An Adaptive, Data-Integrated Agent-Based Modeling Framework for Explainable and Contestable Policy Design
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| Format: | Preprint |
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2025
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| _version_ | 1866911284466810880 |
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| author | Garrone, Roberto |
| author_facet | Garrone, Roberto |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_19726 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An Adaptive, Data-Integrated Agent-Based Modeling Framework for Explainable and Contestable Policy Design Garrone, Roberto Multiagent Systems Artificial Intelligence Machine Learning Systems and Control 90C90 I.6.3; I.2.11; J.4 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. |
| title | An Adaptive, Data-Integrated Agent-Based Modeling Framework for Explainable and Contestable Policy Design |
| topic | Multiagent Systems Artificial Intelligence Machine Learning Systems and Control 90C90 I.6.3; I.2.11; J.4 |
| url | https://arxiv.org/abs/2511.19726 |