Election Control by Manipulating Issue Significance

Andrew Estornell, Sanmay Das, Edith Elkind, Yevgeniy Vorobeychik

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations

Abstract

Integrity of elections is vital to democratic systems, but it is frequently threatened by malicious actors. The study of algorithmic complexity of the problem of manipulating election outcomes by changing its structural features is known as election control. One means of election control that has been proposed is to select a subset of issues that determine voter preferences over candidates. We study a variation of this model in which voters have judgments about relative importance of issues, and a malicious actor can manipulate these judgments. We show that computing effective manipulations in this model is NP-hard even with two candidates or binary issues. However, we demonstrate that the problem is tractable with a constant number of voters or issues. Additionally, while it remains intractable when voters can vote stochastically, we exhibit an important special case in which stochastic voting enables tractable manipulation.

Original languageEnglish (US)
Pages (from-to)340-349
Number of pages10
JournalProceedings of Machine Learning Research
Volume124
StatePublished - 2020
Event36th Conference on Uncertainty in Artificial Intelligence, UAI 2020 - Virtual, Online
Duration: Aug 3 2020Aug 6 2020

Funding

This research was partially supported by the NSF (IIS-1903207, IIS-1910392), and ARO (W911NF1910241).

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering
  • Statistics and Probability

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