Minimum adjustment consensus model for multi-person multi-criteria large scale decision-making with trust consistency propagation and opinion dynamics

Abstract

The consensus reaching process (CRP) represents a multi-round dynamic method essential for harmonizing the interests of multiple parties. With the rise of instant messaging and social media, the complexity of individual social trust networks and structures. Therefore, it is crucial to explore the inherent value of trust networks in the context of multi-person multi-criteria large-scale decision-making (MpMcLSDM) to facilitate consensus. This paper develops a minimum adjustment consensus model (MACM) for MpMcLSDM based on social trust network analysis (STNA). First, the consistency path rule and personal traits are defined through STNA, leading to a formulated strategy for the completion of the trust relationship. Subsequently, a novel centrality measure, informed by the consistency path rule, is proposed, and a weight method is devised to determine decision-maker (DM) weights and sub-cluster weights after clustering. This paper further elucidates the implications of consensus level fluctuations on DM self-confidence and opinion inclination. Ultimately, a MACM is constructed within the MpMcLSDM framework, integrating opinion dynamics. A numerical example demonstrates the model’s effectiveness, and comparisons with other methods show its rationale and improvement in performance.