Category: Network Sci.

Summary: Measuring how the consensus-to-fragmentation threshold sharpens with system size in the bounded-confidence Hegselmann-Krause opinion model.

Bounded-confidence opinion dynamics exhibit a transition between collective consensus and persistent fragmentation, but finite-size scaling of that transition remains a central question. This experiment asks how the critical confidence radius changes with the number of agents and whether the crossover sharpens into a cleaner phase boundary at larger scales.

The code runs GPU-scale Hegselmann-Krause simulations with all-pairs bounded-confidence interactions, allowing much larger agent populations than a naive CPU implementation. It estimates the confidence threshold where the system moves from a single cluster to many clusters and compares that threshold across system sizes.

This makes the project a large-scale phase-transition study of social influence rather than a single-run visualization. The interest is in how finite populations approach a sharper consensus-fragmentation boundary as the system grows.

Method: GPU-accelerated all-pairs Hegselmann-Krause simulations measuring cluster structure while sweeping the confidence scale sigma across multiple system sizes.

What is measured: Critical confidence threshold, number of opinion clusters, finite-size scaling behavior, and consensus-versus-fragmentation outcomes.