Wargames are a powerful tool for understanding and rehearsing real-world
decision making. Automated play of wargames using artificial intelligence (AI)
enables possibilities beyond those of human-conducted games, such as playing
the game many times over to see a range of possible outcomes. There are two
categories of wargames: quantitative games, with discrete types of moves, and
qualitative games, which revolve around open-ended responses. Historically,
automation efforts have focused on quantitative games, but large language
models (LLMs) make it possible to automate qualitative wargames. We introduce
"Snow Globe," an LLM-powered multi-agent system for playing qualitative
wargames. With Snow Globe, every stage of a text-based qualitative wargame from
scenario preparation to post-game analysis can be optionally carried out by AI,
humans, or a combination thereof. We describe its software architecture
conceptually and release an open-source implementation alongside this
publication. As case studies, we simulate a tabletop exercise about an AI
incident response and a political wargame about a geopolitical crisis. We
discuss potential applications of the approach and how it fits into the broader
wargaming ecosystem.

通过大语言模型，我们引入了 “Snow Globe”，一个为玩质性战争游戏提供支持的多智能体系统，可以自动化进行从情景准备到游戏分析的各个阶段，以实现定量战争游戏自动化的潜力。

利用大型语言模型进行开放式战争游戏

Open-Ended Wargames with Large Language Models

Wargames have a long history in the development of military strategy and the
response of nations to threats or attacks. The advent of artificial
intelligence (AI) promises better decision-making and increased military
effectiveness. However, there is still debate about how AI systems, especially
large language models (LLMs), behave as compared to humans. To this end, we use
a wargame experiment with 107 national security expert human players designed
to look at crisis escalation in a fictional US-China scenario and compare human
players to LLM-simulated responses. We find considerable agreement in the LLM
and human responses but also significant quantitative and qualitative
differences between simulated and human players in the wargame, motivating
caution to policymakers before handing over autonomy or following AI-based
strategy recommendations.

战争游戏在军事战略发展和国家对威胁或攻击的反应中有着悠久的历史。人工智能的出现在决策制定和军事效力提升方面承诺着更好的结果。然而，关于人工智能系统，特别是大型语言模型与人类的行为差异仍存在争议。为此，我们使用了一个涉及 107 位国家安全专家人类参与者的战争游戏实验，旨在研究虚构的中美危机升级情景中的人类参与者与模拟大型语言模型回应之间的比较。我们发现，模拟大型语言模型与人类回应之间存在显著的定量和定性差异，这提醒决策制定者在授权自主权或遵循基于人工智能的策略建议之前要谨慎。