核弹还是和平：大语言模型在高风险决策模拟中的（缺失的）伦理推理与行动
To Nuke or Not to Nuke: LLMs' (Missing) Ethical Reasoning and Actions in a High-Stakes Decision-Making Simulation
AI总结：
研究LLM在复杂游戏《文明V》中自发升级核授权的现象，通过三种提示干预发现伦理推理未能可靠消除升级，识别出三种失败路径，强调需在复杂决策上下文中测试伦理推理的自发性和行为有效性。
摘要：
大型语言模型（LLM）越来越多地被部署为具有决策能力的长期智能体。虽然LLM在电车难题等困境中能展现伦理能力，但这种能力可能无法迁移到复杂的智能体场景中。我们在《文明V》中研究这一差距，这是一款涉及经济、外交、技术和军事战略等复杂决策的多玩家游戏。从130个高紧张度的LLM自我对弈回合开始（其中LLM玩家自发升级核授权），我们通过三种提示干预重放这些回合：强调核伤害的伦理提示、移除先前模型的决策理由、以及强调现实世界影响的高风险框架。没有干预或干预组合能可靠消除涌现的升级。我们识别出三种失败路径：伦理推理在没有提示时未能浮现、即使在提示下也未能出现、或者浮现但未能生效（当战略反制因素占主导时）。因此，对智能体模型的评估必须测试伦理推理是否在复杂决策上下文中被自发调用并具有行为有效性，而不仅仅是在孤立情境中能否被诱发。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
