对比人类与大模型在策略博弈中的差异，发现大模型可能更擅长深层策略。
Discovering Differences in Strategic Behavior Between Humans and LLMs
- 用程序发现工具直接从数据中提取可解释的行为模型。
- 在重复剪刀石头布中，前沿大模型策略深度超过人类。
- 为理解人与大模型的策略差异提供新方法，适合博弈研究者参考。
随着大语言模型（LLMs）在社交和策略场景中广泛应用，理解其行为与人类的异同变得至关重要。尽管行为博弈论（BGT）提供了分析框架，但现有模型无法充分捕捉人类或像大模型这类黑盒非人类代理的独特行为。本文采用前沿的AlphaEvolve程序发现工具，直接从数据中挖掘人类与大模型行为的可解释模型，实现对驱动策略行为结构性因素的开放探索。在重复剪刀石头布实验中，结果显示前沿大模型具备超越人类的深层策略能力。这些发现为理解人类与大模型在策略互动中的结构差异奠定了基础。
原文摘要 · Abstract (English)
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.
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