The AI economist: taxation policy design via two-level deep multiagent reinforcement learning
Open accessAbstract
Artificial intelligence (AI) and reinforcement learning (RL) have improved many areas but are not yet widely adopted in economic policy design, mechanism design, or economics at large. The AI Economist is a two-level, deep RL framework for policy design in which agents and a social planner coadapt. In particular, the AI Economist uses structured curriculum learning to stabilize the challenging two-level, coadaptive learning problem. We validate this framework in the domain of taxation. In one-step economies, the AI Economist recovers the optimal tax policy of economic theory. In spatiotemporal economies, the AI Economist substantially improves both utilitarian social welfare and the trade-off between equality and productivity over baselines. It does so despite emergent tax-gaming strategies while accounting for emergent labor specialization, agent interactions, and behavioral change. These results demonstrate that two-level, deep RL complements economic theory and unlocks an AI-based approach to designing and understanding economic policy.
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Prior urban models depend on manually specified rules or fixed utility functions, which restricts their capacity to represent human diversity, adaptability, and long-term dynamics.
“Traditional models often leverage hand-crafted rules or utility functions, restricting their ability to represent the diversity, adaptability, and long-term dynamics inherent in humans”
From CitySim: Modeling Urban Behaviors and City Dynamics with Large-Scale LLM-Driven Agent Simulation (2025) · cites this paper as Zheng et al., 2022
The Results (Emergent phenomena in Gather-Trade-Build; page not provided) contrast learned, heterogeneous behavior with the fixed behavioral rules of traditional agent-based models. The Introduction (Optimal tax policy; page not provided) says theoretical tax models simplify interactions and intertemporal dynamics. The paper does not establish that fixed utility functions cause these limitations—in fact, its own learning agents optimize specified utilities—or make this general claim about urban models and human diversity. (LLM, conf=0.86)
Experimental — accuracy not yet measuredMechanism designers have developed incentive-compatible systems that can lead to cooperation between self-interested agents, but these systems often assume a top-down process that coordinates the process.
“Although mechanism designers have developed incentive-compatible systems that can lead to cooperation between self-interested agents, these systems often assume a top-down process that coordinates the process [65, 75].”
From Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM Agents (2024) · cites this paper as [75]
The Introduction (page not provided) describes mechanism design as a social planner choosing policies so rational agents’ behavior yields a desired social outcome. Its subsection on AI-driven policy design describes a hierarchical game in which the planner designs payoffs and agents optimize their behavior. These passages support the top-down planner aspect, but do not establish that incentive-compatible systems have led to cooperation between self-interested agents or that such systems often require top-down coordination. (LLM, conf=0.82)
Experimental — accuracy not yet measuredAI's potential to simulate various policy outcomes based on historical data and predictive models can aid policymakers in understanding the potential impacts of their decisions.
“Moreover, AI's potential to simulate various policy outcomes based on historical data and predictive models can aid policymakers in understanding the potential impacts of their decisions (145, 146)”
From The impact of generative artificial intelligence on socioeconomic inequalities and policy making (2024) · cites this paper as [145]
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- Authors
- Zheng, S., Trott, A., Srinivasa, S., Parkes, D. C., & Socher, R.
- Affiliations
- Salesforce (United States) (US)
- Harvard University (US)
- You.com, Palo Alto, CA, USA.
- Corresponding author
- Zheng, Stephan
- Trott, Alexander
- Venue
- Science Advances
- Published
- 2022-05-06
- Volume
- 8
- Issue
- 18
- Pages
- eabk2607-eabk2607
- ISSN
- 2375-2548 (ISSN-L)
- PMC
- PMC9067926
- OpenAlex
- W4228997196
- Cited by
- 105
- References
- 30
- Citations by year
- 2026: 27 · 2025: 30 · 2024: 29 · 2023: 15 · 2022: 3 · 2020: 1
- FWCI
- 6.33
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Science Advances (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Economic and Fiscal Studies
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Game Theory and Voting Systems
- Keywords
- optimal taxation, multi-agent reinforcement learning, social planner, curriculum learning, division of labor, social welfare (welfare economics)
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