AI FOR ECONOMICS

SEMINAR CO-ORGANIZED BY HARVARD ECON-CS & SALESFORCE AI RESEARCH

Schedule - Topics - Organizers

  • Can machine learning be used to help with the development of effective economic policy?
  • Can we understand economic behavior through granular, economic data sets?
  • Can we automate economic transactions for individuals?
  • Can we build rich and faithful simulations of economic systems with strategic agents?

SCHEDULE

FULL SCHEDULE

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

TOPICS

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.

ECONOMICS

  • Inequality and social mobility
  • Sustainability
  • Innovation + entrepreneurship
  • Market design (e.g., labor, capital, consumer-facing)
  • Taxation
  • Behavioral economics
  • Game theory
  • Data-driven policy-making, and collecting representative and robust economic datasets

MACHINE LEARNING

  • Reinforcement learning: multi-agent RL, cooperation, social dilemmas, principal-agent problems, equilibria and solution concepts.
  • Inverse reinforcement learning
  • Transfer from simulation to the real world
  • Multi-objective and constrained optimization
  • Causal inference
  • Explainability
  • Ethical issues: addressing bias in economic data, learning equitable policies, privacy-preserving learning.

ORGANIZATION

David C. Parkes

Harvard

Alex Trott

Salesforce

Stephan Zheng

Salesforce