The AI Economist: Economic Policy Design Using Deep Reinforcement Learning and AI Simulations

Explore the groundbreaking AI Economist framework in this 39-minute conference talk by Stephan Zheng, Lead Research Scientist at Salesforce. Dive into the innovative use of deep reinforcement learning and AI simulations for designing economic policies. Learn how this approach addresses real-world socio-economic challenges, particularly in taxation, by overcoming limitations in traditional economics methodology. Discover key research findings, including AI tax policies that significantly improve equality-productivity trade-offs, and AI-driven strategies for balancing public health and economic outcomes during the COVID-19 pandemic. Gain insights into WarpDrive, an open-source GPU framework for multi-agent reinforcement learning, and explore recent developments in modeling general equilibrium economies and creating RL agents that mimic human economic behavior. This talk, part of the Toronto Machine Learning Series, offers a comprehensive look at the intersection of AI and economic policy design.