Multi-Expert Decision-Making with Linguistic Information: A Probabilistic-Based Model

It is well-known that linguistic decision-making problems that manage preferences from different experts follow a common resolution scheme composed by 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. In this paper we propose a probabilistic-based approach to multi-expert decision-making with linguistic information. To this end, instead of using an aggregation operator to obtain a collective preference, a random preference is defined for each alternative in the aggregation phase. Then, the so-called satisfaction principle suggests a linguistic choice function to establish a rank ordering among the alternatives. The method is illustrated by the same application example taken from the literature to compare with previous methods.