Quickly Determining Who Won an Election

Lisa Hellerstein*, Naifeng Liu*, Kevin Schewior*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

This paper considers elections in which voters choose one candidate each, independently according to known probability distributions. A candidate receiving a strict majority (absolute or relative, depending on the version) wins. After the voters have made their choices, each vote can be inspected to determine which candidate received that vote. The time (or cost) to inspect each of the votes is known in advance. The task is to (possibly adaptively) determine the order in which to inspect the votes, so as to minimize the expected time to determine which candidate has won the election. We design polynomial-Time constant-factor approximation algorithms for both the absolute-majority and the relative-majority version. Both algorithms are based on a two-phase approach. In the first phase, the algorithms reduce the number of relevant candidates to O(1), and in the second phase they utilize techniques from the literature on stochastic function evaluation to handle the remaining candidates. In the case of absolute majority, we show that the same can be achieved with only two rounds of adaptivity.

Original languageEnglish
Title of host publication15th Innovations in Theoretical Computer Science Conference, ITCS 2024
EditorsVenkatesan Guruswami
PublisherSchloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing
Publication dateJan 2024
Article number61
ISBN (Electronic)9783959773096
DOIs
Publication statusPublished - Jan 2024
Event15th Innovations in Theoretical Computer Science Conference, ITCS 2024 - Berkeley, United States
Duration: 30. Jan 20242. Feb 2024

Conference

Conference15th Innovations in Theoretical Computer Science Conference, ITCS 2024
Country/TerritoryUnited States
CityBerkeley
Period30/01/202402/02/2024
SeriesLeibniz International Proceedings in Informatics, LIPIcs
Volume287
ISSN1868-8969

Bibliographical note

Publisher Copyright:
© 2024 Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing. All rights reserved.

Keywords

  • approximation algorithms
  • stochastic function evaluation
  • voting

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