On demonstrating liberating effect in complex social networks: Modeling multiple pressure-coping strategies

Abstract

This paper introduces a novel opinion dynamics model, termed the Multiple Pressure-Coping Strategies (MPCS) mode, to investigate the opinion evolution in social networks under group pressure. The model extends the Expressed and Private Opinions (EPO) model based on the Hegselmann–Krause (HK) framework. It includes a quantifiable pressure function, heterogeneous confidence levels, a two-stage liberating effect mechanism, and a dynamic evolution of the network structure. Simulations conducted on two artificial networks and a real network demonstrate that the MPCS model is more effective in achieving consensus under conditions of initial low confidence levels and significant group pressure compared to the classic HK model. We find the critical role of initial confidence levels in consensus time. Our research highlights the impact of different pressure-coping strategies on opinion evolution. In the early iterations of evolution, agents reduce group pressure by increasing confidence levels and updating the network; in the later iterations, the liberating effect becomes pivotal in shaping opinion dynamics. The role of the liberating effect is reflected in its ability to further reduce the pressure on high-pressure nodes within the group and optimize the network structure, thereby facilitating the achievement of group consensus.