A Personalized Individual Semantic Extraction Model Based on Criterion for Adaptive Consensus Reaching Process Under Improved Basic Uncertain Linguistic Environment

Abstract

Personalized individual semantics (PIS) is an important factor reflecting the personal habits of decision makers (DMs) and has been widely studied by scholars. Using criteria as a non-negligible information source in multi-criteria group decision making (MCGDM), how to extract PIS from it is a research gap to be solved. In addition, existing measurements of consensus are insufficiently sensitive to differences between individuals, while the current direction rules use a matrix as the unit of measurement, which is not detailed and precise enough. Therefore, this paper first constructs a PIS extraction model according to the principle that similar criteria have similar descriptions and mutually exclusive criteria have dissimilar descriptions. Secondly, the preference information of PIS is mingled with uncertainty and reliability of improved basic uncertain linguistic information (IBULI) as the data of the consensus reaching algorithm. The proposed consensus algorithm not only fully considers the dispersion of DMs in the consensus measurement stage, but also improves the objectivity of the consensus process through an adaptive feedback stage. Finally, the validity of the proposed model is verified by an example and comparative analysis of the selection of sustainable building materials.