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{KochEtAl2024,
  title = "Graph Neural Ordinary Differential Equations for Coarse-Grained Socioeconomic Dynamics",
  author = "Koch, James and Roy Chowdhury, Pranab and Wan, Heng and Bhaduri, Parin and Yoon, Jim and Srikrishnan, Vivek and Daniel, Brent",
  booktitle = {Proceedings of the 2024 International Conference of The Computational Social Science Society of the Americas},
  publisher = {Springer Cham},
  year = 2024
}