Graph Neural Ordinary Differential Equations for Coarse-Grained Socioeconomic Dynamics

Modeling Socioeconomic Systems: ABMs Socioeconomic systems exhibit intricate patterns of interaction and adaptation, e.g., between social groups and to changing environmental and economic conditions, reflecting a degree of complexity that challenges current methods for analysis and prediction of these systems [1, 2]. Modeling the complexity of these systems often requires detailed knowledge of the interacting components, their associated scales, and sufficient resolution of these details such that bottom-up emergent properties can be observed. Agent-based models (ABMs) are a computational modeling tool to resolve these facets of complex social science problems. ABMs simulate a large number of agents in a shared environment: through agent-to-agent and agent-environment interactions, ABMs relate system-wide emergent properties to individual behaviors [3]. This work was supported by the Multisector Dynamics program area of the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research as part of the multi-program, collaborative Integrated Coastal Modeling (ICoM) project at PNNL, a multi-program national laboratory operated by Battelle for the U.S. Department of Energy under contract DE-AC05-76RL01830.