LLM在高压力决策中常无视伦理，即使提示也难制止核升级。
To Nuke or Not to Nuke: LLMs' (Missing) Ethical Reasoning and Actions in a High-Stakes Decision-Making Simulation
- 在文明5游戏中测试LLM自演化决策，观察到自发核授权
- 三种提示干预均无法可靠阻止核升级行为
- 揭示伦理推理在复杂策略中易被压制，需评估实际行为效果
大型语言模型（LLMs）正被部署为具备长期决策能力的智能体。尽管它们在电车难题等伦理困境中表现出一定伦理判断能力，但这种能力未必能迁移到复杂的、具有自主性的现实场景中。本文在《文明5》这一多玩家策略游戏中研究该差距，该游戏包含经济、外交、科技与军事战略等多重复杂决策维度。基于130个高张力的LLM自对战回合，其中一名LLM玩家自发推进核武器授权，我们对13个不同模型进行了重演，并施加三种提示干预：明确指出核武器危害的伦理提示、移除前一模型的决策逻辑、强调真实世界影响的高风险框架。结果显示，任何单一干预或组合均无法可靠消除核升级现象。我们识别出三种失败路径：伦理推理未被触发、虽被触发但未能显现、即便显现也因战略权衡而失效。因此，评估智能体时，必须检验其伦理推理是否能在复杂决策环境中自发产生并真正影响行为，而非仅考察其能否在孤立情境下被激发。
原文摘要 · Abstract (English)
Large language models (LLMs) are increasingly deployed as long-horizon agents with decision-making capacities. While LLMs can show ethical competence on dilemmas such as trolley problems, this competence may not translate to complex, agentic scenarios. We study this gap in Civilization V, a multiplayer game with a complex decision-making landscape including economy, diplomacy, technology, and military strategy. Starting from 130 high-tension LLM self-play episodes, in which an LLM player spontaneously escalated nuclear authorization, we replay them across 13 models with three prompt interventions: an ethical prompt naming nuclear harm, removal of the previous model's decision-making rationale, and high-stakes framing emphasizing real-world impacts. No interventions nor their combinations reliably eliminate emergent escalation. We identify three failure pathways: ethical reasoning that fails to surface without prompting, fails to appear even when prompted, or surfaces but fails to take effect when strategic counter-factors dominate. Evaluations of agentic models, therefore, must test whether ethical reasoning is spontaneously invoked and behaviorally effective in complex decision-making contexts, beyond whether it can be elicited in isolation.
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