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Algorithms for Sustainable Group Decision Making

funded by the Austrian Science Fund (FWF) under grant P31890



Goal of the Project

Group decision making (GDM) is a central activity of human interaction, and also an increasingly important issue in computer science with applications such as multi-agent systems, preference aggregation in artificial intelligence, e-democracy platforms, and group recommender systems. This research project is situated in the field of computational social choice (COMSOC), which is concerned with the study of GDM from a computational point of view. The main focus of this project lies on sustainable, long-term, algorithm-supported GDM within small or medium-sized groups that decide about a varying range of topics.

Instead of considering decisions as singular events, we want to take the history of past group decisions into account. This novel viewpoint allows us to tackle a fundamental issue of GDM: how to avoid situations where frustrated participants drop out of the decision making process. For example, if a minority has been repeatedly overruled in the past, it might withdraw from the decision process. To overcome this problem, we will introduce and analyze perpetual voting rules: these are voting rules that take the history of previous decisions into account and guarantee fairness across this history.

The overall goal of this project is to find GDM algorithms that foster participation and make the GDM process long-term sustainable. To achieve this goal, we will use a large array of methods established in computational social choice. The outcome of this project contributes to a future in which GDM apps and e-democracy systems have "COMSOC inside", and hence make use of the theoretical and experimental expertise established in this field. The perspectives of the proposed research—long-term GDM, compromise, and reduced information requirements—are essential ingredients for sustaining GDM processes.

Project Team

Principal investigator

Current project staff

Former project staff

  • Stefan Forster (student assistant)
  • Benjamin Krenn (student assistant)

Project partners


First Vienna Workshop in Computational Social Choice


PI Martin Lackner and team member Jan Maly co-organized the First Vienna Workshop in Computational Social Choice, with talks form Christian Klamler, Martin Lackner, Jérôme Lang, Jiehua Chen and Thekla Hamm.

For more information see the website of the workshop.

Jan Maly successfully defends his PhD thesis!

Jan Maly presents his thesis

We congratulate Jan to the successfull defense of his PhD thesis Ranking Sets of Objects – How to Deal with Impossibility Results. Well done, Dr. Maly! :-)

The thesis was reviewed by Ulle Endriss (University of Amsterdam) and Jérôme Lang (CNRS, Paris), who formed the thesis committee together with Christian Klamler (Universität Graz) as national reviewer.

AAAI 2020: Three Accepted papers

Martin Lackner presenting at AAAI2020

We are delighted to announce that three papers by team members have been accepted for presentation at the AAAI 2020 conference in New York.

Team members Martin Lackner and Jan Maly were able to attend the conference and present their work in oral presentations.

New members joined the team


Jan Maly and Benjamin Krenn joined the project. Welcome!

The project "Algorithms for Sustainable Group Decision Making" started


The project "Algorithms for Sustainable Group Decision Making" started in Feburary 2019. Its projected duration is 3 years and 3 months.


Here, we provide a comprehensive list of all project publications.


  1. Jan Maly, Stefan Woltran Michael Bernreiter Encoding Choice Logics in ASP In ASPOCP 2020, Ceur-WS, 2020.
    [ BibTeX | pdf  ]
  2. Martin Lackner and Piotr Skowron Utilitarian Welfare and Representation Guarantees of Approval-Based Multiwinner Rules In Artificial Intelligence, 288: 103366, 2020.
    [ BibTeX | pdf  ]
  3. Dominik Peters and Martin Lackner Preferences Single-Peaked on a Circle In Journal of Artificial Intelligence Research (JAIR), 68: 463-502, 2020.
    [ BibTeX | pdf  ]
  4. Zack Fitzsimmons and Martin Lackner Incomplete Preferences in Single-Peaked Electorates In Journal of Artificial Intelligence Research (JAIR), 67: 797-833, 2020.
    [ BibTeX | pdf  ]
  5. Robert Bredereck, Piotr Faliszewski, Michal Furdyna, Andrzej Kaczmarczyk and Martin Lackner Strategic Campaign Management in Apportionment Elections In Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI 2020),, 2020.
    [ BibTeX | pdf  ]
  6. Martin Lackner Perpetual Voting: Fairness in Long-Term Decision Making In Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI 2020), AAAI Press, 2020.
    [ BibTeX | pdf  ]
  7. Adrian Haret, Martin Lackner, Andreas Pfandler and Johannes P. Wallner Proportional Belief Merging In Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI 2020), AAAI Press, 2020.
    [ BibTeX | pdf  ]
  8. Jan Maly Lifting Preferences over Alternatives to Preferences over Sets of Alternatives: The Complexity of Recognizing Desirable Families of Sets In Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI 2020), AAAI Press, 2020.
    [ BibTeX | pdf  ]


  1. Martin Lackner and Piotr Skowron A Quantitative Analysis of Multi-Winner Rules In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI 2019),, 2019.
    [ BibTeX | pdf  ]
  2. Clemens Gangl, Jan Maly, Martin Lackner and Stefan Woltran Aggregating Expert Opinions in Support of Medical Diagnostic Decision-Making In Proceedings of the 11th International Workshop on Knowledge Representation for Health Care (KR4HC-2019), 2019.
    [ BibTeX | pdf  ]

Tools, Code and Data

Last updated: 2020-11-11 10:12

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