大语言模型作为战略行为体：地缘政治模拟中的行为对齐、风险校准与论证框架
LLMs as Strategic Actors: Behavioral Alignment, Risk Calibration, and Argumentation Framing in Geopolitical Simulations
AI总结：
本文研究了大语言模型在地缘政治模拟中的行为表现，发现其在初期能模拟人类决策，但随时间推移表现出不同的策略特征，且在论证框架上偏向规范性与合作性。
摘要：
大语言模型（LLMs）正被越来越多地提出作为战略决策环境中的智能体，但其在结构化地缘政治模拟中的行为仍缺乏深入研究。我们评估了六个流行的状态-of-the-art LLMs以及人类在四个真实世界危机模拟场景中的结果，要求模型在多个回合中选择预定义的动作并为其决策提供理由。我们比较了模型与人类在行动对齐、通过所选动作的严重性进行风险校准以及基于国际关系理论的论证框架。结果表明，模型在基础模拟回合中近似于人类决策模式，但随着时间推移却出现分歧，展现出不同的行为特征和策略更新。所有模型对所选动作的解释都表现出以稳定性、协调和风险缓解为中心的强烈规范性-合作性框架，且具有有限的对抗性推理。
英文摘要：
Large language models (LLMs) are increasingly proposed as agents in strategic decision environments, yet their behavior in structured geopolitical simulations remains under-researched. We evaluate six popular state-of-the-art LLMs alongside results from human results across four real-world crisis simulation scenarios, requiring models to select predefined actions and justify their decisions across multiple rounds. We compare models to humans in action alignment, risk calibration through chosen actions' severity, and argumentative framing grounded in international relations theory. Results show that models approximate human decision patterns in base simulation rounds but diverge over time, displaying distinct behavioural profiles and strategy updates. LLM explanations for chosen actions across all models exhibit a strong normative-cooperative framing centered on stability, coordination, and risk mitigation, with limited adversarial reasoning.
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。
