在策略遊戲中，人類期望LLM對手展現出理性與合作的行為。

Humans expect rationality and cooperation from LLM opponents in strategic games

May 16, 2025

作者: Darija Barak, Miguel Costa-Gomes

cs.AI

摘要

隨著大型語言模型（LLMs）融入我們的社會與經濟互動中，我們需要深入理解人類在策略性情境下如何應對LLMs的對手。我們首次進行了一項受控且有金錢激勵的實驗室研究，探討人類在多玩家p-選美競賽中對抗其他玩家與LLMs時的行為差異。我們採用受試者內設計，以便在個體層面上比較行為。我們發現，在此環境下，人類受試者在對抗LLMs時選擇的數字顯著低於對抗人類時，這主要是由於「零」納什均衡選擇的普遍性增加所致。這一轉變主要由具有高策略推理能力的受試者驅動。選擇零納什均衡策略的受試者，其動機源自於對LLMs推理能力的感知，以及出乎意料地，對其合作傾向的考量。我們的研究結果為多人類-LLM在同步選擇遊戲中的互動提供了基礎性洞察，揭示了受試者行為的異質性以及他們對LLMs遊戲策略的信念，並對混合人類-LLM系統中的機制設計提出了重要啟示。

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.