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

TL;DR

The paper presents a domain-neutral framework for adaptive multi-agent systems that jointly analyzes agent learning and policy adaptation across four regimes: CPCA, CPVA, VPCA, and VPVA. It combines belief-driven behavior, a declarative policy-causal layer, and diagnostics from structural causal models and information theory to enable explainable and contestable policy design. Synthetic populations, structured environments, and survey-informed priors initialize agent heterogeneity, while seven diagnostic tools—including $h_\mu$, $C_\mu$, and $E$—characterize emergent dynamics and regime transitions. Through two case studies on emissions regulation and adaptive load balancing in electric grids, the framework demonstrates transferability across policy and infrastructure domains and provides concrete methods for evaluating stability, robustness, and interpretability under non-convergent and oscillatory dynamics.

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.