# Building a Foundation for Data-Driven, Interpretable, and Robust Policy Design using the AI Economist

arXiv: https://arxiv.org/abs/2108.02904 · HTML: https://mlsanity.com/paper/2108.02904/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 30 references that are in the map.
- First posted on arXiv 6 Aug 2021.

## Builds on (most cited first, 2 of 2)

- [The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies](https://mlsanity.com/paper/2004.13332/) (2020)
- [The AI Economist: Optimal Economic Policy Design via Two-level Deep Reinforcement Learning](https://mlsanity.com/paper/2108.02755/) (2021)

## Built on by (most cited first, 23 of 23)

- [The Effects of Reward Misspecification: Mapping and Mitigating Misaligned Models](https://mlsanity.com/paper/2201.03544/) (2022)
- [EconAgent: Large Language Model-Empowered Agents for Simulating Macroeconomic Activities](https://mlsanity.com/paper/2310.10436/) (2023)
- [When Can We Learn General-Sum Markov Games with a Large Number of Players Sample-Efficiently?](https://mlsanity.com/paper/2110.04184/) (2021)
- [Independent Policy Gradient for Large-Scale Markov Potential Games: Sharper Rates, Function Approximation, and Game-Agnostic Convergence](https://mlsanity.com/paper/2202.04129/) (2022)
- [LLM Economist: Large Population Models and Mechanism Design in Multi-Agent Generative Simulacra](https://mlsanity.com/paper/2507.15815/) (2025)
- [Provably Fast Convergence of Independent Natural Policy Gradient for Markov Potential Games](https://mlsanity.com/paper/2310.09727/) (2023)
- [WarpDrive: Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU](https://mlsanity.com/paper/2108.13976/) (2021)
- [Data-Centric Epidemic Forecasting: A Survey](https://mlsanity.com/paper/2207.09370/) (2022)
- [Reinforcement Learning in Practice: Opportunities and Challenges](https://mlsanity.com/paper/2202.11296/) (2022)
- [Interpretable AI for policy-making in pandemics](https://mlsanity.com/paper/2204.04256/) (2022)
- [ADAGE: A generic two-layer framework for adaptive agent based modelling](https://mlsanity.com/paper/2501.09429/) (2025)
- [Solving Dynamic Principal-Agent Problems with a Rationally Inattentive Principal](https://mlsanity.com/paper/2202.01691/) (2022)
- [Carbon Market Simulation with Adaptive Mechanism Design](https://mlsanity.com/paper/2406.07875/) (2024)
- [Incentives to Build Houses, Trade Houses, or Trade House Building Skills in Simulated Worlds under Various Governing Systems or Institutions: Comparing Multi-agent Reinforcement Learning to Generative Agent-based Model](https://mlsanity.com/paper/2411.17724/) (2024)
- [Analyzing Micro-Founded General Equilibrium Models with Many Agents using Deep Reinforcement Learning](https://mlsanity.com/paper/2201.01163/) (2022)
- [Linear Convergence of Independent Natural Policy Gradient in Games with Entropy Regularization](https://mlsanity.com/paper/2405.02769/) (2024)
- [Empirical Equilibria in Agent-based Economic systems with Learning agents](https://mlsanity.com/paper/2408.12038/) (2024)
- [EconoJax: A Fast & Scalable Economic Simulation in Jax](https://mlsanity.com/paper/2410.22165/) (2024)
- [Social Interpretable Reinforcement Learning](https://mlsanity.com/paper/2401.15480/) (2024)
- [Learning Macroeconomic Policies through Dynamic Stackelberg Mean-Field Games](https://mlsanity.com/paper/2403.12093/) (2024)
- [Enabling High Data Throughput Reinforcement Learning on GPUs: A Domain Agnostic Framework for Data-Driven Scientific Research](https://mlsanity.com/paper/2408.00930/) (2024)
- [EconAI: Dynamic Persona Evolution and Memory-Aware Agents in Evolving Economic Environments](https://mlsanity.com/paper/2605.13762/) (2026)
- [Why Study Emergent Behavior When You Can Regulate It? Aligning Multi-Agent Systems with Reward Prediction](https://mlsanity.com/paper/2608.07280/) (2026)

## Abstract (arXiv)

Optimizing economic and public policy is critical to address socioeconomic issues and trade-offs, e.g., improving equality, productivity, or wellness, and poses a complex mechanism design problem. A policy designer needs to consider multiple objectives, policy levers, and behavioral responses from strategic actors who optimize for their individual objectives. Moreover, real-world policies should be explainable and robust to simulation-to-reality gaps, e.g., due to calibration issues. Existing approaches are often limited to a narrow set of policy levers or objectives that are hard to measure, do not yield explicit optimal policies, or do not consider strategic behavior, for example. Hence, it remains challenging to optimize policy in real-world scenarios. Here we show that the AI Economist framework enables effective, flexible, and interpretable policy design using two-level reinforcement learning (RL) and data-driven simulations. We validate our framework on optimizing the stringency of US state policies and Federal subsidies during a pandemic, e.g., COVID-19, using a simulation fitted to real data. We find that log-linear policies trained using RL significantly improve social welfare, based on both public health and economic outcomes, compared to past outcomes. Their behavior can be explained, e.g., well-performing policies respond strongly to changes in recovery and vaccination rates. They are also robust to calibration errors, e.g., infection rates that are over or underestimated. As of yet, real-world policymaking has not seen adoption of machine learning methods at large, including RL and AI-driven simulations. Our results show the potential of AI to guide policy design and improve social welfare amidst the complexity of the real world.