the MEDM problem in multigranular linguistic contexts. Two application examples taken from the literature are used to illuminate the proposed techniques.
authors.affiliation: Japan Advanced Institute of Science and Technology, Tatsunokuchi, Ishikawa 923-1292, Japan. huynh@jaist.ac.jp
authors.forename: Van-Nam
authors.initials: VN
authors.lastname: Huynh
authors.forename: Yoshiteru
authors.initials: Y
authors.lastname: Nakamori
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003661:Decision Support Techniques; D017143:Fuzzy Logic; D016247:Information Storage and Retrieval / Q000379:methods*; D008037:Linguistics / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity