A robust ordinal regression feedback consensus model with dynamic trust propagation in social network group decision-making

Abstract

This paper proposes a robust ordinal regression feedback consensus model based on trust relationships for social network group decision-making problems in intuitionistic fuzzy environment. The model consists of two main parts: (a) the construction of the complete trust matrix among decision makers (DMs) and (b) the design of a feedback mechanism formulated on robust ordinal regression. In Part (a), to complete the initial trust matrix between DMs, a dynamic trust propagation mode founded on maximum credibility is proposed to investigate indirect trust relationships between unfamiliar DMs from the perspective of overall optimization. In Part (b), when the group consensus does not reach the consensus threshold, the utility function compatible with the preference information of DMs is constructed through robust ordinal regression, and the most representative utility function is selected by maximizing the difference of the necessary weak preference relationship. In addition, the performance of DMs under four evaluation dimensions, individual consensus degree, individual comprehensive influence, individual opinion retention, and individual proximity centrality, is aggregated into a total utility value. In each iteration, the DM with the lowest total utility value is identified for opinion adjustments. Further, based on trust-similarity analysis, recommendations are designed for opinion adjustments to improve the group consensus. An illustrative example along with the related sensitivity analysis and comparative study is used to examine the effectiveness and advantages of the proposed model.