Opinion Dynamics on Directed Complex Networks

IF 1.4 3区 数学 Q2 MATHEMATICS, APPLIED Mathematics of Operations Research Pub Date : 2024-04-05 DOI:10.1287/moor.2022.0250
Nicolas Fraiman, Tzu-Chi Lin, Mariana Olvera-Cravioto
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Abstract

We propose and analyze a mathematical model for the evolution of opinions on directed complex networks. Our model generalizes the popular DeGroot and Friedkin-Johnsen models by allowing vertices to have attributes that may influence the opinion dynamics. We start by establishing sufficient conditions for the existence of a stationary opinion distribution on any fixed graph, and then provide an increasingly detailed characterization of its behavior by considering a sequence of directed random graphs having a local weak limit. Our most explicit results are obtained for graph sequences whose local weak limit is a marked Galton-Watson tree, in which case our model can be used to explain a variety of phenomena, for example, conditions under which consensus can be achieved, mechanisms in which opinions can become polarized, and the effect of disruptive stubborn agents on the formation of opinions.Funding: This work was supported by the National Science Foundation [Grants NSF-DMS-1929298 and CMMI-2243261].
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有向复杂网络的舆论动力学
我们提出并分析了有向复杂网络中观点演变的数学模型。我们的模型对流行的 DeGroot 和 Friedkin-Johnsen 模型进行了概括,允许顶点具有可能影响观点动态的属性。我们首先建立了在任何固定图上存在静态意见分布的充分条件,然后通过考虑具有局部弱极限的有向随机图序列,对其行为进行了越来越详细的描述。在这种情况下,我们的模型可用于解释各种现象,例如,达成共识的条件、意见两极分化的机制以及顽固分子对意见形成的破坏作用:本研究得到了美国国家科学基金会(NSF-DMS-1929298 和 CMMI-2243261)的资助。
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来源期刊
Mathematics of Operations Research
Mathematics of Operations Research 管理科学-应用数学
CiteScore
3.40
自引率
5.90%
发文量
178
审稿时长
15.0 months
期刊介绍: Mathematics of Operations Research is an international journal of the Institute for Operations Research and the Management Sciences (INFORMS). The journal invites articles concerned with the mathematical and computational foundations in the areas of continuous, discrete, and stochastic optimization; mathematical programming; dynamic programming; stochastic processes; stochastic models; simulation methodology; control and adaptation; networks; game theory; and decision theory. Also sought are contributions to learning theory and machine learning that have special relevance to decision making, operations research, and management science. The emphasis is on originality, quality, and importance; correctness alone is not sufficient. Significant developments in operations research and management science not having substantial mathematical interest should be directed to other journals such as Management Science or Operations Research.
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