A Novel Social Network Group Decision-Making Method in a Quantum Framework

Abstract

Abstract Social networks (SNs) have become popular as a medium for disseminating information and connecting like-minded people. They play a central role in decision-making by correlating the behaviors and preferences of connected agents. However, it is difficult to identify social influence effects in decision-making. In this article, we propose a framework of how to describe the uncertain nature of the social network group decision-making (SN-GDM) process. Social networks analysis (SNA) and quantum probability theory (QPT) are combined to construct a decision framework considering superposition and interference effects in SN-GDM scenarios. For the first time, we divide interference effects into symmetry and asymmetry. We construct an influence diagram, which is a quantum-like Bayesian network (QLBN), to model group decisions with interactions. We identify symmetry interference terms from Shapley value and asymmetry interference terms from trust value, respectively. The probability of an alternative is calculated through quantum probability theory in our influence diagram. The combination of QLBN model and social network could gain an understanding of how the group preferences evolve within SN-GDM scenarios, and provide new insights into SNA. Finally, an overall comparative analysis is performed with traditional SNA and other quantum decision models.

Keywords

• Social network group decision-making (SN-GDM)
• Quantum-like Bayesian network (QLBN)
• Influence diagram
• Social networks analysis (SNA)