# An Adaptive, Data-Integrated Agent-Based Modeling Framework for Explainable and Contestable Policy Design

arXiv: https://arxiv.org/abs/2511.19726 · HTML: https://mlsanity.com/paper/2511.19726/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 81 references that are in the map.
- First posted on arXiv 24 Nov 2025.

## Builds on (most cited first, 6 of 6)

- [Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms](https://mlsanity.com/paper/1911.10635/) (2019)
- [The Complexity of Decentralized Control of Markov Decision Processes](https://mlsanity.com/paper/1301.3836/) (2013)
- [Reinforcement Learning in Economics and Finance](https://mlsanity.com/paper/2003.10014/) (2020)
- [Characterizing Agent-Based Model Dynamics via ε-Machines and Kolmogorov-Style Complexity](https://mlsanity.com/paper/2510.12729/) (2025)
- [Modelling Social Care Provision in An Agent-Based Framework with Kinship Networks](https://mlsanity.com/paper/1904.05267/) (2019)
- [Patients, Primary Care, and Policy: Simulation Modeling for Health Care Decision Support](https://mlsanity.com/paper/1910.11027/) (2019)

## Built on by (most cited first, 3 of 3)

- [Machine-Coached Policy Revision in Adaptive Agent-Based Regulatory Simulation: A Controller-Level Contestability Layer](https://mlsanity.com/paper/2606.20700/) (2026)
- [Structural Distinguishability of Static and Adaptive Policy Regimes in Agent-Based Regulatory Simulation](https://mlsanity.com/paper/2606.20699/) (2026)
- [Verification of Adaptive Agentic Controllers through Finite Rule Revision](https://mlsanity.com/paper/2607.09770/) (2026)

## Abstract (arXiv)

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.