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arXiv 2402.03354physics.soc-phcs.SI
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利用不确定性研究具有异质性的集体舆论动力学

Leveraging Uncertainty in Collective Opinion Dynamics with Heterogeneity

Vito Mengers, Mohsen Raoufi, Oliver Brock, Heiko Hamann, Pawel Romanczuk

AI总结:

本文研究集体舆论动力学中先验信息质量和网络中心性两种异质性,通过引入不确定性作为额外维度,提出自适应加权机制,提高共识准确性和速度,并揭示异质性与信息的双向关系。

摘要:

自然和人工集体在不同维度上表现出异质性,这增加了其行为的复杂性。我们研究了两种此类异质性对集体舆论动力学的影响:智能体先验信息质量的异质性以及网络中心性(即直接邻居数量)的异质性。为了研究这些异质性,我们不仅在模型中考虑了它们,提出了一种具有异质性中心性的新型网络生成器,而且还引入了不确定性作为额外维度。通过量化每个智能体的不确定性,我们为智能体提供了一种自适应地权衡个人信息与社会信息的机制。由于不确定性根据智能体之间的相互作用而发展,它们捕获了关于异质性的信息。因此,不确定性是研究复杂集体舆论动力学的一个相关额外可观测变量,我们利用它来展示异质性中心性与信息之间的双向关系。此外,我们证明了由不确定性驱动的自适应加权能够提高共识的准确性和速度,尤其是在异质性条件下,并为避免不确定性建模中导致性能下降的错误提供了指导。这些提高性能和可观测性的机会表明,不确定性对于研究自然异质系统和设计人工异质系统都具有重要意义。

英文摘要:

Natural and artificial collectives exhibit heterogeneities across different dimensions, contributing to the complexity of their behavior. We investigate the effect of two such heterogeneities on collective opinion dynamics: heterogeneity of the quality of agents' prior information and of centrality in the network, i.e., the number of immediate neighbors. To study these heterogeneities, we not only consider them in our model, proposing a novel network generator with heterogeneous centrality, but also introduce uncertainty as an additional dimension. By quantifying the uncertainty of each agent, we provide a mechanism for agents to adaptively weigh their individual against social information. As uncertainties develop according to the interactions between agents, they capture information on heterogeneities. Therefore, uncertainty is a relevant additional observable in the study of complex collective opinion dynamics that we use to show the bidirectional relationship of heterogeneous centrality and information. Furthermore, we demonstrate that uncertainty-driven adaptive weighting leads to increased accuracy and speed of consensus, especially under heterogeneity, and provide guidelines for avoiding performance-decreasing errors in uncertainty modeling. These opportunities for improved performance and observability suggest the importance of uncertainty both for the study of natural and the design of artificial heterogeneous systems.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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