An Approach Based on Granular Computing and 2-Tuple Linguistic Model to Personalize Linguistic Information in Group Decision-Making

Group decision-making is an inherently collaborative process that can become increasingly complex when addressing the uncertainty associated with linguistic assessments from experts. A crucial principle for achieving a solution of superior quality lies in the acknowledgment that the same word may bear divergent meanings among different experts. Regrettably, a significant number of existing methodologies for computing with words presuppose a uniformity of meaning for linguistic assessments across all participating individuals. In response to this limitation, we propose an innovative methodology based on the 2-tuple linguistic model in conjunction with the granular computing paradigm. Given that the individual interpretations of words, when articulating preferences, are closely linked to the consistency of each expert, our proposal places particular emphasis on the modification of the symbolic translation of the 2-tuple linguistic value with the overarching objective of maximizing the consistency of their assessments. This adjustment is implemented while preserving the original linguistic preferences communicated by the experts. We address a real-world building refurbishment problem and conduct a comparative analysis to demonstrate the effectiveness of the proposal. Focusing on consistency enhances group decision-making processes and outcomes, ensuring both accuracy and alignment with individual interpretations and preferences.