June 25 – 30 , 2017, Dagstuhl Seminar 17261
Voting: Beyond Simple Majorities and Single-Winner Elections
Dorothea Baumeister (Heinrich-Heine-Universität Düsseldorf, DE)
Piotr Faliszewski (AGH University of Science & Technology – Krakow, PL)
Annick Laruelle (University of the Basque Country – Bilbao, ES)
Toby Walsh (TU Berlin, DE)
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Annette Beyer for administrative matters
Roswitha Bardohl for scientific matters
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Computational social choice is an interdisciplinary field of research, focused on computational and algorithmic issues pertaining to aggregating preferences of agents—perhaps self-interested and strategic—and providing them with joint decisions. Computational social choice combines the tools and approaches of social choice theory, computer science (with particular focus on artificial intelligence and theoretical computer science), economics, and operations research. The distinctive feature of computational social choice—as opposed to the classic social choice theory— is that computational considerations (e.g., efficiency of computing outcomes of the preference aggregation processes) are given significant attention. Nonetheless, the two research areas are deeply connected and there is significant interaction between them. The best studied model of (computational) social choice regards single-winner elections. Agents (voters) express their preferences regarding the available candidates (often in the form of rankings, from the most to the least desirable candidate) and then a voting rule (i.e., an appropriate algorithm) specifies the election winner. Due to the fantastic progress in social choice (over the last half a century) and in computational social choice (over the last fifteen years or so), essentially all the stages of the above-described process are quite well studied. However, in the modern world—especially in the era of ubiquitous use of social media—it appears that there is a great range of preference aggregation settings where the classic approach falls short.
The goal of this seminar is to discuss:
- multi-winner elections: parliamentary elections are perhaps the most archetypal example of a multi-winner election, but there are applications far beyond the world of politics.
- multi-issue elections: decisions on a sequence of interdependent issues.
- elections where voters express their preferences in various non-standard ways (ranging from extensions of dichotomous preferences to complex languages allowing one to express condi- tional statements).
- other voting-related settings, including peer selection, and judgement aggregation.
Although voting theory is usually associated with political elections, its possible applications are found in all aspects of collective decision making. Applications in computer science include webpage ranking, online recommendation systems for products and services, and various scheduling tools (such as, e.g., Doodle). Further, there are relevant applications of (multi-winner) voting in the industry (for example finding the best set of products for a given group of clients). A consequence of this diversity of applications is the interdisciplinary approach of the seminar. We intend the seminar to be a place where researchers from various areas of research (including computer science, economics, political science, etc.) can exchange their views, ideas, and experiences regarding these new preference aggregation problems.
This seminar is related to four previous seminars on Computational Social Choice (2007, 2010, 2012, 2015), which contributed to the development of the community.
Creative Commons BY 3.0 DE
Dorothea Baumeister and Piotr Faliszewski and Annick Laruelle and Toby Walsh
Dagstuhl Seminar Series
- 15241: "Computational Social Choice: Theory and Applications" (2015)
- 12101: "Computation and Incentives in Social Choice" (2012)
- 10101: "Computational Foundations of Social Choice" (2010)
- 07431: "Computational Issues in Social Choice " (2007)
- Artificial Intelligence / Robotics
- Data Structures / Algorithms / Complexity
- Modelling / Simulation
- Social Choice
- Artificial Intelligence
- Multi Agent Systems
- Collective Decision Making
- Preference Aggregation
- Preference Elicitation and Preference Learning