Title: SimCity: Multi-Agent Urban Development Simulation with Rich Interactions
Primary area: other topics in machine learning (i.e., none of the above)
Keywords: Macroeconomic, Social Simulation, Large Language Models, Multi-Agent System

Abstract: Large Language Models (LLMs) open new possibilities for constructing realistic and interpretable macroeconomic simulations. We present $\textbf{SimCity}$, a multi-agent framework that leverages LLMs to model an interpretable macroeconomic system with heterogeneous agents and rich interactions. Unlike classical equilibrium models that limit heterogeneity for tractability, 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) deliberate 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 and renders a mapped virtual city, 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, and the Beveridge Curve, and show that SimCity naturally reproduces these empirical patterns while remaining robust across simulation runs.