Voter-like Dynamics with Conflicting Preferences on Modular Networks

Two of the main factors shaping an individual's opinion are social coordination and personal preferences, or personal biases. To understand the role of those and that of the topology of the network of interactions, we study an extension of the voter model proposed by Masuda and Redner (2011), where the agents are divided into two populations with opposite preferences. We consider a modular graph with two communities that reflect the bias assignment, modeling the phenomenon of epistemic bubbles. We analyze the models by approximate analytical methods and by simulations. Depending on the network and the biases' strengths, the system can either reach a consensus or a polarized state, in which the two populations stabilize to different average opinions. The modular structure generally has the effect of increasing both the degree of polarization and its range in the space of parameters. When the difference in the bias strengths between the populations is large, the success of the very committed group in imposing its preferred opinion onto the other one depends largely on the level of segregation of the latter population, while the dependency on the topological structure of the former is negligible. We compare the simple mean-field approach with the pair approximation and test the goodness of the mean-field predictions on a real network.

Automated summary

Social coordination and personal preferences are important factors shaping an individual's opinion. The topology of the network of interactions also plays a significant role. This study examines an extension of the voter model, where agents are divided into populations with opposite preferences, using both analytical and simulation methods. The results show that the modular structure of the network increases polarization, and the success of imposing one group's preferred opinion on the other depends on the segregation of the latter population rather than the topological structure of the former. The mean-field approach is compared with the pair approximation, and its predictions are validated on a real network.