Standard simulations of the Iterated Prisoners Dilemma (IPD) operate in deterministic, noise-free environments, producing strategies that may be theoretically optimal but fragile when confronted with real-world uncertainty. This paper addresses two critical gaps in evolutionary game theory research: (1) the absence of realistic environmental stressors during strategy evolution, and (2) the Interpretability Gap, where evolved genetic strategies remain opaque binary sequences devoid of semantic meaning. We introduce a novel framework combining stochastic environmental perturbations (God Mode) with Large Language Model (LLM)-based behavioral profiling to transform evolved genotypes into interpretable character archetypes. Our experiments demonstrate that strategies evolved under chaotic conditions exhibit superior resilience and present distinct behavioral phenotypes, ranging from Ruthless Capitalists to Diplomatic Enforcers. These phenotypes are readily classified by LLMs but remain nearly impossible to interpret through manual genome inspection alone. This work bridges evolutionary computation with explainable AI and provides a template for automated agent characterization in multi-agent systems.

翻译：标准的迭代囚徒困境（IPD）模拟在确定、无噪声的环境中进行，所产生的策略可能在理论上最优，但在面对现实世界的不确定性时显得脆弱。本文解决了进化博弈论研究中的两个关键空白：（1）策略演化过程中缺乏真实的环境压力因素；（2）可解释性鸿沟，即演化出的遗传策略仍是不透明的、缺乏语义的二进制序列。我们引入了一个新颖的框架，该框架将随机环境扰动（上帝模式）与基于大语言模型（LLM）的行为分析相结合，从而将演化出的基因型转化为可解释的角色原型。我们的实验表明，在混沌条件下演化出的策略展现出更强的韧性，并呈现出从“冷酷资本家”到“外交执法者”等不同的行为表型。这些表型易于被LLMs分类，但仅通过手动检查基因组几乎无法解释。这项工作将进化计算与可解释人工智能连接起来，并为多智能体系统中的自动智能体特征描述提供了一个模板。