The AI Economist- Improving Equality and Productivity with AI-Driven Tax Policies

Explore groundbreaking research on AI-driven tax policies in this Simons Institute lecture from the Theory of Reinforcement Learning Boot Camp. Delve into the concept of the AI Economist, examining how artificial intelligence can be leveraged to improve equality and productivity through innovative tax strategies. Learn about economic inequality, the history of tax theory, and the application of reinforcement learning in economic simulations. Discover the intricacies of a spatial world model, including agent behavior, specialization, and communication. Investigate the role of deep learning in optimizing government policies, mechanism design, and income taxes. Compare various tax models, explore measures of equality, and understand how AI economists respond to economic shocks. Gain insights into the potential of AI to reshape economic policy and address real-world challenges in wealth distribution and productivity.

Introduction
What is this research
Why AI
Economic Inequality
History of Tax Theory
Questions
The Spatial World
LowLevel Details
Assumptions
Simulation
Agents
Deep Behavioral Parameters
Why a Simplified Setting
Specialization
Communication
RL
Government
Optimality
Mechanism Design
Income Taxes
Deep Learning
Two Level Learning
Mitigating Instability
Multiagent Learning
SidebySide Comparison
Tax Models
Elasticity
Strategic
Testing
Measuring Equality
Other Measures of Equality
How AI Economists Respond to Shocks
Conceptual Question