An Automatic Linguistic Consensus Model for Sustainability

Minimum Cost Consensus (MCC) models were designed to seek an agreed collective solution that minimizes the cost associated with modifying the preferences of the experts involved in resolving Group Decision-Making (GDM) problems. Initially, these approaches were tailored for handling a vector with numerical evaluations, neglecting the utilization of linguistic data or multi-criteria decision problems. Therefore, this paper proposes a multi-criteria group decision-making approach based on the concept of MCC that can manage linguistic information. To do so, we will generalize MCC models to handle Extended Comparative Linguistic Expressions with Symbolic Translation (ELICIT). ELICIT offers a versatile continuous linguistic representation and enables precise linguistic calculations without sacrificing interpretability or losing information. As a result, we obtain a multi-criteria decision framework that takes the comparative linguistic expressions provided by experts as inputs and returns the consensual collective opinion that is closest to the individual opinions given by the experts. The methodology is applied to a case study at the University of Jaén, Spain, showcasing its applicability in resolving sustainability problems.