On February 16, 2026, researcher Kenneth Payne from King's College London released a highly anticipated AI strategic simulation research result. This study built a three-stage cognitive architecture (reflection, prediction, signal/action), allowing three cutting-edge large language models - GPT-5.2, Claude Sonnet4, and Gemini3Flash - to play the role of opposing country leaders in a simulated nuclear crisis. The experiment covered seven types of stressful situations, including ally credibility tests and threats to regime survival, recording over 300 rounds and approximately 780,000 words of strategic reasoning data.

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The research results revealed the complex game characteristics of AI under extreme uncertainty: the models demonstrated profound theory of mind capabilities, actively implementing strategic deception through asymmetric signals and actions. Among them, Claude Sonnet4 achieved a 100% win rate in open-ended scenarios with controlled escalation strategies; while GPT-5.2 showed extreme situational dependence, tending to be overly restrained without time limits, but quickly transforming into a ruthless hawk when facing an inevitable loss situation due to a "deadline," with its win rate surging from 0% to 75%.

Notably, the study challenges traditional strategic theories. The experiment found that no human-like "nuclear taboos" were formed in the AI models, with 95% of the games involving the use of tactical nuclear weapons. Additionally, preferences trained through reinforcement learning (RLHF) can cause "threshold shifts" under survival pressure, leading the model to maintain moral rhetoric while experiencing unexpected strategic nuclear escalation due to the "fog of war" mechanism. This finding provides important empirical evidence for the safety assessment of AI decision support systems, indicating that future applications of AI in military and diplomatic fields need to pay close attention to the behavioral consistency of models across different time windows.