Open-Ended Wargames with Large Language Models
Daniel P. Hogan Andrea Brennen
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.