标题： 信念系统动力学作为单层神经网络的网络 显示英文标题

标题： Belief System Dynamics as Network of Single Layered Neural Network

摘要： 随着政治极化问题和错误信息传播的严重性增加，社交网络上的信念传播成为一个重要的研究课题。 此前，在算法方法方面已经取得了突破，用以理解群体共识或极化如何在一个群体中发生。 本文提出了一种修改后的弗里德金-约翰森模型，该模型试图通过将每个个体视为特定陈述证据集上的一层神经网络来解释个体的固执以及可能的反作用效应，其中输入是个体在每条证据上的置信水平，而对该陈述的信念则是这个神经网络的输出。 在本文中，我们重申了麦迪逊对派系危害的补救措施的重要性，并发现当理解结构出现极化时，具有巨组件的网络比有两个社区的网络更能降低信念分布的方差，但这样做会创造更多的社会压力。 我们还发现，当社区结构形成时，信念分布的方差对个体的置信水平变得不那么敏感。 该模型可以应用于由错误信息和冲突的经济利益引起的政界和历史问题，也可以应用于人格理论和行为心理学。 显示英文摘要

摘要： As problems in political polarization and the spread of misinformation become serious, belief propagation on a social network becomes an important question to explore. Previous breakthroughs have been made in algorithmic approaches to understanding how group consensus or polarization can occur in a population. This paper proposed a modified model of the Friedkin-Johnsen model that tries to explain the underlying stubbornness of individual as well as possible back fire effect by treating each individual as a single layer neural network on a set of evidence for a particular statement with input being confidence level on each evidence, and belief of the statement is the output of this neural network. In this papar, we reafirmed the importance of Madison's cure for the mischief of faction, and found that when structure of understanding is polarized, a network with a giant component can decrease the variance in the belief distribution more than a network with two communities, but creates more social pressure by doing so. We also found that when community structure is formed, variance in the belief distribution become less sensitive to confidence level of individuals. The model can have various applications to political and historical problems caused by misinfomation and conflicting economic interest as well as applications to personality theory and behavior psychology.