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利性，应用新颖的策略来理解在线内容传播的潜在机制至关重要。首席研究员和她的团队通过研究一类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很好的框架，因为它们是相对简单的模型，具有惊人的丰富动态，并且它们基于社会科学的研究。该项目早期阶段的理论进展将在最后阶段提供将这些模型拟合到实际数据的策略，这是迄今为止大部分未被探索的重要发展。这项工作将有助于理解形成信息传播的机制，包括错误信息的传播。此外，该项目支持广泛的本科生参与和研究培训。本研究将从两个互补的角度进行。首先，使用基于主体的建模和平均场积分微分方程模型的组合，首席研究员和她的团队将研究外部强迫对网络的影响，其中意见状态通过同步更新的有界置信度机制演变。这允许表征稳态和分岔在相关的顺序参数。与此同时，研究小组将使用一种新的有界置信度机制来研究网络上的信息级联。本研究的目的是为信息传播的竞争、异质性和同质性研究提供见解，并创建一个清晰的框架，通过该框架将这些模型与数据进行比较。该奖项反映了美国国家科学基金会的法定使命，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。

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.