Schedule - Topics - Organizers
Date | Speaker | Title |
Oct 6, 2020 | Doyne Farmer (Oxford) |
Global Microeconomics Video |
Nov 10, 2020 | Amy Greenwald (Brown) |
Learning Equilibria in Simulation-Based Games ... and the Ensuing Empirical Design of Mechanisms Video |
Dec 15, 2020 | Lihong Li (Google) |
Estimating Long-term Rewards by Off-policy Reinforcement Learning Video |
Feb 2, 2021 | Fei Fang (CMU) |
Game Theory and Machine Learning for Multiagent Communication and Coordination Video - Notes (thanks Yaman Habip!) |
March 9, 2021 | Thore Graepel (DeepMind, UCL) |
Automatic Curricula in Deep Multi-agent Reinforcement Learning Video |
April 6, 2021 | John Dickerson (University of Maryland) |
Deep Learning for Auction Design: Fairness, Robustness, and Expressiveness Video |
June 8, 2021 | Nika Haghtalab (UC Berkeley) |
Learning and Persuading with Anecdotes Video |
Machine learning offers enormous potential to transform our understanding of economics, economic decision making, and public policy. Yet its adoption by economists, social scientists, and policymakers remains nascent.
This seminar series will highlight both the opportunities as well as the barriers to the adoption of ML in economics. In particular, we aim to accelerate the use of machine learning to rapidly develop, test, and deploy effective economic policies that are grounded in representative data.
This seminar series will expose some of the critical socio-economic issues that stand to benefit from applying machine learning, expose underexplored economic datasets and simulations, and identify machine learning research directions that would have a significant positive socio-economic impact. This includes policies and mechanisms that target socio-economic issues such as diversity and fair representation in economic outcomes, economic equality, and improving economic opportunity.
David C. Parkes
Harvard
Alex Trott
Salesforce
Stephan Zheng
Salesforce