{"ID":2832985,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.04771","arxiv_id":"2512.04771","title":"Complementary Characterization of Agent-Based Models via Computational Mechanics and Diffusion Models","abstract":"This article extends the preprint \"Characterizing Agent-Based Model Dynamics via $ε$-Machines and Kolmogorov-Style Complexity\" by introducing diffusion models as orthogonal and complementary tools for characterizing the output of agent-based models (ABMs). Where $ε$-machines capture the predictive temporal structure and intrinsic computation of ABM-generated time series, diffusion models characterize high-dimensional cross-sectional distributions, learn underlying data manifolds, and enable synthetic generation of plausible population-level outcomes. We provide a formal analysis demonstrating that the two approaches operate on distinct mathematical domains -- processes vs. distributions -- and show that their combination yields a two-axis representation of ABM behavior based on temporal organization and distributional geometry. To our knowledge, this is the first framework to integrate computational mechanics with score-based generative modeling for the structural analysis of ABM outputs, thereby situating ABM characterization within the broader landscape of modern machine-learning methods for density estimation and intrinsic computation. The framework is validated using the same elder-caregiver ABM dataset introduced in the companion paper, and we provide precise definitions and propositions formalizing the mathematical complementarity between $ε$-machines and diffusion models. This establishes a principled methodology for jointly analyzing temporal predictability and high-dimensional distributional structure in complex simulation models.","short_abstract":"This article extends the preprint \"Characterizing Agent-Based Model Dynamics via $ε$-Machines and Kolmogorov-Style Complexity\" by introducing diffusion models as orthogonal and complementary tools for characterizing the output of agent-based models (ABMs). Where $ε$-machines capture the predictive temporal structure an...","url_abs":"https://arxiv.org/abs/2512.04771","url_pdf":"https://arxiv.org/pdf/2512.04771v1","authors":"[\"Roberto Garrone\"]","published":"2025-12-04T13:14:49Z","proceeding":"cs.MA","tasks":"[\"cs.MA\",\"cs.LG\"]","methods":"[\"Diffusion Model\",\"Generative Adversarial Network\"]","has_code":false}