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Machine Learning for Economic Policy
Stephan Zheng · Alexander Trott · Annie Liang · Jamie Morgenstern · David Parkes · Nika Haghtalab

Fri Dec 11 09:00 AM -- 04:00 PM (PST) @ None
Event URL: http://www.mlforeconomicpolicy.com »


The goal of this workshop is to inspire and engage a broad interdisciplinary audience, including computer scientists, economists, and social scientists, around topics at the exciting intersection of economics, public policy, and machine learning. We feel that machine learning offers enormous potential to transform our understanding of economics, economic decision making, and public policy, and yet its adoption by economists and social scientists remains nascent.

We want to use the workshop to 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 significant positive socio-economic impact. In effect, we aim to accelerate the use of machine learning to rapidly develop, test, and deploy fair and equitable economic policies that are grounded in representative data.

For example, we would like to explore questions around whether machine learning can be used to help with the development of effective economic policy, to understand economic behavior through granular, economic data sets, to automate economic transactions for individuals, and how we can build rich and faithful simulations of economic systems with strategic agents. We would like to develop economic policies and mechanisms that target socio-economic issues including diversity and fair representation in economic outcomes, economic equality, and improving economic opportunity. In particular, we want to highlight both the opportunities as well as the barriers to adoption of ML in economics.

Fri 9:00 a.m. - 9:05 a.m.
Introduction 1 (Opening Remarks)
Fri 9:05 a.m. - 9:45 a.m.

Privacy and Fairness in Markets and Finance

Michael Kearns
Fri 9:45 a.m. - 10:00 a.m.

"Estimating Policy Functions in Payment Systems using Reinforcement Learning” P. Castro, A. Desai, H. Du, R. Garratt, F. Rivadeneyra

Fri 10:00 a.m. - 10:05 a.m.
5 Minute Break (Break)
Fri 10:05 a.m. - 10:45 a.m.
Keynote: Doina Precup (Keynote)
Doina Precup
Fri 10:45 a.m. - 11:45 a.m.

Eva Tardos, Thore Graepel, Doyne Farmer, & Emma Pierson

Fri 11:45 a.m. - 12:00 p.m.
15 Minute Break (Break)
Fri 12:00 p.m. - 12:40 p.m.
Keynote: Susan Athey (Keynote)
Susan Athey
Fri 12:40 p.m. - 12:55 p.m.

“Empirical Welfare Maximization with Constraints”, L. Sun

Fri 12:55 p.m. - 1:00 p.m.
5 Minute Break (Break)
Fri 1:00 p.m. - 1:40 p.m.

Machine Learning and Economic Policy: The Uses of Prediction.

Machine learning tools excel at producing models that work in a predictive sense. Economics and policy, however, rely heavily on causality. One fruitful approach to this tension is to marry causal inference and machine learning techniques. In this talk, I will argue for a complementary, second approach: that prediction in and of itself can be very useful for a swath of applications. Many important policy problems have embedded in them pure prediction problems. Moreover, prediction tools by themselves can help reveal fundamental social mechanisms. These kinds of applications are plentiful, but sit in a blind spot: because we have not had prediction tools in the past, we are not used to seeing them.

Sendhil Mullainathan
Fri 1:40 p.m. - 2:40 p.m.

Rediet Abebe, Sharad Goel, Dan Bjorkegren, & Marietje Schaake

Fri 2:40 p.m. - 2:45 p.m.
5 Minute Break (Break)
Fri 2:45 p.m. - 4:00 p.m.
Posters, Focus Groups, Unstructured Discussion (Poster Session)  link »

Author Information

Stephan Zheng (Salesforce)
Alex Trott (Salesforce Research)
Annie Liang (UPenn)
Jamie Morgenstern (U Washington)
David Parkes (Harvard University)

David C. Parkes is Gordon McKay Professor of Computer Science in the School of Engineering and Applied Sciences at Harvard University. He was the recipient of the NSF Career Award, the Alfred P. Sloan Fellowship, the Thouron Scholarship and the Harvard University Roslyn Abramson Award for Teaching. Parkes received his Ph.D. degree in Computer and Information Science from the University of Pennsylvania in 2001, and an M.Eng. (First class) in Engineering and Computing Science from Oxford University in 1995. At Harvard, Parkes leads the EconCS group and teaches classes in artificial intelligence, optimization, and topics at the intersection between computer science and economics. Parkes has served as Program Chair of ACM EC’07 and AAMAS’08 and General Chair of ACM EC’10, served on the editorial board of Journal of Artificial Intelligence Research, and currently serves as Editor of Games and Economic Behavior and on the boards of Journal of Autonomous Agents and Multi-agent Systems and INFORMS Journal of Computing. His research interests include computational mechanism design, electronic commerce, stochastic optimization, preference elicitation, market design, bounded rationality, computational social choice, networks and incentives, multi-agent systems, crowd-sourcing and social computing.

Nika Haghtalab (Cornell University)

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