Lower Bounds on Kemeny Rank Aggregation with Non-Strict Rankings

Sina Akbari, Adolfo R. Escobedo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish (US)
Title of host publication2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728190488
DOIs
StatePublished - 2021
Event2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Orlando, United States
Duration: Dec 5 2021Dec 7 2021

Publication series

Name2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings

Conference

Conference2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021
Country/TerritoryUnited States
CityOrlando
Period12/5/2112/7/21

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

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