Talks and Poster Presentations (with Proceedings-Entry):

T. Csar, M. Lackner, R. Pichler:
"Computing the Schulze Method for Large-Scale Preference Data Sets";
Talk: IJCAI - International Joint Conference on Artificial Intelligence, Stockholm; 2018-07-13 - 2018-07-19; in: "Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, {IJCAI} 2018, July 13-19, 2018, Stockholm, Sweden", ijcai.org, (2018), ISBN: 978-0-9992411-2-7; 180 - 187.

English abstract:
The Schulze method is a voting rule widely used in practice and enjoys many positive axiomatic properties. While it is computable in polynomial time, its straight-forward implementation does not scale well for large elections. In this paper, we develop a highly optimised algorithm for computing the Schulze method with Pregel, a framework for massively parallel computation of graph problems, and demonstrate its applicability for large preference data sets. In addition, our theoretic analysis shows that the Schulze method is indeed particularly well-suited for parallel computation, in stark contrast to the related ranked pairs method. More precisely we show that winner determination subject to the Schulze method is NL-complete, whereas this problem is P-complete for the ranked pairs method.

Agent-based and Multi-agent Systems: Computational Social Choice; Agent-based and Multi-agent Systems: Voting

"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)

Electronic version of the publication:

Related Projects:
Project Head Reinhard Pichler:
Effiziente, parametrisierte Algorithmen in Künstlicher Intelligenz und logischem Schließen

Project Head Reinhard Pichler:

Project Head Stefan Woltran:

Created from the Publication Database of the Vienna University of Technology.