The impact of social noise on the majority rule model across various network topologies

IF 5.3 1区 数学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Chaos Solitons & Fractals Pub Date : 2024-11-12 DOI:10.1016/j.chaos.2024.115718
Roni Muslim , Didi Ahmad Mulya , Zulkaida Akbar , Rinto Anugraha NQZ
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Abstract

We explore the impact of social noise, characterized by nonconformist behavior, on the phase transition within the framework of the majority rule model. The order–disorder transition can reflect the consensus-polarization state in a social context. This study covers various network topologies, including complete graphs, two-dimensional (2-D) square lattices, three-dimensional (3-D) square lattices, and heterogeneous or complex networks such as Watts–Strogatz (W–S), Barabási–Albert (B–A), and Erdős–Rényi (E–R) networks, as well as their combinations (multilayer network). Social behavior is represented by the parameter p, which indicates the probability of agents exhibiting nonconformist behavior. Our results show that the model exhibits a continuous phase transition across all networks. Through finite-size scaling analysis and evaluation of critical exponents, our results suggest that the model falls into the same universality class as the Ising model.
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不同网络拓扑结构下社会噪音对多数规则模型的影响
我们在多数规则模型的框架内,探讨了以不服从行为为特征的社会噪音对相变的影响。有序-无序转换可以反映社会背景下的共识-极化状态。本研究涵盖各种网络拓扑结构,包括完整图、二维(2-D)方格、三维(3-D)方格和异构或复杂网络,如瓦特-斯特罗加茨(W-S)、巴拉巴西-阿尔伯特(B-A)和厄尔多斯-雷尼(E-R)网络,以及它们的组合(多层网络)。社会行为由参数 p 表示,该参数表示代理人表现出不守规矩行为的概率。我们的研究结果表明,该模型在所有网络中都表现出连续的相变。通过有限规模缩放分析和临界指数评估,我们的结果表明,该模型与伊辛模型属于同一普遍性类别。
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来源期刊
Chaos Solitons & Fractals
Chaos Solitons & Fractals 物理-数学跨学科应用
CiteScore
13.20
自引率
10.30%
发文量
1087
审稿时长
9 months
期刊介绍: Chaos, Solitons & Fractals strives to establish itself as a premier journal in the interdisciplinary realm of Nonlinear Science, Non-equilibrium, and Complex Phenomena. It welcomes submissions covering a broad spectrum of topics within this field, including dynamics, non-equilibrium processes in physics, chemistry, and geophysics, complex matter and networks, mathematical models, computational biology, applications to quantum and mesoscopic phenomena, fluctuations and random processes, self-organization, and social phenomena.
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