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In this research, we enhance the AI-Economist framework to better analyze economic dynamics during the COVID-19 pandemic using the fiscal theory of price level. We have added new features that allow for more assertive economic interventions by a federal planner, enabling detailed exploration of decisions like subsidy implementation and interest rate adjustments in a fiscally tight environment. Additionally, the utilization of multi-agent reinforcement learning and simulation-based policy analysis frameworks play a pivotal role in enriching our framework’s capabilities to address complex economic scenarios. Our improved model provides insights into managing future global crises by simulating policy responses, highlighting the trade-offs and impacts of various policy options to aid real-world decision-making.

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Fusing Multi-agent Reinforcement Learning and Fiscal Theory for Crisis Management

  • 会议论文

  • 发表日期：

    2026年

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  • Quang Truong Dang,
  • Bao Bui Quoc,
  • Anh Son Ta

摘要

In this research, we enhance the AI-Economist framework to better analyze economic dynamics during the COVID-19 pandemic using the fiscal theory of price level. We have added new features that allow for more assertive economic interventions by a federal planner, enabling detailed exploration of decisions like subsidy implementation and interest rate adjustments in a fiscally tight environment. Additionally, the utilization of multi-agent reinforcement learning and simulation-based policy analysis frameworks play a pivotal role in enriching our framework’s capabilities to address complex economic scenarios. Our improved model provides insights into managing future global crises by simulating policy responses, highlighting the trade-offs and impacts of various policy options to aid real-world decision-making.