提出可自适应的多智能体建模框架，让政策设计更透明可辩驳。
An Adaptive, Data-Integrated Agent-Based Modeling Framework for Explainable and Contestable Policy Design
- 融合动态规则与自适应参数，支持学习型智能体与可变控制
- 用熵率等指标评估系统可预测性与结构复杂度，识别行为模式
- 适合政策制定者和研究人员，用于可解释、可争议的仿真决策
多智能体系统常面临反馈、适应与非平稳性，但多数模拟研究仍采用静态决策规则和固定控制参数。本文提出一种通用的自适应多智能体学习框架，包含：(i) 四种动态范式，区分静态/自适应智能体与固定/自适应系统参数；(ii) 信息论诊断工具（熵率、统计复杂度、预测信息）以评估可预测性与结构特征；(iii) 结构因果模型实现明确干预语义；(iv) 从总体或样本数据生成智能体先验的程序；(v) 无监督方法识别涌现行为模式。该框架提供领域无关的架构，分析学习智能体与自适应控制如何共同影响系统轨迹，支持在非均衡、振荡或漂移动态下对稳定性、性能与可解释性的系统比较。文中给出数学定义、计算算子及实验设计模板，构建可解释、可争议的多智能体决策流程的结构化方法。
原文摘要 · Abstract (English)
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.
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