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The AI Economist Economic Policy Design using Deep Reinforcement Learning and AI Simulations
- Speaker’s Bio: Stephan Zheng, Lead Research Scientist, Salesforce
Stephan Zheng (www.stephanzheng.com) leads the AI Economist team at Salesforce Research, which works on deep reinforcement learning and AI 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) and interned 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 Dutch Lorenz graduation prize for his thesis on exotic dualities in topological string theory and was twice awarded the Dutch national Huygens scholarship.
Abstract
Tackling real-world socio-economic challenges requires designing and testing economic policies, such as for taxation. This is hard in practice, due to a lack of (micro-level) economic data, limited opportunity to experiment, and limitations of economics methodology. The AI Economist is an AI simulation and reinforcement learning (RL) framework to address these challenges. In this talk, I will survey key research findings and ongoing work towards applying the AI Economist in the real world. First, we found that AI tax policies can improve the equality and productivity trade-off by at least 16%, compared to the classic Saez tax, US federal tax, and the free market. Second, in a COVID-19 simulation, AI policies can improve public health and economic outcomes. Third, we open-sourced WarpDrive, a GPU framework for multi-agent RL that is orders of magnitude faster than CPU + GPU solutions. Finally, I will survey recent results on modeling general equilibrium economies and RL agents that mimic human economic behavior.