EconGym: A Scalable AI Testbed with Diverse Economic Tasks

• URL: http://arxiv.org/abs/2506.12110v1
• Date: Fri, 13 Jun 2025 09:35:04 GMT
• Title: EconGym: A Scalable AI Testbed with Diverse Economic Tasks
• Authors: Qirui Mi, Qipeng Yang, Zijun Fan, Wentian Fan, Heyang Ma, Chengdong Ma, Siyu Xia, Bo An, Jun Wang, Haifeng Zhang,
• Abstract summary: We introduce EconGym, a scalable testbed that connects diverse economic tasks with AI algorithms.<n>Users can flexibly compose economic roles with diverse agent algorithms to simulate rich multi-agent trajectories.<n>Results indicate that richer task composition and algorithm diversity expand the policy space.
• Score: 14.392383131881877
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• From Debate to Equilibrium: Belief-Driven Multi-Agent LLM Reasoning via Bayesian Nash Equilibrium [52.28048367430481]
  Multi-agent frameworks can boost the reasoning power of large language models (LLMs), but they typically incur heavy computational costs and lack convergence guarantees.<n>We recast multi-LLM coordination as an incomplete-information game and seek a Bayesian Nash equilibrium (BNE)<n>We introduce Efficient Coordination via Nash Equilibrium (ECON), a hierarchical reinforcement-learning paradigm that marries distributed reasoning with centralized final output.
  arXiv  Detail & Related papers  (2025-06-09T23:49:14Z)
• Scalable, Symbiotic, AI and Non-AI Agent Based Parallel Discrete Event Simulations [0.0]
  This paper introduces a novel parallel discrete event simulation (PDES) based methodology to combine multiple AI and non-AI agents.<n>We evaluate our approach by solving four problems from four different domains and comparing the results with those from AI models alone.<n>Results show that overall accuracy of our approach is 68% where as the accuracy of vanilla models is less than 23%.
  arXiv  Detail & Related papers  (2025-05-28T17:50:01Z)
• Transforming the Hybrid Cloud for Emerging AI Workloads [82.21522417363666]
  This white paper envisions transforming hybrid cloud systems to meet the growing complexity of AI workloads.<n>The proposed framework addresses critical challenges in energy efficiency, performance, and cost-effectiveness.<n>This joint initiative aims to establish hybrid clouds as secure, efficient, and sustainable platforms.
  arXiv  Detail & Related papers  (2024-11-20T11:57:43Z)
• Learning Macroeconomic Policies through Dynamic Stackelberg Mean-Field Games [14.341143540616441]
  We formulate a dynamic Stackelberg game: the government (leader) sets policies, and agents (followers) respond by optimizing their behavior over time.<n>As the number of agents increases, explicitly simulating all agent-agent and agent-government interactions becomes computationally infeasible.<n>We propose the Dynamic Stackelberg Mean Field Game framework, which approximates these complex interactions via agent-population and government-population couplings.
  arXiv  Detail & Related papers  (2024-03-14T13:22:31Z)
• EconAgent: Large Language Model-Empowered Agents for Simulating Macroeconomic Activities [43.70290385026672]
  We introduce EconAgent, a large language model-empowered agent with human-like characteristics for macroeconomic simulation. We first construct a simulation environment that incorporates various market dynamics driven by agents' decisions. Through the perception module, we create heterogeneous agents with distinct decision-making mechanisms.
  arXiv  Detail & Related papers  (2023-10-16T14:19:40Z)
• Finding General Equilibria in Many-Agent Economic Simulations Using Deep Reinforcement Learning [72.23843557783533]
  We show that deep reinforcement learning can discover stable solutions that are epsilon-Nash equilibria for a meta-game over agent types. Our approach is more flexible and does not need unrealistic assumptions, e.g., market clearing. We demonstrate our approach in real-business-cycle models, a representative family of DGE models, with 100 worker-consumers, 10 firms, and a government who taxes and redistributes.
  arXiv  Detail & Related papers  (2022-01-03T17:00:17Z)
• The AI Economist: Optimal Economic Policy Design via Two-level Deep Reinforcement Learning [126.37520136341094]
  We show that machine-learning-based economic simulation is a powerful policy and mechanism design framework. The AI Economist is a two-level, deep RL framework that trains both agents and a social planner who co-adapt. In simple one-step economies, the AI Economist recovers the optimal tax policy of economic theory.
  arXiv  Detail & Related papers  (2021-08-05T17:42:35Z)
• ERMAS: Becoming Robust to Reward Function Sim-to-Real Gaps in Multi-Agent Simulations [110.72725220033983]
  Epsilon-Robust Multi-Agent Simulation (ERMAS) is a framework for learning AI policies that are robust to such multiagent sim-to-real gaps. ERMAS learns tax policies that are robust to changes in agent risk aversion, improving social welfare by up to 15% in complextemporal simulations. In particular, ERMAS learns tax policies that are robust to changes in agent risk aversion, improving social welfare by up to 15% in complextemporal simulations.
  arXiv  Detail & Related papers  (2021-06-10T04:32:20Z)
• The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies [119.07163415116686]
  We train social planners that discover tax policies that can effectively trade-off economic equality and productivity. We present an economic simulation environment that features competitive pressures and market dynamics. We show that AI-driven tax policies improve the trade-off between equality and productivity by 16% over baseline policies.
  arXiv  Detail & Related papers  (2020-04-28T06:57:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.