The Prompt War: How AI Decides on a Military Intervention

• URL: http://arxiv.org/abs/2507.06277v1
• Date: Tue, 08 Jul 2025 12:52:08 GMT
• Title: The Prompt War: How AI Decides on a Military Intervention
• Abstract summary: This paper proposes a model to decide on military intervention in 640 vignettes where each was run for 100 times.<n>Highest predictors of AI decision to intervene are high domestic support and high probability of success.<n>Costs such as international condemnation, military deaths, civilian deaths, and negative economic effect are statistically significant, but their effect is around half of domestic support and probability of victory.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Which factors determine AI propensity for military intervention? While the use of AI in war games and military planning is growing exponentially, the simple analysis of key drivers embedded in the models has not yet been done. This paper does a simple conjoint experiment proposing a model to decide on military intervention in 640 vignettes where each was run for 100 times allowing to explore AI decision on military intervention systematically. The analysis finds that largest predictors of AI decision to intervene are high domestic support and high probability of success. Costs such as international condemnation, military deaths, civilian deaths, and negative economic effect are statistically significant, but their effect is around half of domestic support and probability of victory. Closing window of opportunity only reaches statistical significance in interaction with other factors. The results are remarkably consistent across scenarios and across different models (OpenAI GPT, Anthropic Claude, Google Gemini) suggesting a pattern in AI decision-making.

Related papers

• Beyond Killer Robots: General AI Attitudes and Public Support for Military AI in Nine Countries [0.30586855806896046]
  General attitudes toward AI, principled opposition to lethal autonomy, or foreign-policy and geopolitical orientations shape support for military AI.<n>Across six military AI scenarios that vary in lethality and human control, respondents who view AI as beneficial are substantially more supportive of military AI.<n>Opposition to lethal autonomy is not broadly associated with the full index but is related to the application of fully autonomous lethal force.
  arXiv  Detail & Related papers  (2026-05-24T17:55:58Z)
• What is Human in Judgment? Testing Automation Bias and Algorithm Aversion Among United States Military Academy Cadets [0.688204255655161]
  We show that West Point cadets are less prone to cognitive distortion than members of the general public.<n>While the findings are limited, they suggest that military education and exposure to AI can meaningfully shape how AI influences international politics in matters of war and peace.
  arXiv  Detail & Related papers  (2026-04-06T00:57:30Z)
• AI Arms and Influence: Frontier Models Exhibit Sophisticated Reasoning in Simulated Nuclear Crises [0.0]
  We present findings from a crisis simulation in which three frontier large language models play opposing leaders in a nuclear crisis.<n>Our findings both validate and challenge central tenets of strategic theory.<n>We argue that AI simulation represents a powerful tool for strategic analysis, but only if properly calibrated against known patterns of human reasoning.
  arXiv  Detail & Related papers  (2026-02-16T13:35:01Z)
• The Hot Mess of AI: How Does Misalignment Scale With Model Intelligence and Task Complexity? [53.15349353876531]
  As AI becomes more capable, we entrust it with more general and consequential tasks.<n>We operationalize this question using a bias-variance decomposition of the errors made by AI models.<n>As more capable AIs pursue harder tasks, requiring more sequential action and thought, our results predict failures to be accompanied by more incoherent behavior.
  arXiv  Detail & Related papers  (2026-01-30T14:52:03Z)
• Superintelligence Strategy: Expert Version [64.7113737051525]
  Destabilizing AI developments could raise the odds of great-power conflict.<n>Superintelligence -- AI vastly better than humans at nearly all cognitive tasks -- is now anticipated by AI researchers.<n>We introduce the concept of Mutual Assured AI Malfunction.
  arXiv  Detail & Related papers  (2025-03-07T17:53:24Z)
• Human-Alignment Influences the Utility of AI-assisted Decision Making [16.732483972136418]
  We investigate what extent the degree of alignment actually influences the utility of AI-assisted decision making.<n>Our results show a positive association between the degree of alignment and the utility of AI-assisted decision making.
  arXiv  Detail & Related papers  (2025-01-23T19:01:47Z)
• How Performance Pressure Influences AI-Assisted Decision Making [57.53469908423318]
  We show how pressure and explainable AI (XAI) techniques interact with AI advice-taking behavior.<n>Our results show complex interaction effects, with different combinations of pressure and XAI techniques either improving or worsening AI advice taking behavior.
  arXiv  Detail & Related papers  (2024-10-21T22:39:52Z)
• Work-in-Progress: Crash Course: Can (Under Attack) Autonomous Driving Beat Human Drivers? [60.51287814584477]
  This paper evaluates the inherent risks in autonomous driving by examining the current landscape of AVs. We develop specific claims highlighting the delicate balance between the advantages of AVs and potential security challenges in real-world scenarios.
  arXiv  Detail & Related papers  (2024-05-14T09:42:21Z)
• Human vs. Machine: Behavioral Differences Between Expert Humans and Language Models in Wargame Simulations [1.6108153271585284]
  We show that large language models (LLMs) behave differently compared to humans in high-stakes military decision-making scenarios. Our results motivate policymakers to be cautious before granting autonomy or following AI-based strategy recommendations.
  arXiv  Detail & Related papers  (2024-03-06T02:23:32Z)
• Escalation Risks from Language Models in Military and Diplomatic Decision-Making [0.0]
  This work aims to scrutinize the behavior of multiple AI agents in simulated wargames. We design a novel wargame simulation and scoring framework to assess the risks of the escalation of actions taken by these agents. 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.
  arXiv  Detail & Related papers  (2024-01-07T07:59:10Z)
• Assessing Large Language Models' ability to predict how humans balance self-interest and the interest of others [0.0]
  Generative artificial intelligence (AI) holds enormous potential to revolutionize decision-making processes. By leveraging generative AI, humans can benefit from data-driven insights and predictions. However, for AI to be a reliable assistant for decision-making it is crucial that it is able to capture the balance between self-interest and the interest of others.
  arXiv  Detail & Related papers  (2023-07-21T13:23:31Z)
• Fairness in AI and Its Long-Term Implications on Society [68.8204255655161]
  We take a closer look at AI fairness and analyze how lack of AI fairness can lead to deepening of biases over time. We discuss how biased models can lead to more negative real-world outcomes for certain groups. If the issues persist, they could be reinforced by interactions with other risks and have severe implications on society in the form of social unrest.
  arXiv  Detail & Related papers  (2023-04-16T11:22:59Z)
• Cybertrust: From Explainable to Actionable and Interpretable AI (AI2) [58.981120701284816]
  Actionable and Interpretable AI (AI2) will incorporate explicit quantifications and visualizations of user confidence in AI recommendations. It will allow examining and testing of AI system predictions to establish a basis for trust in the systems' decision making.
  arXiv  Detail & Related papers  (2022-01-26T18:53:09Z)
• The human-AI relationship in decision-making: AI explanation to support people on justifying their decisions [4.169915659794568]
  People need more awareness of how AI works and its outcomes to build a relationship with that system. In decision-making scenarios, people need more awareness of how AI works and its outcomes to build a relationship with that system.
  arXiv  Detail & Related papers  (2021-02-10T14:28:34Z)
• Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making [53.62514158534574]
  We study whether features that reveal case-specific model information can calibrate trust and improve the joint performance of the human and AI. We show that confidence score can help calibrate people's trust in an AI model, but trust calibration alone is not sufficient to improve AI-assisted decision making.
  arXiv  Detail & Related papers  (2020-01-07T15:33:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.