用大模型模拟博弈，揭示冲突与合作的深层逻辑。
Multi-Agent Strategic Games with LLMs
- 将大模型置于重复安全困境中，测试其战略行为
- 多极化增加冲突，有限时间导致普遍崩溃，沟通减少对抗
- 可追踪推理过程，适合研究国际关系理论
本文探讨大语言模型（LLMs）能否用于研究冲突与合作的战略基础。将LLMs作为实验主体置于重复安全困境中，评估其是否重现国际关系理论中的经典机制。游戏在三个理论核心维度上扩展：多极格局、有限时间跨度和沟通能力。多个模型的结果呈现系统性一致模式：多极化提升冲突概率，有限时间导致普遍解体，符合逆向归纳逻辑；沟通则通过信号传递与互惠降低冲突。除行为表现外，该设计还能获取代理的私有推理与公开对话，使决策与预判、不确定性下的合作、信任建立等战略逻辑直接关联。研究贡献主要在于方法论：基于LLM的实验提供了一种可扩展、透明且可复现的探查理论机制的新路径。
原文摘要 · Abstract (English)
This paper asks whether large language models (LLMs) can be used to study the strategic foundations of conflict and cooperation. I introduce LLMs as experimental subjects in a repeated security dilemma and evaluate whether they reproduce canonical mechanisms from international relations theory. The baseline game is extended along three theoretically central dimensions: multipolarity, finite time horizons, and the availability of communication. Across multiple models, the results exhibit systematic and consistent patterns: multipolarity increases the likelihood of conflict, finite horizons induce universal unraveling consistent with backward-induction logic, and communication reduces conflict by enabling signaling and reciprocity. Beyond observed behavior, the design provides access to agents' private reasoning and public messages, allowing choices to be linked to underlying strategic logics such as preemption, cooperation under uncertainty, and trust-building. The contribution is primarily methodological. LLM-based experiments offer a scalable, transparent, and replicable approach to probing theoretical mechanisms.
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