Managing Ignorance Elements and Personalized Individual Semantics Under Incomplete Linguistic Distribution Context in Group Decision Making

Linguistic distribution expressions provide a flexible way for decision makers to express their opinions in linguistic decision making. When working with a linguistic distribution, words mean different things for different people, i.e., decision makers have personalized individual semantics (PISs) regarding words. Therefore, in this paper, we propose a consistency-driven methodology to manage distribution linguistic preference relations (DLPRs) with PISs. This methodology can not only estimate the ignorance elements in incomplete DLPRs but also obtain the personalized numerical meanings of linguistic expressions to decision makers. In this way, we can combine the characteristics of the personalized representation in linguistic decision making and guarantee the optimum consistency of incomplete DLPRs with ignorance elements. Detailed numerical and comparison analyses have been proposed to justify our proposal.

Automated summary

This study proposes a consistency-driven methodology to manage distribution linguistic preference relations with personalized individual semantics, ensuring optimal consistency of incomplete DLPRs. Detailed numerical and comparison analyses have been provided to justify the proposal.