Non-consensus opinion model with a neutral view on complex networks

A nonconsensus opinion (NCO) model was introduced recently, which allows the stable coexistence of minority and majority opinions. However, due ​to disparities in the knowledge, experiences, and personality or self-protection of agents, they often remain ​neutral when faced with some opinions in real scenarios. ​To address this issue, we propose a general non-consensus opinion model with neutral view (NCON) ​and we define the dynamic opinion ​change process. We applied the NCON model to different topological networks and studied the formation of opinion clusters. In the case of random graphs, random regular networks, and scale-free (SF) networks, we found that the system moved from a continuous phase transition to a discontinuous phase transition as the connectivity density and exponent of the SF network λ ​decreased and increased in the steady state, respectively. Moreover, the initial proportions of neutral opinions were found to have little effect on the proportional structure of opinions at the steady state. These results suggest that the majority choice between positive and negative opinions depends on the initial proportion of each opinion. The NCON model may have potential applications for decision makers.