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.
翻译:一般平衡宏观经济模型是决策者用来理解一国经济的核心工具,它们代表经济,是前瞻性行为者的集合,其行为可能与随机效应相结合,在动态平衡中确定全球变量(如价格),然而,标准的半分析方法使得解决这些模型难以包括各种经济行为者的重要影响。COVID-19大流行进一步突出了不同性的重要性,例如在就业年龄和就业部门、宏观经济结果以及比较容易纳入这一结果的模型的必要性等方面。我们利用强化学习的技术解决这种模型,以简单、可扩展和计算效率的方式将不同物剂纳入其中。我们展示了这种方法在有已知分析解决办法的棘手问题上的准确性和稳定性,通过解决包括全球差异在内的一般均衡问题,并通过解决综合宏观经济和流行病学模型的灵活性,探索流行病的经济和健康影响。后者成功地捕捉了年龄差异健康风险引起的貌似合理的经济行为。