Can we avoid wars at the crossroads of history? This question has been pursued by individuals, scholars, policymakers, and organizations throughout human history. In this research, we attempt to answer the question based on the recent advances of Artificial Intelligence (AI) and Large Language Models (LLMs). We propose \textbf{WarAgent}, an LLM-powered multi-agent AI system, to simulate the participating countries, their decisions, and the consequences, in historical international conflicts, including the World War I (WWI), the World War II (WWII), and the Warring States Period (WSP) in Ancient China. By evaluating the simulation effectiveness, we examine the advancements and limitations of cutting-edge AI systems' abilities in studying complex collective human behaviors such as international conflicts under diverse settings. In these simulations, the emergent interactions among agents also offer a novel perspective for examining the triggers and conditions that lead to war. Our findings offer data-driven and AI-augmented insights that can redefine how we approach conflict resolution and peacekeeping strategies. The implications stretch beyond historical analysis, offering a blueprint for using AI to understand human history and possibly prevent future international conflicts. Code and data are available at \url{https://github.com/agiresearch/WarAgent}.

翻译：在历史的关键节点，我们能否避免战争？这一直是人类历史中个人、学者、政策制定者和组织共同追寻的问题。本研究基于人工智能与大型语言模型的最新进展，尝试回答该问题。我们提出**WarAgent**——一个由大型语言模型驱动的多智能体AI系统，用于模拟历史国际冲突中参与国的决策及其后果，包括第一次世界大战、第二次世界大战以及中国古代的战国时期。通过评估模拟效果，我们探讨了尖端AI系统在复杂集体人类行为（如不同情境下的国际冲突）研究中的进展与局限性。在这些模拟中，智能体间涌现的交互行为也为审视引发战争的诱因和条件提供了全新视角。我们的发现提供了数据驱动和AI增强的见解，可重新定义冲突解决与和平维持策略的制定方式。其意义超越历史分析，为利用AI理解人类历史并可能预防未来国际冲突提供了蓝图。代码和数据见\url{https://github.com/agiresearch/WarAgent}。