19. – 24. Juli 2020, Dagstuhl-Seminar 20301

RESCHEDULED Matching Under Preferences: Theory and Practice

Due to the Covid-19 pandemic, this seminar was rescheduled to 25. – 30. Juli 2021Seminar 21301.


Haris Aziz (UNSW – Sydney, AU)
Péter Biró (Hungarian Academy of Sciences – Budapest, HU)
Tamás Fleiner (Budapest University of Technology & Economics, HU)
Bettina Klaus (University of Lausanne, CH)
David Manlove (University of Glasgow, GB)

Auskunft zu diesem Dagstuhl-Seminar erteilt

Dagstuhl Service Team


Matching under preferences has witnessed remarkable growth as a field that spans multiple disciplines. The research field’s depth and impact was recognized when the 2012 Nobel Prize in Economic Sciences was awarded to Shapley and Roth for their work on the theory of stable allocations and the practice of market design. Most of the developments in the field have taken place within at least three communities:

  • ECON (Economics): Game Theory, Mechanism Design, and Market Design are the main subfields of economics, where matching market research is deeply studied.
  • CS (Computer Science): Theoretical Computer Science (TCS) and Artificial Intelligence (AI) are the subfields that have been home to many new developments on matching under preferences. The research interface between economics and CS has led to several sub-disciplines and communities, including economics and computation, algorithmic game theory, and computational social choice.
  • OR (Operations Research): From among mathematicians working in OR, research continues on the solvability and structure of matching problems under preferences using the theories of graphs, matroids, and lattices. The field is also witnessing developments within the optimization community that tackles hard matching problems with integer linear programming, SAT-solving, and other optimization techniques.

Currently there are several approaches and perspectives towards matching under preferences. These include (1) design of algorithms and understanding the computational complexity of this class of matching problems; (2) formalizing and understanding desirable axioms and properties of matching methods and outcomes; (3) strategic analysis, and mechanism design approaches to matching under preferences; (4) the use of machine learning to find appropriate matches. Approach (1) is largely considered in the CS and OR communities. Approaches (2) and (3) were initiated and are primarily considered by the economic theory community. Approach (4) is mainly considered in CS/AI, but preference estimation is also a classical research topic in ECON, and there have been solutions.

The next decade will witness further developments in the theory and applications of matching under preferences. In this context, it is important to exploit the synergies of the various research communities working on the same research topic from different angles. The specific goal of the seminar is to identify new challenges and opportunities in both the theoretical foundations as well as the applications of matching markets. This Dagstuhl Seminar will set up new links between novel applications and theoretical research which will provide researchers with new research directions.

Topics that the seminar will focus on include (but will not be limited to): (1) matching markets with distributional constraints; (2) probabilistic and fractional matching; (3) matching in online and dynamic settings; and (4) matching markets and machine learning. Other relevant topics may emerge through suggestions from the participants of the seminar.

The seminar will comprise survey lectures and presentations of recent results to outline the current research frontier in the mornings and interdisciplinary working groups that will work together in the afternoons. Towards the end of the week, there will be an opportunity for working groups to informally report back on their findings.

Motivation text license
  Creative Commons BY 3.0 DE
  Haris Aziz, Péter Biró, Tamás Fleiner, Bettina Klaus, and David Manlove

Related Dagstuhl-Seminar


  • Artificial Intelligence / Robotics
  • Data Structures / Algorithms / Complexity
  • Society / Human-computer Interaction


  • Matching under preferences
  • Market design
  • Organ exchange
  • Stable matching


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