Selecting Voting Locations for Fun and Profit

Fitzsimmons, Zack; Lev, Omer

doi:10.48550/arxiv.2003.06879

Cited by 1 publication

(3 citation statements)

References 19 publications

(28 reference statements)

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“…The heuristic first identifies a feasible solution to the DBLP corresponding to r = 0. When q = 0, we find a tour visiting the nodes of T using any method for the TSP 15 . When q = 1, the DBLP with r = 0 is equivalent to the CTP when the DBLP objective is reformulated as introduced in Section 4.1.…”

Section: A Heuristic Methodsmentioning

confidence: 99%

“…They find the likelihood of drop box risks to be relatively low compared to other risks (39) . Fitzsimmons and Lev (15) study geographic-based risks by introducing a control problem to study how voter turnout can be manipulated through the strategic selection of polling locations. A few papers attempt to detect disruptions or security incidents following an election (24,1) .…”

Section: Literature Reviewmentioning

confidence: 99%

“…This may be desirable even if r is believed to be known 15. Since we do not specify the methods to solve the TSP or CTP, this heuristic is in fact a heuristic scheme.…”

mentioning

confidence: 99%

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Locating ballot drop boxes

Schmidt¹,

Albert²

2022

Preprint

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For decades, voting-by-mail and the use of ballot drop boxes has substantially grown, and in response, many election officials have added new drop boxes to their voting infrastructure. However, existing guidance for locating drop boxes is limited. In this paper, we introduce an integer programming model, the drop box location problem (DBLP), to locate drop boxes. The DBLP considers criteria of cost, voter access, and risk. The cost of the drop box system is determined by the fixed cost of adding drop boxes and the operational cost of a collection tour by a bipartisan team who regularly collects ballots from selected locations. The DBLP utilizes covering sets to ensure each voter is in close proximity to a drop box and incorporates a novel measure of access to measure the ability to use multiple voting pathways to vote. The DBLP is shown to be NP-Hard, and we introduce a heuristic to generate a large number of feasible solutions for policy makers to select from a posteriori. Using a real-world case study of Milwaukee, WI, we study the benefit of the DBLP. The results demonstrate that the proposed optimization model identifies drop box locations that perform well across multiple criteria. The results also demonstrate that the trade-off between cost, access, and risk is non-trivial, which supports the use of the proposed optimization-based approach to select drop box locations.

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Section: A Heuristic Methodsmentioning

confidence: 99%