人类在博弈中更信任大模型的理性与合作，行为更趋保守。
Humans expect rationality and cooperation from LLM opponents in strategic games
- 对比人类与大模型对手，采用控制实验研究个体策略差异。
- 面对大模型时，人们选择更低数字，零策略占比显著上升。
- 高策略思维者更倾向配合大模型，认为其理性且具合作性。
随着大型语言模型（LLMs）融入社会与经济互动，理解人类在战略情境下对大模型对手的行为反应至关重要。本文首次开展受控的、具有货币激励的实验室实验，比较多人参与的p-美丽竞赛中人类与大模型对手的表现。采用被试内设计，在个体层面进行行为对比。结果表明，在该环境中，人类玩家面对大模型时选择的数值显著低于面对人类时，主要由‘零’纳什均衡选择的频率上升驱动。这一变化主要由具备高策略推理能力的参与者推动。选择零策略的参与者表示其决策基于对大模型推理能力的预期，以及意外发现的大模型合作倾向。研究揭示了多玩家人-大模型交互中的异质性行为与信念差异，为混合人-大模型系统的设计提供了重要启示。
原文摘要 · Abstract (English)
As Large Language Models (LLMs) integrate into our social and economic interactions, we need to deepen our understanding of how humans respond to LLMs opponents in strategic settings. We present the results of the first controlled monetarily-incentivised laboratory experiment looking at differences in human behaviour in a multi-player p-beauty contest against other humans and LLMs. We use a within-subject design in order to compare behaviour at the individual level. We show that, in this environment, human subjects choose significantly lower numbers when playing against LLMs than humans, which is mainly driven by the increased prevalence of `zero' Nash-equilibrium choices. This shift is mainly driven by subjects with high strategic reasoning ability. Subjects who play the zero Nash-equilibrium choice motivate their strategy by appealing to perceived LLM's reasoning ability and, unexpectedly, propensity towards cooperation. Our findings provide foundational insights into the multi-player human-LLM interaction in simultaneous choice games, uncover heterogeneities in both subjects' behaviour and beliefs about LLM's play when playing against them, and suggest important implications for mechanism design in mixed human-LLM systems.
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