A consensus reaching model for expert behavior-driven adjustment of expert weights based on picture fuzzy sets

Abstract

This article proposes an expert-driven consensus and decision-making model that comprehensively considers expert behavior in Multi-criteria decision-making (MCDM) scenarios. Under the premise that experts are willing to adjust their viewpoints, the framework strives to reach group consensus to the utmost degree feasible. To tackle experts’ uncertainty during the evaluation process, this article employs the rejection degree in the picture fuzzy sets (PFS) to signify the level of ignorance while they deliver their evaluation opinions. Due to the diversity of expert views, reaching a group consensus is difficult in reality. Therefore, this article additionally presents a strategy for adjusting the weights of experts who did not reach consensus. This approach upholds data integrity and guarantees the precision of the ultimate decision. Finally, this article confirms the efficiency of the aforementioned model by means of a case study on selecting the optimal carbon reduction alternative for Chinese power plants.