  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.
