混沌中的性格演变：一种结合大语言模型的框架用于在环境压力下的迭代囚徒困境中发现角色
Evolving Personalities in Chaos: An LLM-Augmented Framework for Character Discovery in the Iterated Prisoners Dilemma under Environmental Stress
AI总结：
本文提出一种结合大语言模型的框架，在环境压力下通过迭代囚徒困境发现可解释的角色原型，提升策略的鲁棒性和可解释性。
摘要：
标准的迭代囚徒困境（IPD）模拟在确定性和无噪声的环境中运行，产生出可能在理论上最优但面对现实世界不确定性时容易崩溃的策略。本文解决了进化博弈论研究中的两个关键缺口：（1）在策略进化过程中缺乏现实的环境压力因素，以及（2）解释性缺口，即进化出的遗传策略仍然是无意义的二进制序列。我们引入了一种新颖的框架，结合随机环境扰动（God Mode）和基于大语言模型（LLM）的行为分析，将进化出的基因型转化为可解释的角色原型。我们的实验表明，在混沌条件下进化出的策略表现出更强的韧性，并呈现出不同的行为表型，从贪婪资本家到外交执行者。这些表型可以通过LLM轻松分类，但通过手动基因组检查几乎无法解释。这项工作将进化计算与可解释AI相结合，并为多智能体系统中的自动智能体特征化提供了模板。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
