Opinion dynamics on dense dynamic random graphs

TL;DR

This paper analyzes how opinions evolve and stabilize on dense dynamic random graphs, revealing conditions for consensus, polarization, or coexistence, supported by theoretical laws and simulations.

Contribution

It introduces a novel coupling method for co-evolutionary opinion-graph models, providing functional laws of large numbers and characterizations of opinion densities.

Findings

Consensus, polarization, and coexistence regimes identified.

Limiting opinion densities characterized by Beta-distributions.

Method applicable to other dense co-evolutionary models.

Abstract

We consider two-opinion voter models on dense dynamic random graphs. Our goal is to understand and describe the occurrence of consensus versus polarisation over long periods of time. The former means that all vertices have the same opinion, the latter means that the vertices split into two communities with different opinions and few disagreeing edges. We consider three models for the joint dynamics of opinions and graphs: one with a one-way feedback and two which are co-evolutionary, i.e., with a two-way feedback. In the first model only coexistence is attainable, meaning that both opinions survive, but with the presence of many disagreeing edges. In the second model only consensus prevails, while in the third model polarisation is possible. Our main results are functional laws of large numbers for the densities of the two opinions, functional laws of large numbers for the dynamic random graphs in the space of graphons, and a characterisation of the limiting densities in terms of Beta-distributions. Our results are supported by simulations. To prove our results we develop a novel method that involves coupling the co-evolutionary process to a mimicking process with one-way feedback. We expect that this method can be extended to other dense co-evolutionary models.