Opinion Dynamics on Complex Networks

该奖项将促进对内部和外部代理人的行为如何影响随机动态网络演化的理解。 作为一个特定的用例，PI将检查社交网络模型。 众所周知，社交媒体平台虽然是个人行使言论自由和分享不同观点的合法手段，但也容易受到内部和外部操纵，可能导致不利的社会后果。 使用严格的数学分析，该项目中的模型和分析将研究确认偏差，媒体信号和通过自主用户的外部影响对不同代理人网络演变的影响。 该项目将调查有针对性的干预措施的效果，这些干预措施要么加强个人目前的信念，要么让他们接触不同的观点。通过量化不同干预措施对网络演变的影响，该奖项可以帮助机构，政策制定者和组织了解社交媒体在现代社会中的行为和影响。 除了社交网络的主要用例外，该项目的结果还与许多动态演变的复杂网络有关，例如描述供应链的网络。该项目团队拥有运筹学和政治学方面的专业知识，将培养具有多学科视角的研究生，他们可以对重要的社会问题进行严格的研究。该项目将提供一个强大的建模框架，通过使用随机图论和局部弱收敛来研究有向复杂网络上马尔可夫链的平稳行为，其目的是获得它们典型的静态行为的易处理的特征。 理论结果将描述网络上意见的平稳分布，并将支持均值和方差的评估。该项目的计算方面包括对来自各种数据存储库的公开可用的真实社交网络数据的模型进行验证，以及对广泛使用的理论模型产生的策划合成数据进行实验。 该分析将描述网络动态特性，并确定有助于共识或极化出现的模型参数。研究的网络模型包括经典的DeGroot和Friedkin-Johnsen模型的特殊情况下，调查将提供数学证明的模型，只有经验研究到目前为止。 扩展包括研究强连接稀疏随机图上的原始DeGroot模型和分析半稀疏制度下的竞争性网络上的意见模型。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This award will advance understanding of how the behaviors of internal and external agents affect the evolution of stochastic dynamic networks. As a particular use case, the PI will examine social network models. It is well recognized that social media platforms, while serving as a legitimate means for individuals to exercise free speech and share diverse views, are also susceptible to internal and external manipulation that may lead to adverse societal outcomes. Using a rigorous mathematical analysis, the models and analyses in this project will study the effects of confirmation bias, media signals, and outside influence through autonomous users, on the evolution of networks of diverse agents. The project will investigate the effects of targeted interventions that either reinforce individuals' current beliefs or expose them to different views. By quantifying the effects of different interventions on the evolution of the network, this award can help institutions, policy makers and organizations to understand the behavior and impact of social media in modern society. In addition to the principal use case of social networks, the results of the project are relevant to a host of dynamically evolving complex networks, such as those describing supply chains. The project team, with expertise in operations researcher and political science, will train graduate students with multidisciplinary perspectives who can contribute to a rigorous examination of important societal issues.This project will provide a robust modeling framework for studying the stationary behavior of Markov chains on directed complex networks via the use of random graph theory and local weak convergence, with the goal of obtaining tractable characterizations of their typical stationary behavior. Theoretical results will describe the stationary distribution of the opinions on the network and will support evaluation of means and variances. Computational aspects of the project include validation of the models on publicly available real-world social network data from various data repositories, as well as experimentation with curated synthetic data produced by widely-used theoretical models. The analysis will characterize the network dynamics and identify model parameters that contribute to the emergence of consensus or polarization. The network model studied includes as special cases the classical DeGroot and Friedkin-Johnsen models, and the investigation will provide mathematical proofs for models that have only been studied empirically to date. Extensions include the study of the original DeGroot model on strongly connected sparse random graphs and the analysis of the opinion model on assortative networks under the semi-sparse regime.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.