Learning Individual Behavior in Agent-Based Models with Graph Diffusion Networks
This addresses the challenge of integrating ABMs with real-world data for researchers in complex systems, representing a fundamental shift rather than an incremental improvement.
The authors tackled the problem of non-differentiable rules in Agent-Based Models (ABMs) limiting gradient-based optimization by proposing a novel framework that learns a differentiable surrogate using diffusion models and graph neural networks, which replicates individual-level patterns and accurately forecasts emergent dynamics in validation on Schelling's segregation model and a Predator-Prey ecosystem.
Agent-Based Models (ABMs) are powerful tools for studying emergent properties in complex systems. In ABMs, agent behaviors are governed by local interactions and stochastic rules. However, these rules are, in general, non-differentiable, limiting the use of gradient-based methods for optimization, and thus integration with real-world data. We propose a novel framework to learn a differentiable surrogate of any ABM by observing its generated data. Our method combines diffusion models to capture behavioral stochasticity and graph neural networks to model agent interactions. Distinct from prior surrogate approaches, our method introduces a fundamental shift: rather than approximating system-level outputs, it models individual agent behavior directly, preserving the decentralized, bottom-up dynamics that define ABMs. We validate our approach on two ABMs (Schelling's segregation model and a Predator-Prey ecosystem) showing that it replicates individual-level patterns and accurately forecasts emergent dynamics beyond training. Our results demonstrate the potential of combining diffusion models and graph learning for data-driven ABM simulation.