Reaching Consensus Based on the Opinion Dynamics in Social Networks

Abstract

This paper studies the minimum consensus cost strategy in the whole group decision-making (GDM) process under the condition of experts’ opinion and weight evolving over time. Based on the traditional minimum cost consensus model (MCCM), the updated algorithm of opinion dynamics is introduced to obtain the expert’s opinion value and weight, which is used for the optimal strategy of group consensus decision making. In the existing MCCM, the consensus mechanism is established through a simple optimization model, in which each expert’s opinion value and weight coefficient are in a static state and do not change dynamically with time. However, in actual GDM, the expert’s opinion and their own importance (weight coefficient) are random and easily affected by the decision-making environment. Therefore, this paper proposes three novel kinds of MCCMs based on opinion dynamics in social networks. Numerical results show that this method can reach a higher degree of consensus in a reasonable time compared with traditional consensus model. In addition, some comparative experimental data also show that compared with some static consensus, the optimal decision strategy proposed can greatly reduce the cost. In particular, the MCCM model with confidence parameter reduces the consensus cost to the lowest and obtains the highest degree of consensus.