# Learning Macroeconomic Policies through Dynamic Stackelberg Mean-Field Games

arXiv: https://arxiv.org/abs/2403.12093 · HTML: https://mlsanity.com/paper/2403.12093/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 53 references that are in the map.
- First posted on arXiv 14 Mar 2024.

## Builds on (most cited first, 16 of 16)

- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Mean Field Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1802.05438/) (2018)
- [Mean Field Game of Controls and An Application To Trade Crowding](https://mlsanity.com/paper/1610.09904/) (2016)
- [Building a Foundation for Data-Driven, Interpretable, and Robust Policy Design using the AI Economist](https://mlsanity.com/paper/2108.02904/) (2021)
- [Reinforcement Learning for Mean Field Games, with Applications to Economics](https://mlsanity.com/paper/2106.13755/) (2021)
- [Deep Reinforcement Learning in a Monetary Model](https://mlsanity.com/paper/2104.09368/) (2021)
- [Solving Heterogeneous General Equilibrium Economic Models with Deep Reinforcement Learning](https://mlsanity.com/paper/2103.16977/) (2021)
- [TaxAI: A Dynamic Economic Simulator and Benchmark for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2309.16307/) (2023)
- [Deep Learning for Principal-Agent Mean Field Games](https://mlsanity.com/paper/2110.01127/) (2021)
- [An Economy of Neural Networks: Learning from Heterogeneous Experiences](https://mlsanity.com/paper/2110.11582/) (2021)
- [Optimization frameworks and sensitivity analysis of Stackelberg mean-field games](https://mlsanity.com/paper/2210.04110/) (2022)
- [AI-driven Prices for Externalities and Sustainability in Production Markets](https://mlsanity.com/paper/2106.06060/) (2021)
- [Can an AI agent hit a moving target?](https://mlsanity.com/paper/2110.02474/) (2021)
- [A Mean-Field Stackelberg Game Approach for Obfuscation Adoption in Empirical Risk Minimization](https://mlsanity.com/paper/1706.02693/) (2017)
- [Mean Field Games in a Stackelberg problem with an informed major player](https://mlsanity.com/paper/2311.05229/) (2023)
- [A Stochastic Non-Zero-Sum Game of Controlling the Debt-to-GDP Ratio](https://mlsanity.com/paper/2311.17711/) (2023)

## Abstract (arXiv)

Macroeconomic outcomes emerge from individuals' decisions, making it essential to model how agents interact with macro policy via consumption, investment, and labor choices. We formulate this as a dynamic Stackelberg game: the government (leader) sets policies, and agents (followers) respond by optimizing their behavior over time. Unlike static models, this dynamic formulation captures temporal dependencies and strategic feedback critical to policy design. However, as the number of agents increases, explicitly simulating all agent-agent and agent-government interactions becomes computationally infeasible. To address this, we propose the Dynamic Stackelberg Mean Field Game (DSMFG) framework, which approximates these complex interactions via agent-population and government-population couplings. This approximation preserves individual-level feedback while ensuring scalability, enabling DSMFG to jointly model three core features of real-world policymaking: dynamic feedback, asymmetry, and large scale. We further introduce Stackelberg Mean Field Reinforcement Learning (SMFRL), a data-driven algorithm that learns the leader's optimal policies while maintaining personalized responses for individual agents. Empirically, we validate our approach in a large-scale simulated economy, where it scales to 1,000 agents (vs. 100 in prior work) and achieves a fourfold increase in GDP over classical economic methods and a nineteenfold improvement over the static 2022 U.S. federal income tax policy.