AI经济学家代理：一种基于模型的经济分析代理框架，结合RAG、知识图谱和大语言模型
AI Economist Agent: An Agentic Framework for Evidence-Based Economic and Financial Analysis with RAG, Knowledge Graphs, and Large Language Models
AI总结：
提出一种基于RAG的AI经济学家代理框架，利用知识图谱和大语言模型进行经济情景分析，通过代理规划、检索证据、选择模型并生成报告，提高经济叙事的连贯性和可追溯性。
摘要：
我们提出了一种基于模型的RAG型AI经济学家，具有用于经济情景分析的代理框架，使用大语言模型（LLMs）和知识图谱。虽然LLMs可以生成流畅的经济叙事，但经济学家通常需要做出基于经济理论和现实数据的经济主张。基于这一动机，本研究提出了一种基于RAG的AI经济学家，它利用包含经济数据和理论的知识图谱以及基于LLM的代理来规划分析、检索相关证据、选择合适的模型并生成报告。在我们的框架中，我们不直接仅使用语言模型产生定量主张；相反，我们生成基于显式模型计算的叙事，并通过AI代理与检索到的证据相关联。我们将我们的框架称为AI经济学家代理。我们在两个应用中评估了AI经济学家代理：为美国通胀持续性和美联储政策生成经济学家报告，以及为美国商业房地产再融资压力生成银行压力测试叙事。结果说明了如何通过基于生成报告来提高其经济连贯性和可追溯性。
英文摘要：
We propose an AI economist agent for economic and financial scenario analysis. Scenario design often requires analysts to assess emerging risks with limited historical precedent, combine information from many sources, and translate qualitative mechanisms into internally consistent quantitative paths. Large language models (LLMs) can search and synthesize this information, but fluent narratives alone do not establish the model-based calculations needed for economic conclusions. Our framework uses LLM agents to plan the analysis, retrieve relevant evidence, and organize economic mechanisms, while registered quantitative models generate numerical outcomes and predefined tests determine whether intermediate results can be used in the final report. We apply the framework to European macro-financial stress scenarios and bank capital analysis. The empirical analysis evaluates retrieval of economic mechanisms, scenario construction, model execution, and report generation under a historical information cutoff. The results show how the AI economist agent can combine flexible evidence retrieval and scenario construction while keeping the resulting analysis linked to identifiable sources and explicit model calculations.
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。
