多元意见形成:超图上的自适应选民模型

IF 2.2 4区 物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY Annalen der Physik Pub Date : 2024-03-27 DOI:10.1002/andp.202300342
Anastasia Golovin, Jan Mölter, Christian Kuehn
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引用次数: 0

摘要

自适应选民模型被广泛用于社会复杂网络中的舆论动态建模。然而,现有的自适应选民模型仅限于成对互动,无法捕捉群体中错综复杂的社会动态。本文将自适应选民模型扩展到超图中,探讨同伴压力如何影响集体决策。该模型由两个过程组成:个人可以咨询群体并改变自己的意见,也可以离开群体并加入另一个群体。这两个过程之间的相互作用产生了两个阶段的动态变化。在初始阶段,超图的拓扑结构迅速达到一个新的稳定状态。在随后的阶段,舆论动态在新的拓扑结构上展开,这取决于舆论传播的机制。如果群体总是追随多数,网络就会迅速趋同于支离破碎的社区。相反,如果个体根据其在群体中的代表比例选择一种意见,系统就会在较长时间内保持稳定状态。随机模拟和以超图矩为基础的分析均值场描述都支持这些结果,并在配对层面上实现了矩闭合。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Polyadic Opinion Formation: The Adaptive Voter Model on a Hypergraph

The adaptive voter model is widely used to model opinion dynamics in social complex networks. However, existing adaptive voter models are limited to only pairwise interactions and fail to capture the intricate social dynamics that arises in groups. This paper extends the adaptive voter model to hypergraphs to explore how forces of peer pressure influence collective decision-making. The model consists of two processes: individuals can either consult the group and change their opinion or leave the group and join a different one. The interplay between those two processes gives rise to a two-phase dynamics. In the initial phase, the topology of the hypergraph quickly reaches a new stable state. In the subsequent phase, opinion dynamics plays out on the new topology depending on the mechanism by which opinions spread. If the group always follows the majority, the network rapidly converges to fragmented communities. In contrast, if individuals choose an opinion proportionally to its representation in the group, the system remains in a metastable state for an extended period of time. The results are supported both by stochastic simulations and an analytical mean-field description in terms of hypergraph moments with a moment closure at the pair level.

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来源期刊
Annalen der Physik
Annalen der Physik 物理-物理:综合
CiteScore
4.50
自引率
8.30%
发文量
202
审稿时长
3 months
期刊介绍: Annalen der Physik (AdP) is one of the world''s most renowned physics journals with an over 225 years'' tradition of excellence. Based on the fame of seminal papers by Einstein, Planck and many others, the journal is now tuned towards today''s most exciting findings including the annual Nobel Lectures. AdP comprises all areas of physics, with particular emphasis on important, significant and highly relevant results. Topics range from fundamental research to forefront applications including dynamic and interdisciplinary fields. The journal covers theory, simulation and experiment, e.g., but not exclusively, in condensed matter, quantum physics, photonics, materials physics, high energy, gravitation and astrophysics. It welcomes Rapid Research Letters, Original Papers, Review and Feature Articles.
期刊最新文献
(Ann. Phys. 11/2024) (Ann. Phys. 11/2024) Masthead: Ann. Phys. 11/2024 (Ann. Phys. 10/2024) Masthead: Ann. Phys. 10/2024
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