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 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.

翻译：大型语言模型（LLMs）为构建真实且可解释的宏观经济模拟开辟了新的可能性。本文提出SimCity，一个利用LLMs建模具有异质智能体和丰富交互的可解释宏观经济系统的多智能体框架。与为可处理性而限制异质性的经典均衡模型，或依赖手工制定决策规则的传统基于智能体的模型（ABMs）不同，SimCity通过透明的自然语言推理实现了灵活、自适应的行为。在SimCity中，四种核心智能体类型（家庭、企业、中央银行和政府）在一个存在摩擦的劳动力市场、一个异质商品市场和一个金融市场中进行决策与互动。此外，一个视觉语言模型（VLM）决定新企业的地理位置并渲染出一个地图化的虚拟城市，使我们能够在统一环境中同时研究宏观经济规律和城市扩张动态。为了评估该框架，我们编制了一份经典宏观经济现象清单，包括需求价格弹性、恩格尔定律、奥肯定律、菲利普斯曲线和贝弗里奇曲线，并证明SimCity能够自然地复现这些经验模式，同时在多次模拟运行中保持稳健性。