A Collective Decision Model Involving Vague Concepts and Linguistic Expressions

In linguistic collective decision, the main objective is to select the best alternatives using linguistic evaluations provided by multiple experts. This paper presents a collective decision model, which is able to deal with complex linguistic evaluations. In this decision model, the linguistic evaluations are represented by linguistic expressions which are the logic formulas obtained by applying logic connectives to the set of basic linguistic labels. The vagueness of each linguistic expression is implicitly captured by a semantic similarity relation rather than a fuzzy set, since each linguistic expression determines a semantic similarity distribution on the set of basic linguistic labels. The basic idea of this collective decision model is to convert the semantic similarity distributions determined by linguistic expressions into probability distributions of the corresponding linguistic expressions. The main advantage of this proposed model is its capability to deal with complex linguistic evaluations and partial semantic overlapping among neighboring linguistic labels.