Advances in Bounded-Confidence Models on Networks

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.