用推理提示建模大模型博弈中的有限理性行为
Reasoning and Behavioral Equilibria in LLM-Nash Games: From Mindsets to Actions
- 以推理提示为策略空间构建博弈框架，显式建模思考过程
- 均衡结果可能偏离传统纳什解，体现认知约束影响
- 适合研究大模型决策机制与思维模式的交互演化
我们提出LLM-Nash框架，一种基于博弈论的模型，其中代理通过选择推理提示来引导大型语言模型（LLMs）的决策。不同于假设完全理性的效用最大化代理的经典博弈，该框架通过显式建模推理过程来捕捉有限理性。均衡定义在提示空间上，行动则作为LLM推理的行为输出。该方法支持对认知约束、思维模式表达力及认识论学习的研究。通过示例表明，推理均衡可偏离经典纳什结果，为大模型驱动系统中的战略互动提供了新基础。
原文摘要 · Abstract (English)
We introduce the LLM-Nash framework, a game-theoretic model where agents select reasoning prompts to guide decision-making via Large Language Models (LLMs). Unlike classical games that assume utility-maximizing agents with full rationality, this framework captures bounded rationality by modeling the reasoning process explicitly. Equilibrium is defined over the prompt space, with actions emerging as the behavioral output of LLM inference. This approach enables the study of cognitive constraints, mindset expressiveness, and epistemic learning. Through illustrative examples, we show how reasoning equilibria can diverge from classical Nash outcomes, offering a new foundation for strategic interaction in LLM-enabled systems.
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