We propose a two-level information-theoretic framework for characterizing the informational organization of Agent-Based Model (ABM) dynamics within the broader paradigm of Complex Adaptive Systems (CAS). At the macro level, a pooled $\varepsilon$-machine is reconstructed as a reference model summarizing the system-wide informational regime. At the micro level, $\varepsilon$-machines are reconstructed for each caregiver--elder dyad and variable, complemented by algorithm-agnostic Kolmogorov-style measures, including normalized LZ78 complexity and bits per symbol from lossless compression. The resulting feature set, $\{h_μ, C_μ, E, \mathrm{LZ78}, \mathrm{bps}\}$, enables distributional analysis, stratified comparisons, and unsupervised clustering across agents and scenarios. Empirical results show that coupling $\varepsilon$-machines with compression diagnostics yields a coherent picture of where predictive information resides in the caregiving ABM. Global reconstructions provide a memoryless baseline ($L{=}0$ under coarse symbolizations), whereas per-dyad models reveal localized structure, particularly for walkability under ordinal encodings ($m{=}3$). Compression metrics corroborate these patterns: dictionary compressors agree on algorithmic redundancy, while normalized LZ78 captures statistical novelty. Socioeconomic variables display cross-sectional heterogeneity and near-memoryless dynamics, whereas spatial interaction induces bounded temporal memory and recurrent regimes. The framework thus distinguishes semantic organization (predictive causation and memory) from syntactic simplicity (description length) and clarifies how emergence manifests at different system layers. It is demonstrated on a caregiver--elder case study with dyad-level $\varepsilon$-machine reconstructions and compression-based diagnostics.

翻译：本文提出一个双层信息论框架，用于在复杂适应系统（CAS）的宏观范式下刻画多主体模型（ABM）动力学的信息组织模式。在宏观层面，通过重构聚合ε-机作为参考模型，以概括系统整体的信息机制；在微观层面，为每个照护者-老人二元组及变量分别重构ε-机，并辅以与算法无关的柯尔莫哥洛夫式度量——包括归一化LZ78复杂度与无损压缩的每符号比特数。所得特征集$\{h_μ, C_μ, E, \mathrm{LZ78}, \mathrm{bps}\}$支持跨主体与跨情景的分布分析、分层比较和无监督聚类。实证结果表明，将ε-机与压缩诊断相结合，能够清晰呈现照护ABM中预测信息的分布特征：全局重构模型在粗粒度符号化下呈现无记忆基线（$L{=}0$），而按二元组构建的模型则揭示局部结构特征，尤其在有序编码（$m{=}3$）下的步行性变量中表现显著。压缩度量验证了这些模式：字典压缩器一致反映算法冗余，而归一化LZ78则捕捉统计新颖性。社会经济变量呈现横截面异质性与近无记忆动力学，而空间交互则诱发有界时间记忆与循环机制。该框架由此区分了语义组织（预测因果与记忆）与句法简洁性（描述长度），并阐明了涌现现象在不同系统层级的表现形式。本文通过一个照护者-老人案例研究，展示了基于二元组ε-机重构与压缩诊断的分析过程。