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Ballot-Polling Risk Limiting Audits for IRV Elections

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Electronic Voting (E-Vote-ID 2018)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 11143))

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Abstract

Risk-limiting post election audits guarantee a high probability of correcting incorrect election results, independent of why the result was incorrect. Ballot-polling audits select ballots at random and interpret those ballots as evidence for and against the actual recorded result, continuing this process until either they support the recorded result, or they fall back to a full manual recount. Ballot-polling for first-past-the-post elections is well understood, and used in some US elections. We define a number of approaches to ballot-polling risk-limiting audits for Instant Runoff Voting (IRV) elections. We show that for almost all real elections we found, we can perform a risk-limiting audit by looking at only a small fraction of the total ballots (assuming no errors).

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Notes

  1. 1.

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Authors and Affiliations

  1. School of Computing and Information Systems, The University of Melbourne, Parkville, Australia

    Michelle Blom, Peter J. Stuckey & Vanessa J. Teague

Authors
  1. Michelle Blom
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  2. Peter J. Stuckey
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  3. Vanessa J. Teague
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Corresponding author

Correspondence to Michelle Blom .

Editor information

Editors and Affiliations

  1. Tallinn University of Technology, Tallinn, Estonia

    Robert Krimmer

  2. Karlsruhe Institute of Technology, Karlsruhe, Germany

    Melanie Volkamer

  3. LORIA, Vandoeuvre-lès-Nancy Cedex, France

    Véronique Cortier

  4. Australian National University, Canberra, ACT, Australia

    Rajeev Goré

  5. University of New South Wales, Canberra, ACT, Australia

    Manik Hapsara

  6. Ritsumeikan University, Kusatsu, Shiga, Japan

    Uwe Serdült

  7. Tallinn University of Technology, Tallinn, Estonia

    David Duenas-Cid

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Blom, M., Stuckey, P.J., Teague, V.J. (2018). Ballot-Polling Risk Limiting Audits for IRV Elections. In: Krimmer, R., et al. Electronic Voting. E-Vote-ID 2018. Lecture Notes in Computer Science(), vol 11143. Springer, Cham. https://doi.org/10.1007/978-3-030-00419-4_2

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  • DOI: https://doi.org/10.1007/978-3-030-00419-4_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00418-7

  • Online ISBN: 978-3-030-00419-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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