Sociohydrodynamics: Data-driven modeling of social behavior

Area of Science:

• Complex Systems
• Sociophysics
• Computational Social Science

Background:

• Living systems exhibit complex behaviors influenced by physical forces and decision-making.
• Hydrodynamic theories offer simplified descriptions of collective behaviors but often lack data integration.
• Existing models for social dynamics are frequently disconnected from empirical data.

Purpose of the Study:

• To develop a data-driven pipeline linking individual movement (micromotives) to collective behavior (macrobehavior).
• To construct and apply a sociohydrodynamic model to understand residential dynamics in the United States.
• To systematically assess hydrodynamic assumptions using real-world data.

Main Methods:

• Augmenting hydrodynamic theories with individual preferences to guide motion.
• Utilizing a data-driven pipeline integrating census data, sociological surveys, and neural network analysis.
• Employing statistical inference to calibrate a minimal sociohydrodynamic model.

Main Results:

• The calibrated model qualitatively captures key features of US residential dynamics at the county level.
• A social memory effect, analogous to magnetic hysteresis, emerges during segregation-integration transitions.
• The model provides a physics-based analogy for neighborhood tipping, explaining rapid demographic shifts.

Conclusions:

• Sociohydrodynamic models can effectively describe complex social phenomena like residential segregation.
• The concept of emergent social memory offers new insights into collective behavior dynamics.
• This framework facilitates the study of decision-guided motility across various systems, from microorganisms to human populations.