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.
Cite
@article{arxiv.2511.19726,
title = {An Adaptive, Data-Integrated Agent-Based Modeling Framework for Explainable and Contestable Policy Design},
author = {Roberto Garrone},
journal= {arXiv preprint arXiv:2511.19726},
year = {2025}
}
Comments
27 pages, 2 case studies (emissions and smart grids). Preprint prepared during the author's PhD research at the Open University of Cyprus and the University of Milano-Bicocca. Introduces a unified framework for adaptive multi-agent learning with information-theoretic, causal, and clustering diagnostics