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arXiv 2206.09490physics.soc-phcs.SImath.DSnlin.AO
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一个节点活动水平异质的观点动力学有界置信模型

A Bounded-Confidence Model of Opinion Dynamics with Heterogeneous Node-Activity Levels

Grace J. Li, Mason A. Porter

摘要:

基于主体的观点动力学模型允许研究实体之间的观点传播,并研究共识、极化和分裂等现象。通过研究社会网络上的观点动力学模型,可以探索网络结构对这些现象的影响。在社交网络中,一些人比其他人更频繁地分享他们的想法和观点。这些差异可能来自不同的社交能力,不同的活动水平,参与社交媒体平台时分享意见的不同流行程度,或其他原因。为了研究这种异质性对观点动力学的影响,我们通过引入节点权重来推广观点动力学的Deffuant-Weisbuch(DW)有界置信度模型。节点权重允许我们对具有不同交互概率的代理进行建模。使用数值模拟,我们系统地调查(使用各种网络结构和节点权重分布)的节点权重,我们均匀随机分配给节点的影响。我们表明,引入异构节点权重的结果在更长的收敛时间和更多的意见碎片比在基线DW模型。在我们的网络中的节点权重允许一个考虑各种社会学的情况下,代理人与其他代理人交互的异构概率。

英文摘要:

Agent-based models of opinion dynamics allow one to examine the spread of opinions between entities and to study phenomena such as consensus, polarization, and fragmentation. By studying a model of opinion dynamics on a social network, one can explore the effects of network structure on these phenomena. In social networks, some individuals share their ideas and opinions more frequently than others. These disparities can arise from heterogeneous sociabilities, heterogeneous activity levels, different prevalences to share opinions when engaging in a social-media platform, or something else. To examine the impact of such heterogeneities on opinion dynamics, we generalize the Deffuant--Weisbuch (DW) bounded-confidence model (BCM) of opinion dynamics by incorporating node weights. The node weights allow us to model agents with different probabilities of interacting. Using numerical simulations, we systematically investigate (using a variety of network structures and node-weight distributions) the effects of node weights, which we assign uniformly at random to the nodes. We demonstrate that introducing heterogeneous node weights results in longer convergence times and more opinion fragmentation than in a baseline DW model. The node weights in our BCM allow one to consider a variety of sociological scenarios in which agents have heterogeneous probabilities of interacting with other agents.

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