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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.