Sandia National Labs Academic Programs Collaboration Report

Group dynamics and emergent recursive multiscale interaction

CONTRIBUTOR Spotlight

Asmeret Naugle is a Sandia computational social scientist with expertise in a variety of modeling and analysis methods. Her focus on national security applications and topics such as political dynamics, migration, cyber security, and energy systems give her ideal background for the emergent recursive multiscale interaction LDRD project.

Understanding group emergence can improve the ability to anticipate and understand how group dynamics can be influenced in national security applications. Particularly impactful is the new theory on the multiscale, fractal-like nature of the emergence of groups, including recursive interactions between scales, that resulted from this 3-year LDRD project led by PI Asmeret Naugle. The project team also developed new physics-inspired theory on multiscale group dynamics, considering the vibration of locations of people within a social network over time, and tested and validated the theory on real world data. The team utilized a two-year dataset from Game-X, a large MMORPG (Massively Multiplayer Online Role-Playing Game) in which players work together, fight, trade and form guilds to accomplish game goals. Importantly, there is no set “win” condition in this open ended game. A second dataset used for comparison was from the Correlates of War, a collection of real-world data on the interactions between nations on a global scale over the last ~200 years. Given the importance of the global COVID-19 pandemic, the team also decided to shift some of its attention toward building a model of the influence of group dynamics on information flow and compliance with COVID-19 stay-at-home orders. Since this project was focused on behavior, the team developed a model of the interplay between information flow, behavior, and disease dynamics. This model was able to reproduce the wave pattern, seen in case counts over time, using negative feedback passing through news sources to simulated human agents and then to the contact rate of an SEIR (Susceptible Exposed-Infected-Recovered) model. A key question the model is designed to address is how communication policy can influence long-term trust in information sources and, ultimately, in public health messaging by government. This model is thus relevant for studying

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Academic Programs