Modeling Personalized Individual Semantics and Consensus in Comparative Linguistic Expression Preference Relations With Self-Confidence: An Optimization-Based Approach

Comparative linguistic expression preference relations (CLEPRs) are an effective tool to represent uncertain opinions of decision makers in group decision making (GDM). Nevertheless, multiple self-confidence levels are not considered by existing research on CLEPRs. Thus, this article proposes CLEPRs with self-confidence by considering multiple self-confidence levels and presents a way to measure their consistency level. Meanwhile, personalized individual semantics (PIS), indicating that words mean different things for different people, have been highlighted and investigated in the GDM with linguistic assessment information. Considering PIS in comparative linguistic expressions, this article proposes an optimization model based on the consistency-driven methodology to assess individual semantics in CLEPRs with self-confidence. Particularly, the PIS are described and addressed by setting different numerical scales of linguistic terms for different decision makers. Finally, an optimization-based consensus model is proposed to obtain a consensual collective solution, which seeks to minimize the information loss between the decision makers' preference relations with self-confidence and corresponding individual preference vectors.

Automated summary

This article introduces a model of CLEPRs with self-confidence which takes into account multiple levels of confidence among decision makers, as well as the impact of individual semantics. By setting different numerical scales to address Personalized Individual Semantics (PIS), the article proposes an optimization model. Additionally, an optimization-based consensus model is proposed to minimize information loss between decision makers' preference relations and individual preference vectors.