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交与策略场景，理解其行为在何处及为何与人类行为产生分歧变得至关重要。尽管行为博弈论（BGT）为分析行为提供了一个框架，但现有模型并未完全捕捉人类或如LLMs这类黑箱非人类智能体的独特行为模式。我们采用前沿的程序发现工具AlphaEvolve，直接从数据中发现可解释的人类及LLM行为模型，从而实现对驱动人类与LLM行为的结构性因素进行开放式探索。在迭代剪刀石头布博弈中的分析表明，前沿LLMs能够展现出比人类更深层次的策略行为。这些结果为理解策略互动中驱动人类与LLM行为差异的结构性因素奠定了基础。