Hierarchical Multi-Agent Framework for Dynamic Macroeconomic Modeling Using Large Language Models

Large Language Models (LLMs) have demonstrated poten- tial in simulating macroeconomic systems by integrating the agent-based models. Unlike rule-based systems or neural networks with fixed learning patterns, LLM agents capture the heterogeneity of economic actors. However, existing LLM-based simulation environments are generally static, maintaining constant government policies. In this study, we introduce a hierarchical framework that incorporates LLM economic agents and an LLM planner capable of formulating policies in response to evolving economic conditions. Utilizing the proposed framework, we further examine the simulated system’s resilience to economic shocks by ana- lyzing how economic agents respond to unforeseen events and how the planner adapts to mitigate these challenges. Our results indicate that the proposed framework improves the stability of the economic system and captures more dynamic macroeconomic phenomena, offering a pre- cise and versatile simulation platform for studying real-world economic dynamics.