Open-Ended Wargames with Large Language Models

• URL: http://arxiv.org/abs/2404.11446v1
• Date: Wed, 17 Apr 2024 14:54:58 GMT
• Title: Open-Ended Wargames with Large Language Models
• Authors: Daniel P. Hogan, Andrea Brennen,
• Abstract summary: We introduce "Snow Globe," an LLM-powered multi-agent system for playing qualitative wargames. We describe its software architecture conceptually and release an open-source implementation alongside this publication. We discuss potential applications of the approach and how it fits into the broader wargaming ecosystem.
• Score: 3.2228025627337864
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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