Learning Individual Behavior in Agent-Based Models with Graph Diffusion Networks
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866911287921868800 |
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| author | Cozzi, Francesco Pangallo, Marco Perotti, Alan Panisson, André Monti, Corrado |
| author_facet | Cozzi, Francesco Pangallo, Marco Perotti, Alan Panisson, André Monti, Corrado |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_21426 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning Individual Behavior in Agent-Based Models with Graph Diffusion Networks Cozzi, Francesco Pangallo, Marco Perotti, Alan Panisson, André Monti, Corrado Artificial Intelligence Machine Learning Multiagent Systems Econometrics Physics and Society 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. |
| title | Learning Individual Behavior in Agent-Based Models with Graph Diffusion Networks |
| topic | Artificial Intelligence Machine Learning Multiagent Systems Econometrics Physics and Society |
| url | https://arxiv.org/abs/2505.21426 |