Graph Neural Ordinary Differential Equations for Coarse-Grained Socioeconomic Dynamics
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866914887173668864 |
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| author | Koch, James Chowdhury, Pranab Roy Wan, Heng Bhaduri, Parin Yoon, Jim Srikrishnan, Vivek Daniel, W. Brent |
| author_facet | Koch, James Chowdhury, Pranab Roy Wan, Heng Bhaduri, Parin Yoon, Jim Srikrishnan, Vivek Daniel, W. Brent |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_18108 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Graph Neural Ordinary Differential Equations for Coarse-Grained Socioeconomic Dynamics Koch, James Chowdhury, Pranab Roy Wan, Heng Bhaduri, Parin Yoon, Jim Srikrishnan, Vivek Daniel, W. Brent Machine Learning Computers and Society Social and Information Networks Physics and Society 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. |
| title | Graph Neural Ordinary Differential Equations for Coarse-Grained Socioeconomic Dynamics |
| topic | Machine Learning Computers and Society Social and Information Networks Physics and Society |
| url | https://arxiv.org/abs/2407.18108 |