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EconoJax: A Fast & Scalable Economic Simulation in Jax

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  • Koen Ponse
  • Aske Plaat
  • Niki van Stein
  • Thomas M. Moerland

Abstract

Accurate economic simulations often require many experimental runs, particularly when combined with reinforcement learning. Unfortunately, training reinforcement learning agents in multi-agent economic environments can be slow. This paper introduces EconoJax, a fast simulated economy, based on the AI economist. EconoJax, and its training pipeline, are completely written in JAX. This allows EconoJax to scale to large population sizes and perform large experiments, while keeping training times within minutes. Through experiments with populations of 100 agents, we show how real-world economic behavior emerges through training within 15 minutes, in contrast to previous work that required several days. To aid and inspire researchers to build more rich and dynamic economic simulations, we open-source EconoJax on Github at: https://github.com/ponseko/econojax.

Suggested Citation

  • Koen Ponse & Aske Plaat & Niki van Stein & Thomas M. Moerland, 2024. "EconoJax: A Fast & Scalable Economic Simulation in Jax," Papers 2410.22165, arXiv.org.
    • Handle: RePEc:arx:papers:2410.22165
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    References listed on IDEAS

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    1. Sascha Frey & Kang Li & Peer Nagy & Silvia Sapora & Chris Lu & Stefan Zohren & Jakob Foerster & Anisoara Calinescu, 2023. "JAX-LOB: A GPU-Accelerated limit order book simulator to unlock large scale reinforcement learning for trading," Papers 2308.13289, arXiv.org.
    2. Kshama Dwarakanath & Svitlana Vyetrenko & Peyman Tavallali & Tucker Balch, 2024. "ABIDES-Economist: Agent-Based Simulation of Economic Systems with Learning Agents," Papers 2402.09563, arXiv.org.
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