AI 中文总结
研究针对能源危机下政策制定者需快速决策但经济模型整合困难的问题，利用大语言模型开发框架，协调多个异构经济模型，通过构建场景、转化输入、执行模型和合成输出，快速评估霍尔木兹海峡关闭等情况，避免单一模型假设主导结论。
AI 中文摘要
严格的经济模型构建可能需要数月时间，而能源危机要求政策制定者在数天甚至数小时内做出决策。现有现成模型通常只关注系统的有限方面，且分布在不同研究团队、编程语言、非为模型集成设计的软件架构和不兼容格式中。手动整合这些模型耗时过长。我们表明大语言模型可直接进行关键整合。该系统构建内部一致的场景，将假设转化为特定模型输入，按依赖顺序执行现有经济和物理模型，并合成适合政策制定者的输出。我们开发了一个协调16个石油、天然气、航运、水、氦、化肥和宏观经济均衡模型的大语言模型框架，并将其应用于五个场景来评估2026年霍尔木兹海峡关闭的情况，且持续八周每周更新。通过链接现有模型并将它们作为一个整体而非孤立地读取，该架构在能源和地缘政治动荡期间迅速调动分布式科学模型，同时防止任何单个模型的假设主导结论。
英文摘要
Rigorous economic models can take months to construct, yet energy crises demand decisions from policymakers within days or even hours. Any disruption in energy markets is not isolated but rapidly disseminates through interlinked global systems. Off-the-shelf models that already exist typically focus only on limited aspects of the system and are distributed across research groups, programming languages, software architectures not designed for model integration, and incompatible formats. Integrating these models manually can take longer than the crisis itself, forcing analysts to rely on whichever models are easiest to connect and leaving consequential scenarios unexplored. Policymakers must make rapid decisions with obstructed and limited information. We show that large language models can perform the critical integration directly. The system constructs internally consistent scenarios, translates assumptions into model-specific inputs, executes existing economic and physical models in dependency order, and synthesizes outputs tailored to policymakers. The language model generates no quantitative results: every reported value is reproduced directly from an underlying model run, remains traceable to its source and is subject to analyst approval at each stage. We develop a LLM framework that coordinates 16 models of oil, natural gas, shipping, water, helium, fertilizer and macroeconomic equilibrium. The framework is applied across five scenarios to assess the 2026 closure of the Strait of Hormuz and refreshed weekly for eight weeks as events on the ground continued to unfold. By linking models that already exist and reading them as a suite rather than in isolation, this architecture mobilizes distributed scientific models rapidly during energy and geopolitical disruptions while keeping any single model's assumptions from driving the conclusion.
