{"ID":2837818,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.19726","arxiv_id":"2511.19726","title":"An Adaptive, Data-Integrated Agent-Based Modeling Framework for Explainable and Contestable Policy Design","abstract":"Multi-agent systems often operate under feedback, adaptation, and non-stationarity, yet many simulation studies retain static decision rules and fixed control parameters. This paper introduces a general adaptive multi-agent learning framework that integrates: (i) four dynamic regimes distinguishing static versus adaptive agents and fixed versus adaptive system parameters; (ii) information-theoretic diagnostics (entropy rate, statistical complexity, and predictive information) to assess predictability and structure; (iii) structural causal models for explicit intervention semantics; (iv) procedures for generating agent-level priors from aggregate or sample data; and (v) unsupervised methods for identifying emergent behavioral regimes. The framework offers a domain-neutral architecture for analyzing how learning agents and adaptive controls jointly shape system trajectories, enabling systematic comparison of stability, performance, and interpretability across non-equilibrium, oscillatory, or drifting dynamics. Mathematical definitions, computational operators, and an experimental design template are provided, yielding a structured methodology for developing explainable and contestable multi-agent decision processes.","short_abstract":"Multi-agent systems often operate under feedback, adaptation, and non-stationarity, yet many simulation studies retain static decision rules and fixed control parameters. This paper introduces a general adaptive multi-agent learning framework that integrates: (i) four dynamic regimes distinguishing static versus adapti...","url_abs":"https://arxiv.org/abs/2511.19726","url_pdf":"https://arxiv.org/pdf/2511.19726v1","authors":"[\"Roberto Garrone\"]","published":"2025-11-24T21:41:45Z","proceeding":"cs.MA","tasks":"[\"cs.MA\",\"cs.AI\",\"cs.LG\",\"eess.SY\"]","methods":"[]","has_code":false}