Social network large-scale group decision-making considering dynamic trust relationships and historical preferences of decision makers in opinion evolution

Abstract

Individual preferences are formed within intricate social relationships and would be influenced by others’ opinions, especially in large-scale groups, resulting in social network large-scale group decision-making (SNLGDM). As the bridges for the interaction of decision makers (DMs), trust relationships should dynamically evolve with opinions during the decision-making process. However, traditional methods often rely on static social networks and ignore the impact of historical preferences of DMs. Therefore, this study develops a SNLGDM model incorporating dynamic social network, an improved Hegselmann and Krause (HK) model, and historical preferences of DMs to enhance the effectiveness of opinion evolution. First, the initial social network of DMs is constructed by combining the bidirectional and unidirectional trust relationships optimized by the trust propagation model. Then, the dynamic updating mechanisms of the network including trust relationships, confidence sets and relative weights of DMs are designed to ensure the trustworthy interactions. Furthermore, a SNLGDM framework based on VIKOR is proposed, in which, the opinion information is quantified as the Dual hesitant fuzzy sets (DHFSs) and fused by an improved HK model that considers historical preferences of DMs to effectively capture the evolving preferences and reduce information distortion. Finally, the supplier selection for the digital transformation consultation service is established as the case study, and the feasibility and effectiveness of the proposed methodology are validated through results and comparison analysis.