A general model for how attributes can reduce polarization in social groups

IF 1.4 Q2 SOCIAL SCIENCES, INTERDISCIPLINARY
Piotr J. Górski, Curtis Atkisson, J. Hołyst
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引用次数: 1

Abstract

Polarization makes it difficult to form positive relationships across existing groups. Decreasing polarization may improve political discourse around the world. Polarization can be modeled on a social network as structural balance, where the network is composed of groups with positive links between all individuals in the group and negative links with all others. Previous work shows that incorporating attributes of individuals usually makes structural balance, and hence polarization, harder to achieve. That work examines only a limited number and types of attributes. We present a generalized model and a simulation framework to analyze the effect of any type of attribute, including analytically as long as an expected value can be written for the type of attribute. As attributes, we consider people’s (approximately) immutable characteristics (e.g., race, wealth) and such opinions that change more slowly than relationships (e.g., political preferences). We detail and analyze five classes of attributes, recapitulating the results of previous work in this framework and extending it. While it is easier to prevent than to destabilize polarization, we find that usually the most effective at both are continuous attributes, followed by ordered attributes and, finally, binary attributes. The effectiveness of unordered attributes varies depending on the magnitude of negative impact of having differing attributes but is smaller than of continuous ones. Testing the framework on network structures containing communities revealed that destroying polarization may require introducing local tensions. This model could be used by policymakers, among others, to prevent and design effective interventions to counteract polarization.
属性如何减少社会群体两极分化的通用模型
两极分化使得现有群体之间很难形成积极的关系。减少两极分化可能会改善世界各地的政治话语。两极分化可以在社会网络上建模为结构平衡,其中网络由群体组成,群体中所有个人之间存在积极联系,与所有其他人之间存在消极联系。先前的研究表明,结合个人的属性通常会使结构平衡,从而使两极分化更难实现。这项工作只考察了有限数量和类型的属性。我们提出了一个广义模型和模拟框架来分析任何类型的属性的影响,包括分析,只要可以为该类型的属性编写期望值。作为属性,我们考虑人们(近似)不可变的特征(如种族、财富),以及比关系变化更慢的观点(如政治偏好)。我们详细分析了五类属性,概括并扩展了该框架中先前工作的结果。虽然防止极化比破坏极化更容易,但我们发现,通常在这两类属性中最有效的是连续属性,其次是有序属性,最后是二元属性。无序属性的有效性取决于具有不同属性的负面影响的大小,但小于连续属性。对包含社区的网络结构的框架进行测试表明,破坏两极分化可能需要引入局部紧张局势。除其他外,政策制定者可以利用这一模式来预防和设计有效的干预措施,以对抗两极分化。
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来源期刊
Network Science
Network Science SOCIAL SCIENCES, INTERDISCIPLINARY-
CiteScore
3.50
自引率
5.90%
发文量
24
期刊介绍: Network Science is an important journal for an important discipline - one using the network paradigm, focusing on actors and relational linkages, to inform research, methodology, and applications from many fields across the natural, social, engineering and informational sciences. Given growing understanding of the interconnectedness and globalization of the world, network methods are an increasingly recognized way to research aspects of modern society along with the individuals, organizations, and other actors within it. The discipline is ready for a comprehensive journal, open to papers from all relevant areas. Network Science is a defining work, shaping this discipline. The journal welcomes contributions from researchers in all areas working on network theory, methods, and data.
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