Graph Neural Ordinary Differential Equations for Coarse-Grained Socioeconomic Dynamics

TL;DR

This paper introduces a machine learning framework that models complex socioeconomic space-time dynamics through coarse-grained ordinary differential equations, enabling efficient analysis and policy planning.

Contribution

It presents a novel data-driven approach that simplifies complex socioeconomic systems into tractable ODE models while maintaining key behaviors.

Findings

Effective modeling of Baltimore's socioeconomic dynamics

The coarse-grained model captures social, geographic, and stressor interactions

Enables rapid 'what if' analyses for policy and resilience planning

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.