EconGym: A Scalable AI Testbed with Diverse Economic Tasks

2025-06-13

AI Summary

Existing AI-based economic simulation platforms suffer from oversimplified tasks and limited scalability, failing to capture realistic complexities such as demographic transitions, multi-level government coordination, and large-scale heterogeneous agent interactions. Method: We introduce the first modular, multi-agent economic simulation platform supporting high-fidelity, cross-domain modeling with up to 10,000 heterogeneous agents. It encompasses 25+ real-world economic tasks—including fiscal policy, pension systems, and monetary policy—and integrates 11 agent roles with microfoundational interaction mechanisms. The platform bridges dynamic general equilibrium theory with AI training requirements, proposing a novel algorithm–economics hybrid benchmarking framework. Contribution/Results: Experiments demonstrate that AI agents augmented with classical economic methods achieve optimal performance in policy-coordination tasks. The platform enables real-time simulation of 10,000 agents, substantially expanding the explorable policy space and enhancing evaluation robustness and generalizability.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Mechanism Design
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• Lack of scalable AI platforms for complex economic simulations
• Limited existing environments for multi-agent economic interactions
• Need for diverse economic task integration with AI algorithms

Innovation

• Scalable modular testbed for economic AI tasks
• Diverse agent roles with defined interactions
• Supports 10k agents with high efficiency