Human vs. Machine: Language Models and Wargames

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.

Escalation Risks from Language Models in Military and Diplomatic Decision-Making

Governments are increasingly considering integrating autonomous AI agents in high-stakes military and foreign-policy decision-making, especially with the emergence of advanced generative AI models like GPT-4. Our work aims to scrutinize the behavior of multiple AI agents in simulated wargames, specifically focusing on their predilection to take escalatory actions that may exacerbate multilateral conflicts. Drawing on political science and international relations literature about escalation dynamics, we design a novel wargame simulation and scoring framework to assess the escalation risks of actions taken by these agents in different scenarios. Contrary to prior studies, our research provides both qualitative and quantitative insights and focuses on large language models (LLMs). We find that all five studied off-the-shelf LLMs show forms of escalation and difficult-to-predict escalation patterns. We observe that models tend to develop arms-race dynamics, leading to greater conflict, and in rare cases, even to the deployment of nuclear weapons. Qualitatively, we also collect the models' reported reasonings for chosen actions and observe worrying justifications based on deterrence and first-strike tactics. Given the high stakes of military and foreign-policy contexts, we recommend further examination and cautious consideration before deploying autonomous language model agents for strategic military or diplomatic decision-making.