Open-Ended Wargames with Large Language Models
<|reference_start|>Open-Ended Wargames with Large Language Models: 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.<|reference_end|>
@article{hogan2024open-ended,
    title={Open-Ended Wargames with Large Language Models},
    author={Daniel P. Hogan, Andrea Brennen},
    journal={arXiv preprint arXiv:2404.11446},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.11446},
    primaryClass={cs.CL cs.AI cs.CY}
}