TOWARDS THE IMAGENET MOMENT FOR AI X ECONOMICS:
SOLIVING GLOBAL ECONOMIC AND CLIMATE CHALLENGES USING THE AI ECONOMIST

STEPHAN ZHENG

Biography
Stephan Zheng leads the AI Economist team at Salesforce Research. He works on using deep reinforcement learning and simulations to model economies and design economic policy. His work has been widely covered, e.g., in the Financial Times, Axios, Forbes, Zeit, Volkskrant, and MIT Tech Review. At Caltech (PhD 2018), he researched imitation learning of NBA basketball players and neural network robustness, amongst others, and interned at 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.

Abstract
Solving global socioeconomic challenges, e.g., economic inequality or sustainability, requires new tools and data to design effective economic policies. The AI Economist is a multi-agent deep reinforcement learning (RL) framework that outperforms and overcomes key limitations of traditional policy design methods. First, I will survey recent scientific and engineering progress in this area, including on AI tax policies that significantly improve equality and productivity and WarpDrive, our open-source framework for end-to-end multi-agent RL on GPUs. Second, I will introduce AI for Global Climate Cooperation (www.ai4climatecoop.org), an initiative to model and optimize climate negotiations and agreements using the AI Economist framework.