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Record link: 
http://hdl.handle.net/20.500.12708/58560
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Title: 
Computing Kemeny Rankings from d-Euclidean Preferences
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Citation: 
Hamm, T., Lackner, M., & Rapberger, A. (2021). Computing Kemeny Rankings from d-Euclidean Preferences. In Algorithmic Decision Theory (pp. 147–161). https://doi.org/10.1007/978-3-030-87756-9_10
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Publisher DOI: 
10.1007/978-3-030-87756-9_10
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Publication Type: 
Inproceedings - Full-Paper Contribution
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Authors: 
Hamm, Thekla 
Lackner, Martin 
Rapberger, Anna 
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Organisational Unit: 
E192-01 - Forschungsbereich Algorithms and Complexity 
E192-02 - Forschungsbereich Databases and Artificial Intelligence 
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Published in: 
Algorithmic Decision Theory 
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Date (published): 
2021
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Event name: 
ADT 2021 - Algorithmic Decision Theory - 7th International Conference 
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Event date: 
3-Nov-2021 - 5-Nov-2021
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Event place: 
Toulouse, France
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Number of Pages: 
15
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Peer reviewed: 
Yes
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Keywords: 
Euclidean preferences Kemeny´s voting rule Rank aggregation algorithms Computational complexity
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Abstract: 
Kemeny´s voting rule is a well-known and computationally intractable rank aggregation method. In this work, we propose an algorithm that finds an embeddable Kemeny ranking in d-Euclidean elections. This algorithm achieves a polynomial runtime (for a fixed dimension d) and thus demonstrates the algorithmic usefulness of the d-Euclidean restriction. We further investigate how well embeddable Kemeny rankings approximate optimal (unrestricted) Kemeny rankings.
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Research Areas: 
Logic and Computation: 100%
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Science Branch: 
Informatik
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Appears in Collections:
Conference Paper

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