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


Dr. Stephan Zheng, Salesforce Research

Tackling real-world socio-economic challenges requires designing and testing economic policies, such as income tax policy. However, this is hard in practice, due to a lack of appropriate (micro-level) economic data and limited opportunity to experiment. We propose a two-level deep reinforcement learning approach to learn dynamic tax policies, based on principled economic simulations in which both agents and a social planner (government) learn and adapt. AI social planners can discover tax policies that improve the equality and productivity trade-off by at least 16%, compared to the prominent Saez tax model, US Federal tax, and the free market. The learned tax policies are qualitatively different from the baselines, and certain model instances are effective in human studies as well.

This talk will present three topics: 1) economic policy design in the context of multi-agent RL, 2) our two-level RL approach to economic policy design, and 3) open research problems towards an AI Economist for the real world. These include key methodological challenges in two-level RL and data-driven economic modeling, multi-agent RL, mechanism design, robustness, explainability, and others.

Please find the full paper here: https://arxiv.org/abs/2004.13332



About the speaker

Stephan Zheng (www.stephanzheng.com) 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 Research and Google Brain. 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.



Venue: via Zoom (please contact us at seminars@merit.unu.edu for the Zoom link)

Date: 18 March 2021

Time: 16:30 - 17:30


UNU-MERIT