发表机构
CNRS, Complex Systems Institute of Paris Île-de-France (ISC-PIF); Laboratoire de Physique Théorique et Modélisation, CNRS-CY Cergy Paris Université; EHESS, Centre d’Analyse et de Mathématique Sociales (CAMS); Cergy Paris Université（法国国家科学研究中心，巴黎法兰西岛复杂系统研究所; 理论与建模物理实验室，CNRS-CY塞尔吉巴黎大学; 高等社会科学学院，社会分析与数学中心; 塞尔吉巴黎大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究探讨了Hegselmann-Krause模型的三种现实变体，发现概率邻居选择与确认偏误对共识阈值有相反影响，并在经验网络上验证了社区特定初始化带来的动态差异。
AI 中文摘要
在这项工作中，我们研究了经典Hegselmann-Krause意见动力学模型的三种修改版本，这些版本融入了许多现实世界系统中常见的特征，例如交互智能体选择中的不确定性，以及在影响函数中对意见相关性进行加权评估以增强确认偏误。通过在多种网络拓扑上进行广泛模拟，从风格化网络模型（Barabási-Albert、Erdős-Rényi和Watts-Strogatz网络）到具有社区结构的经验网络，我们识别了每种修改对意见演化和收敛的影响。我们的发现表明，它们对达成共识所需的有界置信阈值产生相反的影响。我们进一步探索了一种基于数据驱动的意见初始化的扩展，应用于经验网络，其中初始意见从每个检测到的社区特定的高斯分布中抽取。虽然三种修改模型的定性效应保持一致，但这种新的初始化策略揭示了网络社区内不同的动态。这些见解为Hegselmann-Krause模型的现实变体（在交互规则和初始意见分布方面）如何影响意见动态提供了新的全面视角，并揭示了结构化社区内共识形成的机制。
英文摘要
In this work, we investigate three modified versions of the classic Hegselmann-Krause opinion dynamics model, incorporating features that are typical of many real-world systems, such as uncertainty in the selection of interacting agents and a weighted evaluation of the relevance of their opinions in the influence function to enhance confirmation bias. Through extensive simulations across different network topologies, ranging from stylized network models (Barabási-Albert, Erdős-Rényi, and Watts-Strogatz networks) to empirical networks with community structure, we identify the influence of each modification on opinion evolution and convergence. Our findings reveal that they exert opposite effects on the bounded confidence threshold required for consensus. We further explore an extension based on a data-driven opinion initialization on the empirical networks, where initial opinions are drawn from Gaussian distributions specific to each detected community. While the qualitative effects of the three modified models remain consistent, this new initialization strategy reveals distinct dynamics within the network communities. These insights provide a new and comprehensive perspective on how realistic variations of the Hegselmann-Krause model, in terms of both interaction rules and initial opinion distributions, affect opinion dynamics and shed light on the mechanisms of consensus formation within structured communities.
