AI Economist Agent: An Agentic Framework for Model-Grounded Economic Analysis with RAG, Knowledge Graphs, and Large Language Models
arXiv:2606.20041v1 Announce Type: cross Abstract: 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
The rapid advancement and integration of large language models and knowledge graphs are enabling increasingly sophisticated applications in complex domains like economics.
Sophisticated AI agents capable of economic analysis grounded in theory and data represent a significant step towards automating and augmenting high-level white-collar work, impacting decision-making in finance and policy.
Economic analysis can now be potentially augmented or even partially automated by agentic AI systems that synthesize theoretical frameworks with real-world data, moving beyond simple narrative generation to grounded claims.
- · Financial institutions and hedge funds adopting AI economist agents
- · Economic modeling and simulation platforms
- · Data and knowledge graph providers
- · Traditional economic forecasting services relying on manual analysis
- · Entry-level economic analysts
- · Consulting firms unprepared for AI augmentation
AI economist agents provide faster and more consistent economic scenario analysis, improving real-time decision-making for businesses and governments.
The development and deployment of these agents could lead to new forms of economic policy experimentation and algorithmic governance, with profound implications for market stability and regulation.
A fully agentic economic system could autonomously identify and mitigate systemic risks, but also potentially create new, opaque forms of 'algorithmic black swans' that are difficult to predict or control.
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Read at arXiv cs.LG