Lower Bounds on Kemeny Rank Aggregation with Non-Strict Rankings

Sina Akbari; Adolfo R. Escobedo

doi:10.1109/SSCI50451.2021.9660119

Lower Bounds on Kemeny Rank Aggregation with Non-Strict Rankings

Sina Akbari, Adolfo R. Escobedo

Computer Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

Abstract

Rank aggregation has many applications in computer science, operations research, and group decision-making. This paper introduces lower bounds on the Kemeny aggregation problem when the input rankings are non-strict (with and without ties). It generalizes some of the existing lower bounds for strict rankings to the case of non-strict rankings, and it proposes shortcuts for reducing the run time of these techniques. More specifically, we use Condorcet criterion variations and the Branch Cut method to accelerate the lower bounding process.

Original language	English (US)
Title of host publication	2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728190488
DOIs	https://doi.org/10.1109/SSCI50451.2021.9660119
State	Published - 2021
Event	2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Orlando, United States Duration: Dec 5 2021 → Dec 7 2021

Publication series

Name	2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings

Conference

Conference	2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021
Country/Territory	United States
City	Orlando
Period	12/5/21 → 12/7/21

Keywords

Branch cut method
Condorcet Criterion
Group decision-making
Kemeny-Snell distance
Lower bounding techniques
Rank aggregation

ASJC Scopus subject areas

Artificial Intelligence
Computer Science Applications
Decision Sciences (miscellaneous)
Safety, Risk, Reliability and Quality
Control and Optimization

Access to Document

10.1109/SSCI50451.2021.9660119

Cite this

Akbari, S., & Escobedo, A. R. (2021). Lower Bounds on Kemeny Rank Aggregation with Non-Strict Rankings. In 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings (2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSCI50451.2021.9660119

Lower Bounds on Kemeny Rank Aggregation with Non-Strict Rankings. / Akbari, Sina; Escobedo, Adolfo R.

2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2021. (2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Akbari, S & Escobedo, AR 2021, Lower Bounds on Kemeny Rank Aggregation with Non-Strict Rankings. in 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings. 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021, Orlando, United States, 12/5/21. https://doi.org/10.1109/SSCI50451.2021.9660119

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abstract = "Rank aggregation has many applications in computer science, operations research, and group decision-making. This paper introduces lower bounds on the Kemeny aggregation problem when the input rankings are non-strict (with and without ties). It generalizes some of the existing lower bounds for strict rankings to the case of non-strict rankings, and it proposes shortcuts for reducing the run time of these techniques. More specifically, we use Condorcet criterion variations and the Branch Cut method to accelerate the lower bounding process.",

keywords = "Branch cut method, Condorcet Criterion, Group decision-making, Kemeny-Snell distance, Lower bounding techniques, Rank aggregation",

author = "Sina Akbari and Escobedo, {Adolfo R.}",

note = "Funding Information: ACKNOWLEDGMENT The authors gratefully acknowledge funding support from the National Science Foundation (Award 1850355). This research was supported in part through high performance computing resources provided by Arizona State University. Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 ; Conference date: 05-12-2021 Through 07-12-2021",

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Lower Bounds on Kemeny Rank Aggregation with Non-Strict Rankings

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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