Source-linked AI summary
Who's talking first? Consensus or lack thereof in coevolving opinion formation models
Cecilia Nardini, Balazs Kozma, Alain Barrat
TL;DR
The paper asks how adaptive rewiring changes opinion dynamics and whether allowing agents to hold multiple opinions alters these effects. Using Voter-like and Naming Game models with mean-field analysis, it finds that rewiring can either accelerate consensus or sustain interacting diversity, while intermediate states restore fast consensus.
Problem
The paper examines how small differences in opinion-update rules interact with evolving network topology to produce sharply different collective outcomes.
Method
The authors analyze direct and reverse Voter-like and Naming Game models on adaptive networks using mean-field equations and numerical simulations.
Results
Rewiring favors consensus in the direct Voter model but produces exponentially long-lived interacting opinion groups in the reverse model, whereas intermediate opinion states yield logarithmic convergence times in both Naming Game variants.
Takeaways & Limitations
Adaptive-network feedback can reverse the effect of rewiring, while allowing agents to retain multiple opinions strongly enhances robust convergence toward consensus.
Abstract
from arXiv · showhide
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.