论文标题
Ising模型中的回声室及其对平均磁化的影响
Echo chambers in the Ising model and implications on the mean magnetization
论文作者
论文摘要
Echo-Chamber效应是意见动态建模中的一个常见术语，可以描述一个人的意见如何通过社交互动反映在她身上，从而可以人为地增强。在这里，我们研究了统计力学模型中这种效果的存在，统计力学模型通常用于研究意见动态。我们表明，Ising模型没有表现出回声室，但该结果是特殊对称性的结果。然后，我们区分三种类型的模型：（i）具有强烈回声对称性的强度，根本没有回声室； （ii）那些可以表现出回声室的回声室对称性弱的人，但前提是系统中有外部字段，而没有回声室对称性的模型（iii）通常具有回声室。我们使用这些结果来构建有效的算法来有效，精确地计算任意树网络中的磁化。最后，我们将此算法应用于研究两个系统：在伯特晶格上的随机字段中的相变和社交网络中的影响优化问题。
The echo-chamber effect is a common term in opinion dynamic modeling to describe how a person's opinion might be artificially enhanced as it is reflected back at her through social interactions. Here, we study the existence of this effect in statistical mechanics models, which are commonly used to study opinion dynamics. We show that the Ising model does not exhibit echo-chambers, but this result is a consequence of a special symmetry. We then distinguish between three types of models: (i) those with a strong echo-chamber symmetry, that have no echo-chambers at all; (ii) those with a weak echo-chamber symmetry that can exhibit echo-chambers but only if there are external fields in the system, and (iii) models without echo-chamber symmetry that generically have echo-chambers. We use these results to construct an efficient algorithm to efficiently and precisely calculate magnetization in arbitrary tree networks. Finally, We apply this algorithm to study two systems: phase transitions in the random field Ising model on a Bethe lattice and the influence optimization problem in social networks.
