An adaptive bounded-confidence model of opinion dynamics on networks
An adaptive bounded-confidence model of opinion dynamics on networks
复制标题
网络舆论动态的自适应有限置信模型
DOI：
10.1093/comnet/cnac055
复制
发表时间：
2023
影响因子：
2.1
通讯作者：
Porter, Mason A.
中科院分区：
文献类型：
--
作者：
Kan, Unchitta;Feng, Michelle;Porter, Mason A.
在社交网络中相互作用的个体经常交换想法并影响彼此的意见。研究网络上观点传播的一种流行方法是检查有界置信度模型（Bounded-Confidence Model，BCM），其中网络的节点具有编码其观点的连续值状态，并且当它们位于自己观点的置信度范围内时，可以接受其他节点的观点。在这篇文章中，我们扩展了Deffuant-Weisbuch（DW）模型，这是一个著名的模型，通过研究与网络结构共同进化的观点的传播。我们提出了一个自适应的DW模型，其中网络的节点可以（1）改变他们的意见，当他们与相邻节点和（2）打破与邻居的意见容忍阈值的基础上连接，然后形成新的连接后，同质性的原则。这个意见容忍度阈值确定相邻节点的意见是否足够不同，以被视为“不和谐”。使用数值模拟，我们发现，我们的自适应DW模型需要一个更大的置信区间比基线DW模型的网络节点，以实现共识的意见。在参数空间的一个区域中，我们观察到“伪共识”稳定状态，其中存在意见簇的多个子簇，其意见彼此之间存在少量差异。在我们的模拟中，我们还研究了早期动态和节点的作用，最初温和的意见，以实现共识。此外，我们探讨了协同进化对收敛时间的影响。
Individuals who interact with each other in social networks often exchange ideas and influence each other's opinions. A popular approach to study the spread of opinions on networks is by examining bounded-confidence models (BCMs), in which the nodes of a network have continuous-valued states that encode their opinions and are receptive to other nodes’ opinions when they lie within some confidence bound of their own opinion. In this article, we extend the Deffuant–Weisbuch (DW) model, which is a well-known BCM, by examining the spread of opinions that coevolve with network structure. We propose an adaptive variant of the DW model in which the nodes of a network can (1) alter their opinions when they interact with neighbouring nodes and (2) break connections with neighbours based on an opinion tolerance threshold and then form new connections following the principle of homophily. This opinion tolerance threshold determines whether or not the opinions of adjacent nodes are sufficiently different to be viewed as ‘discordant’. Using numerical simulations, we find that our adaptive DW model requires a larger confidence bound than a baseline DW model for the nodes of a network to achieve a consensus opinion. In one region of parameter space, we observe ‘pseudo-consensus’ steady states, in which there exist multiple subclusters of an opinion cluster with opinions that differ from each other by a small amount. In our simulations, we also examine the roles of early-time dynamics and nodes with initially moderate opinions for achieving consensus. Additionally, we explore the effects of coevolution on the convergence time of our BCM.