通过强化学习来控制欧洲主权债务
Tato Khundadze1, Willi Semmler1,2,3
1Department of Economics, The New School for Social Research, New York, NY, United States.
Frontiers in artificial intelligence
|July 3, 2025
概括
经济性取决于危机期间协调的货币和财政政策. 本研究展示了使用先进算法的合作策略,有效管理公共债务,稳定像欧元区这样的经济.
科学领域:
- 经济学 经济学 经济学
- 宏观经济的建模.
- 计算经济学是计算机经济学.
背景情况:
- 经济系统的性取决于利益相关者在冲击期间的协调.
- 过去的危机 (欧元区,COVID-19) 突出了协调失败及其后果.
- 不同质的经济区域内的不对称冲击需要协调的政策反应.
研究的目的:
- 展示在紧急情况下货币和财政政策合作的重要性.
- 解决宏观经济的不可抗性问题,重点关注公共债务管理.
- 开发和测试一个宏观经济建模框架来模拟合作政策的相互作用.
主要方法:
- 开发一个宏观经济建模框架.
- 非线性控制模型与强化学习技术的整合.
- 实施软行动者-批评算法,以优化政策.
主要成果:
- 软行为者-关键算法优化了对关键宏观经济变量的政策反应.
- 该算法表现出与非线性模型预测控制 (NMPC) 相同或更高的性能.
- 模拟显示了合作制度在管理宏观经济稳定的有效性.
结论:
- 协调的货币和财政政策对于经济性至关重要.
- 强化学习,特别是软行为者-批判,为宏观经济政策优化提供了强大的工具.
- 这些发现对于像欧元区这样面临不对称冲击的多行为体经济区域尤为重要.
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