Sociohydrodynamics: data-driven modelling of social behavior

Community behavior arises from the decisions of each individual in the population. While physics provides tools to model large-scale behavior, incorporating individual preferences and decision-making remains an open problem. We used data-driven analysis to adapt hydrodynamic approaches to capture social behavior and examined a case study of residential dynamics in US Census data. Our model predicted an emergent societal memory at the transition between integration and segregation which slows community change.

Collaboration with Daniel Seara, Michel Fruchart, Yael Avni, David Martin, and Vincenzo Vitelli (UChicago).