As Large Language Models (LLMs) are increasingly deployed in social and strategic scenarios, it becomes critical to understand where and why their behavior diverges from that of humans. While behavioral game theory (BGT) provides a framework for analyzing behavior, existing models do not fully capture the idiosyncratic behavior of humans or black-box, non-human agents like LLMs. We employ AlphaEvolve, a cutting-edge program discovery tool, to directly discover interpretable models of human and LLM behavior from data, thereby enabling open-ended discovery of structural factors driving human and LLM behavior. Our analysis on iterated rock-paper-scissors reveals that frontier LLMs can be capable of deeper strategic behavior than humans. These results provide a foundation for understanding structural differences driving differences in human and LLM behavior in strategic interactions.

翻译：随着大型语言模型（LLMs）越来越多地应用于社会与战略场景，理解其行为与人类行为的差异所在及原因变得至关重要。尽管行为博弈理论（BGT）为行为分析提供了框架，但现有模型无法完全捕捉人类或类LLM的黑箱非人类智能体的独特性行为。我们采用前沿程序发现工具AlphaEvolve，直接从数据中发现可解释的人类与LLM行为模型，从而实现对驱动人类与LLM行为结构性因素的开放性探索。基于迭代式石头剪刀布的分析表明，前沿LLMs能够展现出比人类更深刻的战略行为。这些结果为理解人类与LLM在战略互动中行为差异的结构性动因奠定了基础。