A Large-Scale Group Decision-Making Method Considering Personalized Individual Semantics in a Probabilistic Linguistic Environment
Xiaoxia Xu et al.
What the paper says
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.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.