The Lucas critique emphasizes the importance of considering the impact of policy changes on the expectations of micro-level agents in macroeconomic policymaking. However, the inherently self-interested nature of large-scale micro-agents, who pursue long-term benefits, complicates the formulation of optimal macroeconomic policies. This paper proposes a novel general framework named Dynamic Stackelberg Mean Field Games (Dynamic SMFG) to model such policymaking within sequential decision-making processes, with the government as the leader and households as dynamic followers. Dynamic SMFGs capture the dynamic interactions among large-scale households and their response to macroeconomic policy changes. To solve dynamic SMFGs, we propose the Stackelberg Mean Field Reinforcement Learning (SMFRL) algorithm, which leverages the population distribution of followers to represent high-dimensional joint state and action spaces. In experiments, our method surpasses macroeconomic policies in the real world, existing AI-based and economic methods. It allows the leader to approach the social optimum with the highest performance, while large-scale followers converge toward their best response to the leader's policy. Besides, we demonstrate that our approach retains effectiveness even when some households do not adopt the SMFG policy. In summary, this paper contributes to the field of AI for economics by offering an effective tool for modeling and solving macroeconomic policy-making issues.

翻译：卢卡斯批判强调了在宏观经济政策制定中考虑政策变化对微观主体预期影响的重要性。然而，大规模微观主体追求长期利益的自利本质使得最优宏观经济政策的制定变得复杂。本文提出了一种名为动态Stackelberg平均场博弈（Dynamic SMFG）的新型通用框架，用于在序贯决策过程中对此类政策制定进行建模，其中政府作为领导者，家庭作为动态跟随者。动态SMFG捕捉了大规模家庭之间的动态互动及其对宏观经济政策变化的响应。为解决动态SMFG，我们提出了Stackelberg平均场强化学习（SMFRL）算法，该算法利用跟随者的群体分布来表示高维联合状态与动作空间。在实验中，我们的方法超越了现实世界中的宏观经济政策、现有基于AI的方法以及经济学方法。它使领导者能够以最高性能接近社会最优状态，同时大规模跟随者收敛至对领导者政策的最佳响应。此外，我们证明了即使部分家庭未采用SMFG策略，我们的方法仍能保持有效性。总之，本文通过为宏观经济政策制定问题的建模与求解提供有效工具，推动了人工智能在经济学领域的应用。