让神经网络遵循群体模型规律，提升可解释性与预测能力
Towards agent-based-model informed neural networks
- 用受限图网络和分层分解构建结构保持的神经网络
- 在三个案例中均实现参数还原、预测精度与抗噪能力提升
- 适合需要可解释建模的复杂系统研究者使用
本文提出一种神经网络设计框架，使其与基于主体模型（ABM）的基本原理保持一致。标准神经微分方程在建模复杂系统时往往忽略物理守恒量（如能量），却需强制满足质量守恒、信息局部性、有限理性等约束。为此，我们引入受ABM启发的神经网络（ABM-NN），结合受限图神经网络与分层分解，学习可解释且结构保持的动力学。在三个递增复杂度的案例中验证：(i) 广义广义洛特卡-沃尔泰拉系统，从短轨迹中恢复真实参数并处理干预；(ii) 基于图的SIR传播模型，优于GCN、GraphSAGE、Graph Transformer等基线，在外样本预测与抗噪性上表现更优；(iii) 十大经济体真实宏观经济模型，从实证数据学习耦合的GDP动态，并支持政策干预的反事实分析。
原文摘要 · Abstract (English)
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 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
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