MLEconPolicy20: ML for Economic Policy Workshop |
Website | http://www.mlforeconomicpolicy.com/ |
Submission link | https://easychair.org/conferences/?conf=mleconpolicy20 |
Submission deadline | October 9, 2020 |
Machine Learning for Economic Policy
NeurIPS 2020, December 11-12 (day TBC)
mlforeconomicpolicy.neurips2020@gmail.com
- 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.
The workshop will host four keynote speakers, two panels, spotlight presentations, and breakout sessions for discussion. Confirmed keynote speakers include Susan Athey (Stanford), Michael Kearns (Penn) and Sendhil Mullainathan (Chicago).
Topics of interest
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.
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
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.
- Submit here: https://easychair.org/conferences/?conf=mleconpolicy20.
- Up to 8 pages (excluding references), using the NeurIPS format.
- Please do not include author information, submissions must be 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 two weeks before the day of the workshop.
Important Dates
- Submission Deadline: October 9, 2020, 11:59 pm, Anywhere-on-Earth
- Notification of Acceptance: October 30, 2020
- Camera-ready Deadline for Accepted Papers: TBC
- Workshop: December 11 or 12, 2020 (TBC)
Organizers
- Nika Haghtalab (Cornell)
- Annie Liang (UPenn)
- Jamie Morgenstern (UWashington)
- David C. Parkes (Harvard)
- Alex Trott (Salesforce)
- Stephan Zheng (Salesforce)