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Title: Optimal defense against election control by deleting voter groups
Authors: Yin, Yue
Vorobeychik, Yevgeniy
An, Bo
Hazon, Noam
Keywords: Election Control
Protecting Elections
Issue Date: 2018
Source: Yin, Y., Vorobeychik, Y., An, B., & Hazon, N. (2018). Optimal defense against election control by deleting voter groups. Artificial Intelligence, 259, 32-51.
Series/Report no.: Artificial Intelligence
Abstract: Election control encompasses attempts from an external agent to alter the structure of an election in order to change its outcome. This problem is both a fundamental theoretical problem in social choice, and a major practical concern for democratic institutions. Consequently, this issue has received considerable attention, particularly as it pertains to different voting rules. In contrast, the problem of how election control can be prevented or deterred has been largely ignored. We introduce the problem of optimal defense against election control, including destructive and constructive control, where manipulation is allowed at the granularity of groups of voters (e.g., voting locations) through a denial-of-service attack, and the defender allocates limited protection resources to prevent control. We consider plurality voting, and show that it is computationally hard to prevent both types of control, though destructive control itself can be performed in polynomial time. For defense against destructive control, we present a double-oracle framework for computing an optimal prevention strategy. We show that both defender and attacker best response subproblems are NP-complete, and develop exact mixed-integer linear programming approaches for solving these, as well as fast heuristic methods. We then extend this general approach to develop effective algorithmic solutions for defense against constructive control. Finally, we generalize the model and algorithmic approaches to consider uncertainty about voter preferences. Experiments conducted on both synthetic and real data demonstrate that the proposed computational framework can scale to realistic problem instances.
ISSN: 0004-3702
DOI: 10.1016/j.artint.2018.02.001
Rights: © 2018 Elsevier B.V. This is the author created version of a work that has been peer reviewed and accepted for publication by Artificial Intelligence, Elsevier B.V. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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