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

TL;DR

This paper presents a comprehensive, adaptive multi-agent modeling framework that combines information-theoretic diagnostics, causal models, and data-driven priors to enhance explainability and contestability in policy design.

Contribution

It introduces a novel, domain-neutral architecture integrating multiple dynamic regimes, diagnostics, and causal semantics for analyzing adaptive multi-agent systems.

Findings

Framework enables systematic comparison of stability and performance.

Provides tools for analyzing non-stationary, oscillatory, and drifting dynamics.

Offers a structured methodology for explainable decision processes.

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.