用AI代理框架让经济报告既有逻辑又可追溯。
AI Economist Agent: An Agentic Framework for Model-Grounded Economic Analysis with RAG, Knowledge Graphs, and Large Language Models
- 构建基于RAG与知识图谱的AI经济代理，结合模型计算生成报告。
- 在通胀与银行压力测试场景中，报告更符合经济理论且可追踪证据来源。
- 适合政策分析、金融风控等需要严谨推理的领域使用。
我们提出一种基于RAG的模型驱动型AI经济学家框架，利用大型语言模型（LLMs）和知识图谱实现经济情景分析。尽管LLMs能生成流畅的经济叙述，但经济学家需基于经济理论和真实数据做出可信判断。本研究设计的AI经济学家代理通过知识图谱整合经济数据与理论，由智能体规划分析流程、检索相关证据、选择合适模型并生成报告。报告内容不直接依赖语言模型的推断，而是基于显式模型计算，并通过AI代理与检索到的证据建立关联。我们在两个应用中评估该框架：美国通胀持续性与美联储政策的经济报告生成，以及美国商业房地产再融资压力测试的叙事生成。结果表明，基于模型的报告在经济连贯性和可追溯性方面显著提升。
原文摘要 · Abstract (English)
We propose a model-grounded RAG-based AI economist with an agentic framework for economic scenario analysis using large language models (LLMs) and knowledge graphs. While LLMs can generate fluent economic narratives, economists are often required to make economic claims grounded by economic theory and real-world data. Based on this motivation, this study proposes an RAG-based AI economist, which utilizes knowledge graphs including economic data and theory and LLM-based agents to plan the analysis, retrieve relevant evidence, select appropriate models, and generate reports. In our framework, we do not produce quantitative claims directly with the language model alone; instead, we generate narratives grounded in explicit model-based computations and linked to the retrieved evidence via AI agents. We refer to our framework as an AI economist agent. We evaluate the AI economist agent in two applications: economist report generation for U.S. inflation persistence and Federal Reserve policy, and bank stress-test narrative generation for U.S. commercial real estate refinancing stress. The results illustrate how grounding the generated reports improves their economic coherence and traceability.
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