A Continuous Opinion Dynamic Model in Co-evolving Networks--A Novel Group Decision Approach

TL;DR

This paper introduces a co-evolving network model for opinion dynamics that incorporates relationship evolution, persistence, and confidence bounds to promote consensus and analyze opinion polarization.

Contribution

It presents a novel framework combining opinion and network co-evolution, considering persistence and antagonism, to enhance group decision stability.

Findings

Higher persistence reduces convergence time.

Group size and initial network topology affect opinion clustering.

Confidence bounds influence the number of opinion clusters.

Abstract

Opinion polarization is a ubiquitous phenomenon in opinion dynamics. In contrast to the traditional consensus oriented group decision making (GDM) framework, this paper proposes a framework with the co-evolution of both opinions and relationship networks to improve the potential consensus level of a group and help the group reach a stable state. Taking the bound of confidence and the degree of individual's persistence into consideration, the evolution of the opinion is driven by the relationship among the group. Meanwhile, the antagonism or cooperation of individuals presented by the network topology also evolve according to the dynamic opinion distances. Opinions are convergent and the stable state will be reached in this co-evolution mechanism. We further explored this framework through simulation experiments. The simulation results verify the influence of the level of persistence on the time cost and indicate the influence of group size, the initial topology of networks and the bound of confidence on the number of opinion clusters.