War and Peace (WarAgent): Large Language Model-based Multi-Agent Simulation of World Wars
On the map: LLM agents and tool use → Social simulation and multi-agent debate
Abstract (arXiv)
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 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 urlhttps://github.com/agiresearch/WarAgent.
Key facts. Cited by 145 papers in the map (data through 14 Oct 2026). Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use. Builds on 12 of its 72 references that are in the map. First posted on arXiv 28 Nov 2023.
Builds on
12 of 72 references.
Closest ancestors · 2
Same direction, newest first
- 2023Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate30 May
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Other references · 10
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Citing papers per year: 2023: 5; 2024: 66; 2025: 50; 2026: 24.
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