SimCity: Multi-Agent Urban Development Simulation with Rich Interactions

Large Language Models (LLMs) open new possibilities for constructing realistic and interpretable macroeconomic simulations. We present SimCity, a multi-agent framework that leverages LLMs to model an interpretable macroeconomic system with heterogeneous agents and rich interactions. Unlike classical equilibrium models with limited heterogeneity, or traditional agent-based models (ABMs) that rely on hand-crafted decision rules, SimCity enables flexible, adaptive behavior with transparent natural-language reasoning. Within SimCity, four core agent types (households, firms, a central bank, and a government) make decisions and participate in a frictional labor market, a heterogeneous goods market, and a financial market. Furthermore, a Vision–Language Model (VLM) determines the geographic placement of new firms based on a virtual city map, allowing us to study both macroeconomic regularities and urban expansion dynamics within a unified environment. To evaluate the framework, we compile a checklist of canonical macroeconomic phenomena, including price elasticity of demand, Engel’s Law, Okun’s Law, the Phillips Curve, etc., and show that SimCity effectively exhibits these canonical patterns. Using our framework, we study novel economic shocks that are difficult to analyze in existing models and examine their macroeconomic implications qualitatively, yielding coherent results.