Spontaneous opinion swings in the voter model with latency.

IF 2.2 3区 物理与天体物理 Q2 PHYSICS, FLUIDS & PLASMAS Physical Review E Pub Date : 2024-08-01 DOI:10.1103/PhysRevE.110.024313
Giovanni Palermo, Anna Mancini, Antonio Desiderio, Riccardo Di Clemente, Giulio Cimini
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Abstract

The cognitive process of opinion formation is often characterized by stubbornness or resistance of agents to changes of opinion. To capture this feature we introduce a constant latency time in the standard voter model of opinion dynamics: after switching opinion, an agent must keep it for a while. This seemingly simple modification drastically changes the stochastic diffusive behavior of the original model, leading to deterministic dynamical oscillations in the average opinion of the agents. We explain the origin of the oscillations and develop a mathematical formulation of the dynamics that is confirmed by extensive numerical simulations. We further characterize the rich phase space of the model and its asymptotic behavior. Our work offers insights into understanding and modeling the phenomenon of opinion swings, often observed in diverse social contexts.

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有延迟的选民模型中的自发意见波动。
在意见形成的认知过程中,代理人通常会固执己见或抵制意见的改变。为了捕捉这一特征,我们在意见动态的标准投票者模型中引入了一个恒定的延迟时间:在转换意见后,代理人必须保持一段时间。这一看似简单的修改极大地改变了原始模型的随机扩散行为,导致了代理人平均意见的确定性动态振荡。我们解释了振荡的起源,并建立了动态的数学公式,该数学公式得到了大量数值模拟的证实。我们进一步描述了模型丰富的相空间及其渐近行为。我们的研究为理解和模拟在不同社会环境中经常出现的意见波动现象提供了启示。
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来源期刊
Physical Review E
Physical Review E PHYSICS, FLUIDS & PLASMASPHYSICS, MATHEMAT-PHYSICS, MATHEMATICAL
CiteScore
4.50
自引率
16.70%
发文量
2110
期刊介绍: Physical Review E (PRE), broad and interdisciplinary in scope, focuses on collective phenomena of many-body systems, with statistical physics and nonlinear dynamics as the central themes of the journal. Physical Review E publishes recent developments in biological and soft matter physics including granular materials, colloids, complex fluids, liquid crystals, and polymers. The journal covers fluid dynamics and plasma physics and includes sections on computational and interdisciplinary physics, for example, complex networks.
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