A Large-Scale Group Decision-Making Method Considering Personalized Individual Semantics in a Probabilistic Linguistic Environment

Abstract Individual cognitive differences may cause decision makers to interpret the same linguistic terms differently. To address this issue, this paper proposes a novel consensus model for large-scale group decision-making by incorporating personalized individual semantics into a probabilistic linguistic preference framework. A normalization method integrating emotional tone is introduced to refine probabilistic linguistic preference relations, and an additive consistency-based semantic optimization model is developed to assign appropriate linguistic terms to decision makers. To promote interaction among those with similar interests, a weighting method based on semantic similarity and a fuzzy clustering algorithm using personalized individual semantics are employed to form subgroups with similar semantics. A consensus-reaching process, including assessment and feedback stages, is then applied to guide decision makers toward agreement. A case study on environmental project selection verifies the effectiveness and applicability of the proposed approach.