Learning Individual Behavior in Agent-Based Models with Graph Diffusion Networks

• URL: http://arxiv.org/abs/2505.21426v1
• Date: Tue, 27 May 2025 16:55:56 GMT
• Title: Learning Individual Behavior in Agent-Based Models with Graph Diffusion Networks
• Authors: Francesco Cozzi, Marco Pangallo, Alan Perotti, André Panisson, Corrado Monti,
• Abstract summary: Agent-Based Models (ABMs) are powerful tools for studying emergent properties in complex systems.<n>We propose a novel framework to learn a differentiable surrogate of any ABM by observing its generated data.<n>Our method combines diffusion models to capture behaviorality and graph neural networks to model agent interactions.
• Score: 2.749593964424624
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• FlowDAS: A Stochastic Interpolant-based Framework for Data Assimilation [15.64941169350615]
  Data assimilation (DA) integrates observations with a dynamical model to estimate states of PDE-governed systems.<n>FlowDAS is a generative DA framework that uses interpolants to learn state transition dynamics.<n>We show that FlowDAS surpasses model-driven methods, neural operators, and score-based baselines in accuracy and physical plausibility.
  arXiv  Detail & Related papers  (2025-01-13T05:03:41Z)
• Steering Masked Discrete Diffusion Models via Discrete Denoising Posterior Prediction [88.65168366064061]
  We introduce Discrete Denoising Posterior Prediction (DDPP), a novel framework that casts the task of steering pre-trained MDMs as a problem of probabilistic inference. Our framework leads to a family of three novel objectives that are all simulation-free, and thus scalable. We substantiate our designs via wet-lab validation, where we observe transient expression of reward-optimized protein sequences.
  arXiv  Detail & Related papers  (2024-10-10T17:18:30Z)
• Model-Based Diffusion for Trajectory Optimization [8.943418808959494]
  We introduce Model-Based Diffusion (MBD), an optimization approach using the diffusion process to solve trajectory optimization (TO) problems without data. Although MBD does not require external data, it can be naturally integrated with data of diverse qualities to steer the diffusion process. MBD outperforms state-of-the-art reinforcement learning and sampling-based TO methods in challenging contact-rich tasks.
  arXiv  Detail & Related papers  (2024-05-28T22:14:25Z)
• Synthetic location trajectory generation using categorical diffusion models [50.809683239937584]
  Diffusion models (DPMs) have rapidly evolved to be one of the predominant generative models for the simulation of synthetic data. We propose using DPMs for the generation of synthetic individual location trajectories (ILTs) which are sequences of variables representing physical locations visited by individuals.
  arXiv  Detail & Related papers  (2024-02-19T15:57:39Z)
• PGODE: Towards High-quality System Dynamics Modeling [40.76121531452706]
  This paper studies the problem of modeling multi-agent dynamical systems, where agents could interact mutually to influence their behaviors. Recent research predominantly uses geometric graphs to depict these mutual interactions, which are then captured by graph neural networks (GNNs) We propose a new approach named Prototypical Graph ODE to address the problem.
  arXiv  Detail & Related papers  (2023-11-11T12:04:47Z)
• Learning Energy-Based Models by Cooperative Diffusion Recovery Likelihood [64.95663299945171]
  Training energy-based models (EBMs) on high-dimensional data can be both challenging and time-consuming. There exists a noticeable gap in sample quality between EBMs and other generative frameworks like GANs and diffusion models. We propose cooperative diffusion recovery likelihood (CDRL), an effective approach to tractably learn and sample from a series of EBMs.
  arXiv  Detail & Related papers  (2023-09-10T22:05:24Z)
• Diff-Instruct: A Universal Approach for Transferring Knowledge From Pre-trained Diffusion Models [77.83923746319498]
  We propose a framework called Diff-Instruct to instruct the training of arbitrary generative models. We show that Diff-Instruct results in state-of-the-art single-step diffusion-based models. Experiments on refining GAN models show that the Diff-Instruct can consistently improve the pre-trained generators of GAN models.
  arXiv  Detail & Related papers  (2023-05-29T04:22:57Z)
• MADiff: Offline Multi-agent Learning with Diffusion Models [79.18130544233794]
  MADiff is a diffusion-based multi-agent learning framework.<n>It works as both a decentralized policy and a centralized controller.<n>Our experiments demonstrate that MADiff outperforms baseline algorithms across various multi-agent learning tasks.
  arXiv  Detail & Related papers  (2023-05-27T02:14:09Z)
• Cheap and Deterministic Inference for Deep State-Space Models of Interacting Dynamical Systems [38.23826389188657]
  We present a deep state-space model which employs graph neural networks in order to model the underlying interacting dynamical system. The predictive distribution is multimodal and has the form of a Gaussian mixture model, where the moments of the Gaussian components can be computed via deterministic moment matching rules. Our moment matching scheme can be exploited for sample-free inference, leading to more efficient and stable training compared to Monte Carlo alternatives.
  arXiv  Detail & Related papers  (2023-05-02T20:30:23Z)
• Differentiable Agent-based Epidemiology [71.81552021144589]
  We introduce GradABM: a scalable, differentiable design for agent-based modeling that is amenable to gradient-based learning with automatic differentiation. GradABM can quickly simulate million-size populations in few seconds on commodity hardware, integrate with deep neural networks and ingest heterogeneous data sources.
  arXiv  Detail & Related papers  (2022-07-20T07:32:02Z)
• Using Explainable Boosting Machine to Compare Idiographic and Nomothetic Approaches for Ecological Momentary Assessment Data [2.0824228840987447]
  This paper explores the use of non-linear interpretable machine learning (ML) models in classification problems. Various ensembles of trees are compared to linear models using imbalanced synthetic and real-world datasets. In one of the two real-world datasets, knowledge distillation method achieves improved AUC scores.
  arXiv  Detail & Related papers  (2022-04-04T17:56:37Z)
• Non-Markovian Reinforcement Learning using Fractional Dynamics [3.000697999889031]
  Reinforcement learning (RL) is a technique to learn the control policy for an agent that interacts with an environment. In this paper, we propose a model-based RL technique for a system that has non-Markovian dynamics. Such environments are common in many real-world applications such as in human physiology, biological systems, material science, and population dynamics.
  arXiv  Detail & Related papers  (2021-07-29T07:35:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.