发表机构
Universitat de Barcelona; Fondazione Bruno Kessler; Universitat de Barcelona Institute of Complex Systems (UBICS)（巴塞罗那大学; 布鲁诺·凯斯勒基金会; 巴塞罗那大学复杂系统研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出通用随机过程框架，分析大规模外生冲击对意见形成的影响，揭示冲击对达成共识的利弊条件及新型多峰状态。
AI 中文摘要
大规模事件，如政治丑闻或虚假信息宣传活动，可以突然改变群体的集体意见，并以标准社会物理学的局部交互模型无法捕捉的方式重塑动力学。在此，我们研究当一组交互代理受到突然的、宏观的外生冲击时意见形成的动力学。我们的框架是通用的，适用于任意意见动力学模型；为说明其适用范围，我们考虑了选民模型及其变体。我们验证了该数学描述在完全图及复杂网络上对选民模型达到共识的首达性质具有极好的描述能力。值得注意的是，我们识别了冲击对达成共识可能有利或有害的条件。对于外生事件下的全连接噪声选民模型，我们揭示了一个丰富的相图，其特征是新颖的三峰和不对称双峰状态。最终，这项工作提供了一个严谨而灵活的随机过程框架，用于理解在反复外生压力源下意见动力学的非平衡行为。
英文摘要
Large-scale events such as political scandals or misinformation campaigns can abruptly shift the collective opinion of a population and reshape the dynamics in ways that standard local-interaction models of sociophysics do not capture. Here, we investigate the dynamics of opinion formation when a group of interacting agents is subjected to sudden, macroscopic exogenous shocks. Our framework is general and applies to arbitrary opinion dynamics models; to illustrate its scope, we consider the voter model and variations thereof. We verify that the mathematical description is excellent in describing the first-passage properties to consensus of the voter model on the complete graph and on complex networks. Remarkably, we identify the conditions under which shocks can be advantageous or detrimental to reach consensus. For the case of the all-to-all noisy voter model under exogenous events, we unveil a rich phase diagram characterized by novel trimodal and asymmetric bimodal states. Ultimately, this work provides a rigorous yet flexible stochastic-process framework for understanding the out-of-equilibrium behavior of opinion dynamics under recurrent exogenous stressors.
Comments15 pages, 9 figures
