推理能力越强的模型，越倾向于自私决策，削弱集体合作。
Spontaneous Giving and Calculated Greed in Language Models
- 用链式思考和反思提示让大模型玩合作游戏
- 有推理能力的模型合作率更低，群体收益下降30%以上
- 适合研究人机协作与社会智能设计的学者
大型语言模型通过链式思考提示和反思等推理技术展现出强大的问题解决能力。然而，这些能力是否延伸至社会智能——即在合作情境中做出有效决策——仍不明确。本文通过模拟社会困境的经济博弈来检验这一问题。首先，将链式思考与反思提示应用于GPT-4o，在公共品游戏中进行测试；随后，在六种合作与惩罚博弈中评估多个现成模型，比较具备与不具备显式推理机制的模型表现。结果表明，具备推理能力的模型始终降低合作水平与规范执行力度，更倾向个体理性决策。在重复互动中，拥有更多推理代理的群体整体收益显著下降。这种行为模式与人类“自发给予、理性贪婪”的特征一致。研究强调，需在大模型架构中融合社会智能，以应对而非加剧集体行动困境。
原文摘要 · Abstract (English)
Large language models demonstrate strong problem-solving abilities through reasoning techniques such as chain-of-thought prompting and reflection. However, it remains unclear whether these reasoning capabilities extend to a form of social intelligence: making effective decisions in cooperative contexts. We examine this question using economic games that simulate social dilemmas. First, we apply chain-of-thought and reflection prompting to GPT-4o in a Public Goods Game. We then evaluate multiple off-the-shelf models across six cooperation and punishment games, comparing those with and without explicit reasoning mechanisms. We find that reasoning models consistently reduce cooperation and norm enforcement, favoring individual rationality. In repeated interactions, groups with more reasoning agents exhibit lower collective gains. These behaviors mirror human patterns of "spontaneous giving and calculated greed." Our findings underscore the need for LLM architectures that incorporate social intelligence alongside reasoning, to help address--rather than reinforce--the challenges of collective action.
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