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Solving Heterogeneous General Equilibrium Economic Models with Deep Reinforcement Learning

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  • Edward Hill
  • Marco Bardoscia
  • Arthur Turrell

Abstract

General equilibrium macroeconomic models are a core tool used by policymakers to understand a nation's economy. They represent the economy as a collection of forward-looking actors whose behaviours combine, possibly with stochastic effects, to determine global variables (such as prices) in a dynamic equilibrium. However, standard semi-analytical techniques for solving these models make it difficult to include the important effects of heterogeneous economic actors. The COVID-19 pandemic has further highlighted the importance of heterogeneity, for example in age and sector of employment, in macroeconomic outcomes and the need for models that can more easily incorporate it. We use techniques from reinforcement learning to solve such models incorporating heterogeneous agents in a way that is simple, extensible, and computationally efficient. We demonstrate the method's accuracy and stability on a toy problem for which there is a known analytical solution, its versatility by solving a general equilibrium problem that includes global stochasticity, and its flexibility by solving a combined macroeconomic and epidemiological model to explore the economic and health implications of a pandemic. The latter successfully captures plausible economic behaviours induced by differential health risks by age.

Suggested Citation

  • Edward Hill & Marco Bardoscia & Arthur Turrell, 2021. "Solving Heterogeneous General Equilibrium Economic Models with Deep Reinforcement Learning," Papers 2103.16977, arXiv.org.
  • Handle: RePEc:arx:papers:2103.16977
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    References listed on IDEAS

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    Cited by:

    1. Beggs, Alan, 2022. "Reference points and learning," Journal of Mathematical Economics, Elsevier, vol. 100(C).
    2. Thomas J. Sargent & John Stachurski, 2024. "Dynamic Programming: Finite States," Papers 2401.10473, arXiv.org.
    3. Simone Brusatin & Tommaso Padoan & Andrea Coletta & Domenico Delli Gatti & Aldo Glielmo, 2024. "Simulating the economic impact of rationality through reinforcement learning and agent-based modelling," Papers 2405.02161, arXiv.org.
    4. Buckmann, Marcus & Haldane, Andy & Hüser, Anne-Caroline, 2021. "Comparing minds and machines: implications for financial stability," Bank of England working papers 937, Bank of England.
    5. Jialin Dong & Kshama Dwarakanath & Svitlana Vyetrenko, 2023. "Analyzing the Impact of Tax Credits on Households in Simulated Economic Systems with Learning Agents," Papers 2311.17252, arXiv.org.
    6. Michael Curry & Alexander Trott & Soham Phade & Yu Bai & Stephan Zheng, 2022. "Analyzing Micro-Founded General Equilibrium Models with Many Agents using Deep Reinforcement Learning," Papers 2201.01163, arXiv.org, revised Feb 2022.
    7. 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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