论文标题
当拉动转弯时：意见动态的连续时间模型
When Pull Turns to Shove: A Continuous-Time Model for Opinion Dynamics
论文作者
论文摘要
舆论动态的准确建模有可能帮助我们理解两极分化以及有效的政治话语成为可能或不可能的原因。在这里，我们使用基于物理的方法来模拟不断分布的人群中政治观点的演变。我们利用一个无网络的系统来确定政治影响力和对感知内容的反应的局部呼吸，远端抑制动态。我们的方法允许纳入组间偏见，从而使来自受信任的小组内来源的消息比群体外部偏爱更大。我们能够通过使用代表偏见环境的概率功能将这些非线性微观动力学推断到宏观种群分布。我们提出的框架可以重现现实世界的政治分布和实验观察到的动态，并且随着更多数据的可用性，可以进一步完善。
Accurate modeling of opinion dynamics has the potential to help us understand polarization and what makes effective political discourse possible or impossible. Here, we use physics-based methods to model the evolution of political opinions within a continuously distributed population. We utilize a network-free system of determining political influence and a local-attraction, distal-repulsion dynamic for reaction to perceived content. Our approach allows for the incorporation of intergroup bias such that messages from trusted in-group sources enjoy greater leeway than out-group ones. We are able to extrapolate these nonlinear microscopic dynamics to macroscopic population distributions by using probabilistic functions representing biased environments. The framework we put forward can reproduce real-world political distributions and experimentally observed dynamics, and is amenable to further refinement as more data becomes available.
