15. – 20. September 2019, Dagstuhl-Seminar 19381

Application-Oriented Computational Social Choice


Umberto Grandi (University Toulouse Capitole, FR)
Stefan Napel (Universität Bayreuth, DE)
Rolf Niedermeier (TU Berlin, DE)
Kristen Brent Venable (IHMC – Pensacola, US)

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Computational social choice (COMSOC) combines models from political science and economics with techniques from computer science, to analyze collective decision processes from a computational perspective. Classical contributions include the study of the computational barriers to various forms of manipulation in elections, the definition of novel procedures for distributed resources among a group of human or artificial agents, as well as the study of complex collective decisions such as multi-winner voting rules and voting in combinatorial domains. COMSOC is a thriving field of research, with an international bi-annual workshop now at its 7th edition and a handbook published in 2016 which structures more than a decade of research, but future success will depend on the practical applicability of its findings. The purpose of this seminar is to address this challenge by stimulating application-driven research in computational social choice, i.e., theoretical studies modeling existing practical problems in all their complexity.

The seminar shall set up a fruitful feedback loop between practical applications and theoretical research, feeding researchers with novel research problems. It aims to open up the community to players from industry, policy-making, and startup environments. The seminar provides a platform to meet and to stimulate the debate among researchers on how to reach out to these sectors. Four areas of COMSOC, which have already proven or bear particular potential for synergies and applicability to real-life problems, will be in the focus. Namely, the seminar attracts researchers and meaningful contributions on group recommendation systems, fair allocation, interactive democracy, and electoral systems.

Each of these areas addresses present-day challenges that provide an opportunity for an interdisciplinary approach building on contributions from computer scientists, economists, mathematicians, and political scientists:

  • Recommender systems is a very successful application that combines several artificial intelligence techniques. Indeed, there have been few other examples of autonomous reasoning tools with comparable impact and pervasiveness in practice. The seminar aims at producing a detailed picture of the state of the art, identify gaps and put forward ideas to fully exploit the synergy between classical social choice techniques and modern recommender systems.
  • Fair division has already proven a successful testbed for the application of theoretical work, thanks for the recently launched Spliddit webpage, which provides a user-friendly implementation for a number of algorithms in this field. This experience poses a number of questions and challenges for application-oriented research in fair division and beyond, such as data collection and analysis, possibly leading to new theoretical problems.
  • Interactive democracy comprises a variety of approaches to make democratic processes more engaging and responsive. For instance, successful design and implementation of online decision platforms presents a multidisciplinary research challenge. The seminar will explore how voting theory can be employed to aid decision platforms and other tools to deal with challenges like providing minority protection, budget constraints, or avoiding cycles in delegated voting.
  • Real electoral systems often have features that are absent in the single or multi-winner systems analyzed in textbooks and scientific papers. Voting theory and computational methods can help to identify non-monotonicity problems of real electoral systems, to provide normative benchmarks for institutional design, and to conduct influence and performance comparisons of different voting arrangements.

Motivation text license
  Creative Commons BY 3.0 DE
  Umberto Grandi, Stefan Napel, Rolf Niedermeier, and Kristen Brent Venable


  • Artificial Intelligence / Robotics
  • Data Structures / Algorithms / Complexity


  • Social Choice
  • Multi-Agent Systems
  • AI for the Social Good
  • Collective Decision Making


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