Abstract
For decades, election research in the United States has focussed on the potentially disenfranchising consequences of evolving election laws and procedures. With the widespread changes and requirements caused by the COVID-19 pandemic, elections have adapted to prevent the spread of the virus. Despite implementing system changes, it is unclear what impact the pandemic had on the voter experience. This paper presents a case study, performed in collaboration with the Rhode Island Board of Elections, to investigate a Rhode Island polling location operated during the 2020 General Election and quantify the effect of COVID-19 mitigating procedures on system performance. To validate the modelling approach, a case study is developed for a Rhode Island polling location using data collected from that location during the 2020 General Election. The validated model is then adapted to create a hypothetical non-COVID-19 system to simulate the polling location pre-COVID-19. Simulated system performance of the COVID-19 and non-COVID-19 models are statistically compared to quantify the impact of COVID-19 mitigation strategies on system performance. This approach may be applied in future work to assist in election preparation for sudden system changes or in response to new election laws.
Original language | English |
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Journal | Journal of Simulation |
DOIs | |
State | Accepted/In press - 2022 |
Bibliographical note
Funding Information:The authors would like to thank the Rhode Island Board of Elections and Rhode Island Department of State for their continued support and collaboration throughout this project. This research was funded in part by Democracy Fund (R-201802-02227; R-201903-03975) and Massachusetts Institute of Technology Election Data and Science Lab (AWD08509; AWD08133) in collaboration with the University of Rhode Island Voter OperaTions and Election Systems (URI VOTES).
Publisher Copyright:
© Operational Research Society 2022.
Keywords
- COVID-19
- Discrete event simulation
- election systems
ASJC Scopus subject areas
- Software
- Modeling and Simulation
- Management Science and Operations Research
- Industrial and Manufacturing Engineering