Quickly Determining Who Won an Election

Lisa Hellerstein, Naifeng Liu, Kevin Schewior

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


    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 (US)
    Title of host publication15th Innovations in Theoretical Computer Science Conference, ITCS 2024
    EditorsVenkatesan Guruswami
    PublisherSchloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing
    ISBN (Electronic)9783959773096
    StatePublished - Jan 2024
    Event15th Innovations in Theoretical Computer Science Conference, ITCS 2024 - Berkeley, United States
    Duration: Jan 30 2024Feb 2 2024

    Publication series

    NameLeibniz International Proceedings in Informatics, LIPIcs
    ISSN (Print)1868-8969


    Conference15th Innovations in Theoretical Computer Science Conference, ITCS 2024
    Country/TerritoryUnited States


    • approximation algorithms
    • stochastic function evaluation
    • voting

    ASJC Scopus subject areas

    • Software


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