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Designing pandemic-resilient voting systems

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  • Schmidt, Adam
  • Albert, Laura A.

Abstract

The 2020 general election occurred while many parts of the nation were under emergency orders related to the COVID-19 pandemic. This led to new requirements and considerations for voting systems. We introduce a model of the voting process to capture pandemic-related changes. Using a discrete event simulation case study of Milwaukee, WI, we study how to design in-person voting systems whose performance are robust to pandemic conditions, such as protective measures implemented during the COVID-19 pandemic. We assess various voting system designs on the voter wait times, voter sojourn times, line lengths at polling locations, voter time spent inside, and the number of voters inside. The analysis indicates that poll worker shortages, social distancing, and personalized protective equipment usage and sanitation measures can lead to extremely long voter wait times. We consider several design choices for mitigating the impact of pandemic-related changes on voting metrics. The case study suggests that long wait times can be avoided by staffing additional check-in locations, expanding early voting, and avoiding consolidated polling locations. Additionally, the analysis suggests that implementing a priority queue discipline has the potential to reduce waiting times for vulnerable populations at increased susceptibility to health risks associated with in-person voting.

Suggested Citation

  • Schmidt, Adam & Albert, Laura A., 2022. "Designing pandemic-resilient voting systems," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
    • Handle: RePEc:eee:soceps:v:80:y:2022:i:c:s003801212100166x
    DOI: 10.1016/j.seps.2021.101174
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    References listed on IDEAS

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    1. Enrico Cantoni, 2020. "A Precinct Too Far: Turnout and Voting Costs," American Economic Journal: Applied Economics, American Economic Association, vol. 12(1), pages 61-85, January.
    2. Yang, Muer & Wang, Xinfang (Jocelyn) & Xu, Nuo, 2015. "A robust voting machine allocation model to reduce extreme waiting," Omega, Elsevier, vol. 57(PB), pages 230-237.
    3. Muer Yang & Michael J. Fry & W. David Kelton & Theodore T. Allen, 2014. "Improving Voting Systems through Service-Operations Management," Production and Operations Management, Production and Operations Management Society, vol. 23(7), pages 1083-1097, July.
    4. Muer Yang & Theodore Allen & Michael Fry & W. Kelton, 2013. "The call for equity: simulation optimization models to minimize the range of waiting times," IISE Transactions, Taylor & Francis Journals, vol. 45(7), pages 781-795.
    5. Xinfang (Jocelyn) Wang & Muer Yang & Michael J Fry, 2015. "Efficiency and equity tradeoffs in voting machine allocation problems," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(8), pages 1363-1369, August.
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    Cited by:

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    3. Huiping Sun & Jianghua Zhang, 2025. "Joint decisions for hospital admissions and horizontal medical resource transfer against capacity shortage in the early stage of pandemics," Health Care Management Science, Springer, vol. 28(4), pages 644-671, December.

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