🤖 AI Summary
Multi-agent systems (MAS) dynamically evolve under feedback, adaptation, and non-stationarity, yet lack a general adaptive modeling framework. Method: We propose an interpretable, learning-control-integrated MAS modeling framework that unifies multi-agent reinforcement learning, entropy rate and statistical complexity analysis, predictive information metrics, structural causal models (SCMs), and clustering-based emergent behavior identification; it partitions system dynamics into four regimes—stationary, oscillatory, drifting, and chaotic. Contribution/Results: This work is the first to embed information-theoretic diagnostics and causal inference directly into the adaptive learning pipeline, enabling prior-free agent initialization, unsupervised behavioral pattern discovery, and falsifiable policy evaluation. The framework provides formal definitions, computable operators, and standardized experimental templates, facilitating systematic cross-environment comparison across stability, performance, and interpretability in non-stationary settings.
📝 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.