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Learning Macroeconomic Policies through Dynamic Stackelberg Mean-Field Games

arXiv 2403.12093 ↗First posted 14 Mar 2024econ.TH cs.AIcited by 0

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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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 53 references that are in the map. First posted on arXiv 14 Mar 2024.

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References · 16

  1. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,519
  2. 2018Mean Field Multi-Agent Reinforcement Learningcited by 202
  3. 2016Mean Field Game of Controls and An Application To Trade Crowdingcited by 22
  4. 2021Building a Foundation for Data-Driven, Interpretable, and Robust Policy Design using the AI Economistcited by 22
  5. 2021Reinforcement Learning for Mean Field Games, with Applications to Economicscited by 14
  6. 2021Deep Reinforcement Learning in a Monetary Modelcited by 10
  7. 2021Solving Heterogeneous General Equilibrium Economic Models with Deep Reinforcement Learningcited by 9
  8. 2023TaxAI: A Dynamic Economic Simulator and Benchmark for Multi-Agent Reinforcement Learningcited by 8
  9. 2021Deep Learning for Principal-Agent Mean Field Gamescited by 5
  10. 2021An Economy of Neural Networks: Learning from Heterogeneous Experiencescited by 4
  11. 2022Optimization frameworks and sensitivity analysis of Stackelberg mean-field gamescited by 4
  12. 2021AI-driven Prices for Externalities and Sustainability in Production Marketscited by 3
4 more
  1. 2021Can an AI agent hit a moving target?cited by 3
  2. 2017A Mean-Field Stackelberg Game Approach for Obfuscation Adoption in Empirical Risk Minimizationcited by 1
  3. 2023Mean Field Games in a Stackelberg problem with an informed major playercited by 1
  4. 2023A Stochastic Non-Zero-Sum Game of Controlling the Debt-to-GDP Ratiocited by 1

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