A probabilistic model for linguistic multi-expert decision making involving semantic overlapping

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The main objective of linguisticmulti-expertdecisionmaking (MEDM) is to select the best alternative(s) using linguistic judgements provided by multiple experts. This paper presents a probabilistic model for linguistic MEDM, which is able to deal with semantic overlapping 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. A comparative analysis with prior research is also given to show the advantages of our model via an example borrowed from the literature. The main advantage of our model is its capacity to deal with linguistic labels having partial semantic overlapping as well as incorporate experts and decision-makers’ preferences.

identifier:https://dspace.jaist.ac.jp/dspace/handle/10119/10722

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