ML FOR ECONOMIC POLICY

FRIDAY DECEMBER 11, NEURIPS 2020
  • 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?
  • How can we build rich and faithful simulations of economic systems with strategic agents?

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 workshop 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 workshop 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.

KEYNOTE SPEAKERS

  • Micheal Kearns (UPenn)
  • Susan Athey (Stanford)
  • Sendhil Mullainathan (University of Chicago)
  • Doina Precup (Deepmind, McGill)

PANELISTS

  • Sharad Goel (Stanford)
  • Daniel Bjorkegren (Brown)
  • Eva Tardos (Cornell)
  • Rediet Abebe (Harvard)
  • Thore Graepel (Deepmind, UCL)
  • Doyne Farmer (Oxford)
  • Marietje Schaake (Stanford)
  • Emma Pierson (Cornell)

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

Nika Haghtalab

Cornell

Annie Liang

UPenn

Jamie Morgenstern

UW

David C. Parkes

Harvard

Alex Trott

Salesforce

Stephan Zheng

Salesforce

CALL FOR PAPERS

BEST PAPER PRIZES

We will award two best paper prizes from the contributed works. The two awards will recognize methodological and empirical contributions, respectively. Each best paper will be invited to give an oral presentation during the workshop.

FINANCIAL SUPPORT

We will provide financial support (registration award) for authors with accepted papers, speakers, and attendees on a needs-based basis, sponsored by Salesforce. Based on demand, there will be a limit to the number of attendees we will support. Please fill out and apply via this form: https://forms.gle/WMhnyqz6vZe74b2Z7.

SUBMISSION INSTRUCTIONS

Submit here: EasyChair (https://easychair.org/conferences/?conf=mleconpolicy20).

This workshop is non-archival. All accepted papers will be presented as virtual posters and invited to record 5-minute videos, with exceptional submissions also presented as 20-minute oral presentations.

Submissions should not include work that was published or first made available before January 1, 2017. Work that has been published or has appeared on January 1, 2017, or later is acceptable. Work that is in submission / under review is acceptable.

  • Up to 8 pages (excluding references), using the NeurIPS format.
  • Please do not include author information, submissions must be made anonymous.
  • Each paper should include a short declaration on the ethics and societal impact of the work.
  • Each paper should state the relevance of the work to the workshop. The organizers will desk-reject submissions that are not relevant to the workshop.
  • Each paper should state that the submitted work has not been published or first made available before January 1, 2017, and is original work.
  • Any papers found in violation of these rules will be rejected.
  • Accepted papers will be online 2 weeks before the day of the workshop.
We will employ a double-blind reviewing system. Reviewers and organizers will not know the identity of the authors of submitted work. Only a designated external neutral party will have access to all author information.

IMPORTANT DATES

  • Submission Deadline: October 16, 2020, 11:59 pm, Anywhere-on-Earth
  • Notification of Acceptance: October 30, 2020
  • Camera-ready Deadline for Accepted Papers: TBC
  • Workshop: December 11, 2020

ACCESS

TBC

SCHEDULE

Start Time (EST) Event Speakers
12:00 pm Introduction
12:05 pm Invited Talk Michael Kearns
12:45 pm Best Paper: Methodology TBA
1:05 pm Invited Talk Suthan Athey
1:45 pm Panel Discussion: Algorithms & Methodology Eva Tardos
Thore Graepel
Doyne Farmer
Emma Pierson
2:45 pm Break
3:00 pm Introduction
3:05 pm Invited Talk Doina Precup
3:45 pm Best Paper: Empirical TBA
4:05 pm Invited Talk Sendhil Mullainathan
4:45 pm Panel Discussion: ML in Economics & Real-World Policy Rediet Abebe
Sharad Goel
Dan Bjorkegren
Marietje Schaake
5:45 pm Poster Session

ACCEPTED PAPERS

TBA

PROGRAM COMMITTEE

TBA