ERC Starting Grant: Machine Learning-based Market Design

Market designers study how to set the "rules of a marketplace" such that the market works well. However, markets are getting increasingly complex such that designing good market mechanisms "by hand" is often infeasible, in particular when certain design desiderata (such as efficiency, strategyproofness, or fairness) are in conflict with each other. Moreover, human agents are boundedly-rational: already in small domains, they are best modeled as having incomplete preferences, because they may only know a ranking or the values of their top choices. In combinatorial domains, the number of choices grows exponentially, such that it quickly becomes impossible for an agent to report its full valuation, even if it had complete preferences. In this ERC project, we aim to combine techniques from "machine learning" with "market design" to address these challenges.

In addition to pushing the scientific boundaries of market design research, this ERC project will also have an immediate impact on practical market design. We will apply our techniques in two different settings: (1) for the design of combinatorial spectrum auctions, a multi-billion dollar domain; and (2) for the design of matching markets (e.g., school choice, refugee matching, adoption matching).

• Principal Investigator:
  • Prof. Dr. Sven Seuken (University of Zurich)
• PhD Students:
  • Paul Friedrich (University of Zurich)
  • Ermis Soumalias (University of Zurich)
  • Jakob Weissteiner (University of Zurich)
  • Behnoosh Zamanlooy (University of Zurich)
• Master's Students:
  • Manuel Beyeler (University of Zurich)
• PostDocs:
  • Dr. Ludwig Dierks (University of Zurich)
  • We are hiring!

• External Collaborators:
  • Prof. Benjamin Lubin, PhD (Boston University)
  • Prof. Dr. Vincent Slaugh (SC Johnson College of Business, Cornell University)
  • Prof. Dr. M. Utku Ünver (Department of Economics, Boston College)
  • Dr. Gianluca Brero (Harvard University)
  • Dr. Sébastien Lahaie (Google Research)

Open Positions: We are looking for 1 PostDoc with expertise in Machine Learning to join this project! Please apply here!

1. Uncertainty Bounds for Neural Networks - An algorithmic Approach.
   Jakob Weissteiner, Hanna Wutte, Jakob Heiss, Sven Seuken, and Josef Teichmann. Working Paper. February 2021.
2. Search and Matching for Adoption from Foster Care.
   Nils Olberg, Ludwig Dierks, Sven Seuken, Vincent Slaugh, and M. Utku Ünver. Working Paper. February 2021.
3. Machine Learning-powered Iterative Combinatorial Auctions with Interval Bidding. [pdf]
   Manuel Beyeler, Gianluca Brero, Benjamin Lubin, and Sven Seuken. Working Paper. September 2020.
4. Fourier Analysis-based Iterative Combinatorial Auctions. [pdf]
   Jakob Weissteiner, Chris Wendler, Sven Seuken, Ben Lubin, and Markus Püschel. Working Paper. September 2020.
5. Deep Learning-powered Iterative Combinatorial Auctions. [pdf]
   Jakob Weissteiner and Sven Seuken. In Proceedings of the Thirty-fourth AAAI Conference on Artificial Intelligence (AAAI-20), New York, NY, February 2020.
6. Machine Learning-powered Iterative Combinatorial Auctions. [pdf]
   Gianluca Brero, Benjamin Lubin, and Sven Seuken. Working Paper. November 2019.
7. Enabling Trade-offs in Machine Learning-based Matching for Refugee Resettlement. [pdf]
   Nils Olberg and Sven Seuken. Working Paper. June 2019.
8. Fast Iterative Combinatorial Auctions via Bayesian Learning. [pdf]
   Gianluca Brero, Sébastien Lahaie, and Sven Seuken. In Proceedings of the Thirty-third AAAI Conference of Artificial Intelligence (AAAI-19), Honolulu, USA, January 2019.
9. Combinatorial Auctions via Machine Learning-based Preference Elicitation. [pdf]
   Gianluca Brero, Benjamin Lubin, and Sven Seuken. In Proceedings of the Twenty-seventh International Joint Conference on Artificial Intelligence and the Twenty-third European Conference on Artificial Intelligence (IJCAI-ECAI-18), Stockholm, Sweden, July 2018.
10. Probably Approximately Efficient Combinatorial Auctions via Machine Learning. [pdf]
    Gianluca Brero, Benjamin Lubin, and Sven Seuken. In Proceedings of the Thirty-first AAAI Conference of Artificial Intelligence (AAAI-17), San Francisco, USA, February 2017.

1. Interview with Sven Seuken in the UZH Magazin
   UZH Department of Informatics News Entry, March 24, 2020

2. ERC Starting Grant 2018 for Sven Seuken
   UZH Department of Informatics News Entry, August 9, 2018

3. Four Million Euros Awarded to Three UZH Researchers
   University of Zurich Press Release, August 9, 2018

4. Forschungsprojekt zu maschinellem Lernen erhält EU-Fördergeld
   Horizont, August 9, 2018

5. Vier Schweizer ICT-Projekte erhalten Millionenförderung der EU
   Netzwoche, August 14, 2018