TY - GEN
T1 - Quickly Determining Who Won an Election
AU - Hellerstein, Lisa
AU - Liu, Naifeng
AU - Schewior, Kevin
N1 - Publisher Copyright:
© 2024 Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing. All rights reserved.
PY - 2024/1
Y1 - 2024/1
N2 - 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.
AB - 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.
KW - approximation algorithms
KW - stochastic function evaluation
KW - voting
UR - http://www.scopus.com/inward/record.url?scp=85184149843&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85184149843&partnerID=8YFLogxK
U2 - 10.4230/LIPIcs.ITCS.2024.61
DO - 10.4230/LIPIcs.ITCS.2024.61
M3 - Conference contribution
AN - SCOPUS:85184149843
T3 - Leibniz International Proceedings in Informatics, LIPIcs
BT - 15th Innovations in Theoretical Computer Science Conference, ITCS 2024
A2 - Guruswami, Venkatesan
PB - Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing
T2 - 15th Innovations in Theoretical Computer Science Conference, ITCS 2024
Y2 - 30 January 2024 through 2 February 2024
ER -