# EconGym: A Scalable AI Testbed with Diverse Economic Tasks

arXiv: https://arxiv.org/abs/2506.12110 · HTML: https://mlsanity.com/paper/2506.12110/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 52 references that are in the map.
- First posted on arXiv 13 Jun 2025.

## Builds on (most cited first, 18 of 18)

- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [TradingAgents: Multi-Agents LLM Financial Trading Framework](https://mlsanity.com/paper/2412.20138/) (2024)
- [AgentSociety: Large-Scale Simulation of LLM-Driven Generative Agents Advances Understanding of Human Behaviors and Society](https://mlsanity.com/paper/2502.08691/) (2025)
- [EconAgent: Large Language Model-Empowered Agents for Simulating Macroeconomic Activities](https://mlsanity.com/paper/2310.10436/) (2023)
- [Survey on Large Language Model-Enhanced Reinforcement Learning: Concept, Taxonomy, and Methods](https://mlsanity.com/paper/2404.00282/) (2024)
- [FinCon: A Synthesized LLM Multi-Agent System with Conceptual Verbal Reinforcement for Enhanced Financial Decision Making](https://mlsanity.com/paper/2407.06567/) (2024)
- [Society-in-the-Loop: Programming the Algorithmic Social Contract](https://mlsanity.com/paper/1707.07232/) (2017)
- [Emergent Bartering Behaviour in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2205.06760/) (2022)
- [Deep Reinforcement Learning in a Monetary Model](https://mlsanity.com/paper/2104.09368/) (2021)
- [DeepHAM: A Global Solution Method for Heterogeneous Agent Models with Aggregate Shocks](https://mlsanity.com/paper/2112.14377/) (2021)
- [A Multi-LLM-Agent-Based Framework for Economic and Public Policy Analysis](https://mlsanity.com/paper/2502.16879/) (2025)
- [Solving Heterogeneous General Equilibrium Economic Models with Deep Reinforcement Learning](https://mlsanity.com/paper/2103.16977/) (2021)
- [TaxAI: A Dynamic Economic Simulator and Benchmark for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2309.16307/) (2023)
- [Simulating the Economic Impact of Rationality through Reinforcement Learning and Agent-Based Modelling](https://mlsanity.com/paper/2405.02161/) (2024)
- [An Economy of Neural Networks: Learning from Heterogeneous Experiences](https://mlsanity.com/paper/2110.11582/) (2021)
- [AI-driven Prices for Externalities and Sustainability in Production Markets](https://mlsanity.com/paper/2106.06060/) (2021)
- [Can an AI agent hit a moving target?](https://mlsanity.com/paper/2110.02474/) (2021)
- [EconoJax: A Fast & Scalable Economic Simulation in Jax](https://mlsanity.com/paper/2410.22165/) (2024)

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

- [SimCity: Multi-Agent Urban Development Simulation with Rich Interactions](https://mlsanity.com/paper/2510.01297/) (2025)
- [CoRenew: A large language model agent-based policy simulation platform for multifamily residential redevelopment](https://mlsanity.com/paper/2607.25447/) (2026)

## Abstract (arXiv)

Artificial intelligence (AI) has become a powerful tool for economic research, enabling large-scale simulation and policy optimization. However, applying AI effectively requires simulation platforms for scalable training and evaluation-yet existing environments remain limited to simplified, narrowly scoped tasks, falling short of capturing complex economic challenges such as demographic shifts, multi-government coordination, and large-scale agent interactions. To address this gap, we introduce EconGym, a scalable and modular testbed that connects diverse economic tasks with AI algorithms. Grounded in rigorous economic modeling, EconGym implements 11 heterogeneous role types (e.g., households, firms, banks, governments), their interaction mechanisms, and agent models with well-defined observations, actions, and rewards. Users can flexibly compose economic roles with diverse agent algorithms to simulate rich multi-agent trajectories across 25+ economic tasks for AI-driven policy learning and analysis. Experiments show that EconGym supports diverse and cross-domain tasks-such as coordinating fiscal, pension, and monetary policies-and enables benchmarking across AI, economic methods, and hybrids. Results indicate that richer task composition and algorithm diversity expand the policy space, while AI agents guided by classical economic methods perform best in complex settings. EconGym also scales to 10k agents with high realism and efficiency.