Exploring the foundation of social diversity and coherence with a novel attraction–repulsion model framework

Cui, Peng-Bi

doi:10.1016/j.physa.2023.128714

Cited by 5 publications

(12 citation statements)

References 54 publications

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“…More in detail, the presence of opinion-based feedback can facilitate stable integrated opinion cluster centering on a neutral point (see Figure 1A), along with stable considerable S and low-level SD (see Figure 2A). Since individuals are allowed to remain greatly sensitive to the diverse views of others in spite of their opinion distance being slight (see Supplementary Figure S1A), this occurs in frequent opinion exchanges because of both being rather sensitive and have weak mutual attraction [39], further leading to persisting violently oscillating average susceptibility (see Supplementary Figure S1A). In such cases, the system does not easily get trapped in a monotonous state such as GC or AP.…”

Section: Resultsmentioning

confidence: 99%

“…The model considers a population of size N where each individual is typified by an opinion x i (t) at time t, which is a real number in the interval x i (t) ∈ [−10, + 10]. We adopt the opinion model proposed by [39], where the updating rules of an individual's opinion x i (t) are formulated by the following equations:…”

Section: Modelmentioning

confidence: 99%

“…, x N ). We measure the opinion diversity by calculating the opinion entropy S) of the population Cui [39], S xmax xmin xρ x , so as to capture the emergence of the HD state. ρ x Nx N is the density of the individual owning opinion x, and N x denotes the population of the opinion x.…”

Section: Modelmentioning

confidence: 99%

“…Inspired by the phase-identification method developed in [39], we still numerically estimate the boundaries between different states by means of the susceptibility of not only S but also SD.…”

Section: Modelmentioning

confidence: 99%

“…In order to address these questions arising from the lack of micro foundation in AP, we adopt a novel attraction-repulsion model (ARM) that captures the coupled feedback between individuals' opinions and their responses, which essentially belongs to the negative feedback class. The proposed model constructs an adaptive-system perspective on the effects of the rules on emergent dynamics of not only AP and global census (GC) but also the "harmony with diversity" (HD) state, which has been recently identified [39]. It should be noted that the opinion updating rules in our model paradigm are based on a simple assumption that an individual's attraction to or repulsion from others is only governed by the opinion dissimilarity between them, rather than the positions themselves.…”

Section: Introductionmentioning

confidence: 99%

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Exploring the formation dynamics of affective polarization by considering coupled feedback

Cui

2023

Front. Phys.

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Polarization issues are generally subject to ideological and affective polarization. Particularly, affective polarization generally accelerates the polarization process. Yet, a wide array of the existing literature has not provided valid ways to make distinctions between them. Therefore, the mechanism contributing to the rise of affective polarization still remains unclear, along with its unique emergent dynamics. To address this issue, we introduces the coupled feedback between opinions and response susceptibility to an attraction–repulsion model which takes into account three parameters: interaction strength, response susceptibility, and tolerance to others. The model features phase diagrams of global consensus, affective polarization, and “harmony with diversity” states. Subsequently, we proceed simulations on time-varying and static social networks, showing that intermediate parameter ranges yield a global consensus as one integrated cluster collapsing and converging toward an uncertain moderate position after long-term persistence. The feedback essentially offers a counterforce to establish an inversion between the global consensus and “harmony with diversity”. Remarkably, strengthening feedback may facilitate polarization by driving the system to first self-organize into one integrated cluster, which then gradually approaches polarization, especially for low tolerance and strong interactions, whereby the step-like dynamic behaviors of opinion entropy suggest the occurrence of a dynamic equilibrium. In summary, the aforementioned phenomena have never been unearthed before and can be regarded as unique dynamics features of affective polarization. Finally, this study attempts to offer a useful approach to the micro foundations of affective polarization for the first time, and the results guide us on how to avoid the dilemmas of this polarization.

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Section: Resultsmentioning

confidence: 99%

Section: Modelmentioning

confidence: 99%

Section: Modelmentioning

confidence: 99%

Section: Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Exploring the formation dynamics of affective polarization by considering coupled feedback

Cui

2023

Front. Phys.

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Breaking the symmetry neutralizes the extremization under the repulsion and higher order interactions

Huang,

Bian,

Han

2024

Chaos, Solitons & Fractals

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Effects of correlation between bounded confidences and node degrees on consensus in opinion dynamics on complex networks

Feng,

Zhang,

Han

2024

Int. J. Mod. Phys. C

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In bounded confidence opinion dynamics, agents only consider other agents when their opinions are sufficiently close. In many works, the confidence bound is assumed as a homogeneous one. However, agents in different situations may be with different confidence bounds in real life. In this work, the correlation of degrees and confidence bounds is introduced into modified Hegselmann–Krause model on complex networks and we mainly focus on whether the population can reach consensus, where all agents hold a same opinion. The number of opinion clusters, the relative size of the largest cluster and the probability of reaching consensus are the measures of the opinion cluster profile. The numerical simulation shows that the existence of the correlation promotes the probability of reaching consensus and even in the case where some agents are with very small confidence bounds. On scale-free and Erdös–Rényi random networks, the negative correlation supports the consensus much more than the positive correlation case. However, two correlations show the same effect on small-world networks, and this effect is also observed in scale-free and Erdös–Rényi random networks when the average degree becomes very large.

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Exploring the foundation of social diversity and coherence with a novel attraction–repulsion model framework

Cited by 5 publications

References 54 publications

Exploring the formation dynamics of affective polarization by considering coupled feedback

Exploring the formation dynamics of affective polarization by considering coupled feedback

Breaking the symmetry neutralizes the extremization under the repulsion and higher order interactions

Effects of correlation between bounded confidences and node degrees on consensus in opinion dynamics on complex networks

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