Noisy Hegselmann-Krause Systems: Phase Transition and the 2R-Conjecture
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Abstract (arXiv)
The classic Hegselmann-Krause (HK) model for opinion dynam- ics consists of a set of agents on the real line, each one instructed to move, at every time step, to the mass center of all the agents within a fixed distance R. In this work, we investigate the effects of noise in the continuous-time version of the model as described by its mean-field limiting Fokker-Planck equation. In the presence of a finite number of agents, the system exhibits a phase transition from order to disorder as the noise increases. The ordered phase features clusters whose width depends only on the noise level. We introduce an order parameter to track the phase transition and resolve the corresponding phase dia- gram. The system undergoes a phase transition for small R but none for larger R. Based on the stability analysis of the mean-field equation, we derive the existence of a forbidden zone for the disordered phase to emerge. We also provide a theoretical explanation for the well-known 2R conjecture, which states that, for a random initial distribution in a fixed interval, the final configuration consists of clusters separated by a distance of roughly 2R. Our theoretical analysis also confirms previous simulations and predicts properties of the noisy HK model in higher dimension.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 Nov 2015.
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Citing papers per year: 2022: 1; 2023: 1; 2024: 0; 2025: 0; 2026: 2.
4 papers.
- 2022Neural parameter calibration for large-scale multi-agent modelscited by 14
- 2026Krause Synchronization Transformerscited by 5
- 2026Phase transitions in Doi-Onsager, Noisy Transformer, and other multimodal modelscited by 2
- 2023Machine Learning for the identification of phase-transitions in interacting agent-based systems: a Desai-Zwanzig examplecited by 0
- 2026Phase transitions in Doi-Onsager, Noisy Transformer, and other multimodal models17 Apr 2026
- 2026Krause Synchronization Transformers12 Feb 2026
- 2023Machine Learning for the identification of phase-transitions in interacting agent-based systems: a Desai-Zwanzig example29 Oct 2023
- 2022Neural parameter calibration for large-scale multi-agent models27 Sep 2022