The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies
Designing sound economic policy is hard in practice, given a lack of high-quality economic data and limited opportunity to experiment. Bridging these gaps, the AI Economist is a two-level deep RL framework to learn economic policy in economic simulations with agents and a planner who both learn and co-adapt. This approach yields tax policies that improve the equality and productivity trade-off by at least 16%, compared to the Saez tax, US federal tax, and the free market. These results show that the AI Economist can overcome many limitations of traditional economics and provides an exciting new approach to economic design.
Stephan Zheng is a Lead Research Scientist and heads the AI Economist team at Salesforce Research. He currently works on using deep reinforcement learning and economic simulations to design economic policy. His work has been widely covered in the media, including the Financial Times, Axios, Forbes, Zeit, Volkskrant, MIT Tech Review, and others. He holds a Ph.D. in Physics from Caltech (2018), where he worked on imitation learning of NBA basketball players and neural network robustness, amongst others. He was twice a research intern with Google. Before machine learning, he studied mathematics and theoretical physics at the University of Cambridge, Harvard University, and Utrecht University. He received the Lorenz graduation prize from the Royal Netherlands Academy of Arts and Sciences for his master's thesis on exotic dualities in topological string theory and was twice awarded the Dutch national Huygens scholarship. www.stephanzheng.com.