Graph Neural Ordinary Differential Equations for Coarse-Grained Socioeconomic Dynamics

Abstract

We present a data-driven machine-learning approach for modeling space-time socioeconomic dynamics. Through coarse-graining fine-scale observations, our modeling framework simplifies these complex systems to a set of tractable mechanistic relationships – in the form of ordinary differential equations – while preserving critical system behaviors. This approach allows for expedited ‘what if’ studies and sensitivity analyses, essential for informed policy-making. Our findings, from a case study of Baltimore, MD, indicate that this machine learning-augmented coarse-grained model serves as a powerful instrument for deciphering the complex interactions between social factors, geography, and exogenous stressors, offering a valuable asset for system forecasting and resilience planning.

Citation

@INPROCEEDINGS{Koch2025-lk,
  title = {{Graph neural differential equations for coarse-grained socioeconomic
  dynamics}},
  author = {Koch, James and Chowdhury, Pranab K Roy and Wan, Heng and Bhaduri,
  Parin and Yoon, Jim and Srikrishnan, Vivek and Daniel, W Brent},
  editor = {Yang, Zining and von Briesen, Elizabeth},
  booktitle = {{Proceedings of the 2024 International Conference of The
  Computational Social Science Society of the Americas}},
  publisher = {Springer Nature Switzerland},
  location = {Cham},
  eventtitle = {CSSSA 2024},
  venue = {Santa Fe, New Mexico, USA},
  pages = {237--256},
  date = {2025},
  doi = {10.1007/978-3-031-89692-7_15},
  series = {Springer Proceedings in Complexity}
}