Who's talking first? Consensus or lack thereof in coevolving opinion formation models

TL;DR

This paper explores how opinion formation models on adaptive networks can either rapidly reach consensus or maintain diversity, depending on the dynamics and multi-opinion states, with mean-field analysis clarifying these mechanisms.

Contribution

It introduces the impact of multi-opinion states on opinion formation models, revealing how they can accelerate consensus or sustain diversity, supported by mean-field analysis.

Findings

Rewiring can lead to consensus or persistent diversity.

Multi-opinion states significantly speed up consensus.

Mean-field analysis clarifies the underlying mechanisms.

Abstract

We investigate different opinion formation models on adaptive network topologies. Depending on the dynamical process, rewiring can either (i) lead to the elimination of interactions between agents in different states, and accelerate the convergence to a consensus state or break the network in non-interacting groups or (ii) counter-intuitively, favor the existence of diverse interacting groups for exponentially long times. The mean-field analysis allows to elucidate the mechanisms at play. Strikingly, allowing the interacting agents to bear more than one opinion at the same time drastically changes the model's behavior and leads to fast consensus.