Linguistic Multi-Expert Decision Making Involving Semantic Overlapping
ABSTRACT This paper presents a probabilistic model for linguistic multi-expert decision making (MEDM), which is able to deal with vague
concepts in linguistic aggregation and decision-makers’ preference information in choice function. In linguistic aggregation
phase, the vagueness of each linguistic judgement is captured by a possibility distribution on a set of linguistic labels.
A confidence parameter is also incorporated into the basic model to model experts’ confidence degree. The basic idea of this
linguistic aggregation is to transform a possibility distribution into its associated probability distribution. The proposed
linguistic aggregation results in a set of labels having a probability distribution. As a choice function, a target-oriented
ranking method is proposed, which implies that the decision-maker is satisfactory to choose an alternative as the best if
its performance is as at least “good” as his requirements.