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dc.contributor.advisor Byrne, Michael D.
dc.creatorWang, Xianni
dc.date.accessioned 2020-09-22T18:12:13Z
dc.date.available 2020-09-22T18:12:13Z
dc.date.created 2020-08
dc.date.issued 2020-09-16
dc.date.submitted August 2020
dc.identifier.citation Wang, Xianni. "Computational Modeling Reveals How Navigation Strategy and Ballot Layout Lead to Voter Error." (2020) Master’s Thesis, Rice University. https://hdl.handle.net/1911/109363.
dc.identifier.urihttps://hdl.handle.net/1911/109363
dc.description.abstract Bad ballot design has affected the outcome of multiple elections in the United States. In order to build an automated tool for evaluation of ballots for potential usability problems, a range of voting behaviors on different ballot layouts have to be understood and modeled. The current studies are focussed on full-face paper ballots. Study 1 is an eye-tracking study. The ways that voters seek information on a full-face paper ballot was examined and the insights from the analysis results were integrated into Study 2. Study 2 is a cognitive modeling study. A family of 160 voting strategies were modeled using ACT-R to investigate how errors arise from the interaction of strategy and ballot design. The model was then validated by testing on a well-known bad ballot: the ballot from Kewaunee County, Wisconsin 2002. The Wisconsin error was reproduced successfully.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.subjectvoting
ballot layout
usability
ACT-R
computational modeling
dc.title Computational Modeling Reveals How Navigation Strategy and Ballot Layout Lead to Voter Error
dc.type Thesis
dc.date.updated 2020-09-22T18:12:13Z
dc.type.material Text
thesis.degree.department Psychology
thesis.degree.discipline Social Sciences
thesis.degree.grantor Rice University
thesis.degree.level Masters
thesis.degree.name Master of Arts
thesis.degree.major Human Computer Interaction


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