# SimCity: Multi-Agent Urban Development Simulation with Rich Interactions

arXiv: https://arxiv.org/abs/2510.01297 · HTML: https://mlsanity.com/paper/2510.01297/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use.
- Builds on 13 of its 159 references that are in the map.
- First posted on arXiv 1 Oct 2025.

## Placed in

- LLM agents and tool use / [Social simulation and multi-agent debate](https://mlsanity.com/agents/social-simulation-and-multi-agent-debate/)

## Builds on (most cited first, 13 of 13)

- [Large Language Models Empowered Agent-based Modeling and Simulation: A Survey and Perspectives](https://mlsanity.com/paper/2312.11970/) (2023)
- [From Persona to Personalization: A Survey on Role-Playing Language Agents](https://mlsanity.com/paper/2404.18231/) (2024)
- [AgentSociety: Large-Scale Simulation of LLM-Driven Generative Agents Advances Understanding of Human Behaviors and Society](https://mlsanity.com/paper/2502.08691/) (2025)
- [Language Agents with Reinforcement Learning for Strategic Play in the Werewolf Game](https://mlsanity.com/paper/2310.18940/) (2023)
- [FIRST: A Framework for Optimizing Information Quality in Mobile Crowdsensing Systems](https://mlsanity.com/paper/1804.11147/) (2018)
- [GenSim: A General Social Simulation Platform with Large Language Model based Agents](https://mlsanity.com/paper/2410.04360/) (2024)
- [Simulating Financial Market via Large Language Model based Agents](https://mlsanity.com/paper/2406.19966/) (2024)
- [Simulating Public Administration Crisis: A Novel Generative Agent-Based Simulation System to Lower Technology Barriers in Social Science Research](https://mlsanity.com/paper/2311.06957/) (2023)
- [AdaSociety: An Adaptive Environment with Social Structures for Multi-Agent Decision-Making](https://mlsanity.com/paper/2411.03865/) (2024)
- [Can A Society of Generative Agents Simulate Human Behavior and Inform Public Health Policy? A Case Study on Vaccine Hesitancy](https://mlsanity.com/paper/2503.09639/) (2025)
- [A Simulation System Towards Solving Societal-Scale Manipulation](https://mlsanity.com/paper/2410.13915/) (2024)
- [A Visualized Framework for Event Cooperation with Generative Agents](https://mlsanity.com/paper/2509.13011/) (2025)
- [EconGym: A Scalable AI Testbed with Diverse Economic Tasks](https://mlsanity.com/paper/2506.12110/) (2025)

## Built on by (most cited first, 2 of 2)

- [What Makes a Sale? Simulating End-to-End Seller--Buyer Retail Dynamics with LLM Agents](https://mlsanity.com/paper/2604.04468/) (2026)
- [Step-Level Preference Learning for Generative Agents in Social Simulations](https://mlsanity.com/paper/2607.14485/) (2026)

## Abstract (arXiv)

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.