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arXiv · 2407.18108

Graph Neural Ordinary Differential Equations for Coarse-Grained Socioeconomic Dynamics

James Koch·Pranab Roy Chowdhury·Heng Wan·Parin Bhaduri·Jim Yoon·Vivek Srikrishnan·W. Brent Daniel
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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.

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James Koch, Pranab Roy Chowdhury, Heng Wan, Parin Bhaduri, Jim Yoon, Vivek Srikrishnan, W. Brent Daniel. 2024-07-25. Graph Neural Ordinary Differential Equations for Coarse-Grained Socioeconomic Dynamics. https://arxiv.org/abs/2407.18108

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