https://doi.org/10.1140/epjds/s13688-025-00616-z
Research
Towards agent-based-model informed neural networks
1
Aisot Technologies AG, R&D, Zurich, Switzerland
2
Computational Social Science, ETH Zurich, Zurich, Switzerland
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Received:
30
September
2025
Accepted:
21
December
2025
Published online:
12
January
2026
Abstract
In this article, we present a framework for designing neural networks that remain consistent with the underlying principles of agent-based models. We begin by highlighting the limitations of standard neural differential equations in modeling complex systems, where physical invariants (like energy) are often absent but other constraints (like mass conservation, information locality, bounded rationality) must be enforced. To address this, we introduce Agent-Based-Model informed Neural Networks (ABM-NNs), which leverage restricted graph neural networks and hierarchical decomposition to learn interpretable, structure-preserving dynamics. We validate the framework across three case studies of increasing complexity: (i) a Generalized Lotka–Volterra system, where we recover ground-truth parameters from short trajectories in presence of interventions; (ii) a graph-based SIR contagion model, where our method outperforms state-of-the-art graph learning baselines (GCN, GraphSAGE, Graph Transformer) in out-of-sample forecasting and noise robustness; and (iii) a real-world macroeconomic model of the ten largest economies, where we learn coupled GDP dynamics from empirical data and demonstrate counterfactual analysis for policy interventions.
Key words: Agent-based models / Neural ordinary differential equations / Graph neural networks / Complex systems / Network dynamics
Handling Editor: Anna Carbone
© The Author(s) 2026
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