Advances in Bounded-Confidence Models on Networks

由于社交媒体的广泛使用和分享内容的便捷性，应用新的策略来了解在线内容传播的基本机制至关重要。首席研究员和她的团队通过研究一类被称为有界置信度模型的意见动态数学模型来解决这个问题。这些模型提供了一个极好的框架，因为它们是相对简单的模型，具有令人惊讶的丰富的动力学，而且它们以社会科学的研究为基础。该项目早期阶段的理论进展将在最后阶段提供使这些模型适应真实数据的策略，这是一项重要的发展，到目前为止基本上还没有被探索过。这项工作将有助于理解影响信息传播的机制，包括错误信息的传播。此外，该项目支持本科生的广泛参与和研究培训。这项研究将从两个互补的角度进行。首先，使用基于代理的建模和平均场积分-微分方程模型的组合，主要研究人员和她的团队将研究外部强迫对网络的影响，在网络中，观点状态通过同步更新的有界信任机制演变。这允许在相关的序参数中描述定态和分叉。同时，研究小组将使用一种关于有界信任机制的新机制来研究网络上的信息级联。这项研究的目标是深入了解竞争、异质性和同质性对信息传播的研究，并创建一个清晰的框架，用于将这些模型与数据进行比较。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Due to the broad usage of social media and the ease of sharing content, it is crucial to apply novel strategies to understand the underlying mechanisms of online content dissemination. The principal investigator and her team tackle this problem by studying a class of mathematical models of opinion dynamics called bounded-confidence models. These models provide an excellent framework because they are relatively simple models with surprisingly rich dynamics, and they are grounded in research from social science. The theoretical advances in the early stages of this project will in the final stages provide a strategy for fitting these models to real data, which is an important development that has so far gone largely unexplored. This work will contribute to the understanding of the mechanisms that shape information dissemination, including the spread of misinformation. In addition, this project supports extensive undergraduate student involvement and research training.This research will be pursued from two complementary perspectives. First, using a combination of agent-based modeling and mean-field integro-differential equation models, the principal investigator and her team will study the effects of external forcing on networks where opinion states evolve via a synchronous-updating bounded-confidence mechanism. This allows for the characterization of the stationary states and bifurcations in the relevant order parameters. In parallel, the research team will study information cascades on networks using a novel twist on bounded-confidence mechanisms. The goal of this research is to provide insight into the study of competition, heterogeneity, and homophily on information dissemination and create a clear framework by which to compare these models with data.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.