Nonmonotonic consensus transitions in bounded-confidence dynamics on unbiased networks
Paolo Molignini

TL;DR
This paper investigates how network connectivity and confidence bounds influence opinion consensus in the Hegselmann-Krause model on sparse networks, revealing nonmonotonic transitions and the impact of structural factors.
Contribution
It introduces a comprehensive phase diagram analysis of opinion dynamics on sparse networks, uncovering non-intuitive effects of increased connectivity on consensus formation.
Findings
Increased connectivity can suppress consensus, showing a nonmonotonic transition.
Full unanimity is impossible at low connectivity due to structural isolation.
Convergence times show two distinct slowdowns related to system size and phase transitions.
Abstract
We study the Hegselmann-Krause model of opinion dynamics on sparse, unbiased networks generated via Wilson's algorithm, unveiling how network connectivity and confidence bounds jointly determine collective behavior. By systematically exploring the parameter space spanned by the confidence level and the mean degree density , we construct comprehensive phase diagrams that classify the emergent steady states into different degrees of fragmentation and consensus. We uncover a nonmonotonic re-entrant transition where increased connectivity can paradoxically suppress consensus, and show that full unanimity is unattainable at low connectivity due to structural isolation. Convergence times exhibit two distinct slowdowns: a finite-size, connectivity-dependent resonance near , and a critical peak associated with the established fragmentation-to-consensus…
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