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Risk-Limiting Audits for Condorcet Elections

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Financial Cryptography and Data Security. FC 2023 International Workshops (FC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13953))

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Abstract

Elections where electors rank the candidates (or a subset of the candidates) in order of preference allow the collection of more information about the electors’ intent. The most widely used election of this type is Instant-Runoff Voting (IRV), where candidates are eliminated one by one, until a single candidate holds the majority of the remaining ballots. Condorcet elections treat the election as a set of simultaneous decisions about each pair of candidates. The Condorcet winner is the candidate who beats all others in these pairwise contests. There are various proposals to determine a winner if no Condorcet winner exists. In this paper we show how we can efficiently audit Condorcet elections for a number of variations. We also compare the audit efficiency (how many ballots we expect to sample) of IRV and Condorcet elections.

This work was supported by the University of Melbourne’s Research Computing Services and the Petascale Campus Initiative; and by the Australian Research Council (Discovery Project DP220101012; OPTIMA ITTC IC200100009).

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Notes

  1. 1.

    www.preflib.org, accessed 14 Mar 2023.

  2. 2.

    Ties are also possible, but very rare for elections with many ballots.

  3. 3.

    https://en.wikipedia.org/wiki/Minimax_Condorcet_method, accessed 14 Mar 2023.

  4. 4.

    https://en.wikipedia.org/wiki/Smith_set, accessed 14 Mar 2023.

References

  1. Blom, M., et al.: Assertion-based approaches to auditing complex elections, with application to party-list proportional elections. In: Krimmer, R., et al. (eds.) E-Vote-ID 2021. LNCS, vol. 12900, pp. 47–62. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-86942-7_4, Preprint: https://arxiv.org/abs/2107.11903arXiv:2107.11903

  2. Blom, M., Stuckey, P.J., Teague, V.: RAIRE: risk-limiting audits for IRV elections (2019). https://arxiv.org/abs/1903.08804arXiv:1903.08804

  3. Stark, P.B.: Sets of half-average nulls generate risk-limiting audits: SHANGRLA. In: Bernhard, M., et al. (eds.) FC 2020. LNCS, vol. 12063, pp. 319–336. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-54455-3_23, Preprint: https://arxiv.org/abs/1911.10035arXiv:1911.10035

  4. Tideman, T.N.: Independence of clones as a criterion for voting rules. Soc. Choice Welf. 4(3), 185–206 (1987)

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Author information

Authors and Affiliations

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

    Michelle Blom

  2. Department of Data Science and AI, Monash University, Clayton, Australia

    Peter J. Stuckey

  3. Thinking Cybersecurity Pty. Ltd., Melbourne, Australia

    Vanessa Teague

  4. Australian National University, Canberra, Australia

    Vanessa Teague

  5. Department of Econometrics and Business Statistics, Monash University, Clayton, Australia

    Damjan Vukcevic

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

Correspondence to Michelle Blom .

Editor information

Editors and Affiliations

  1. Western University, London, ON, Canada

    Aleksander Essex

  2. Georgetown University, Washington, DC, USA

    Shin'ichiro Matsuo

  3. IT University of Copenhagen, Copenhagen, Denmark

    Oksana Kulyk

  4. Imperial College London, London, UK

    Lewis Gudgeon

  5. Imperial College London, London, UK

    Ariah Klages-Mundt

  6. Imperial College London, London, UK

    Daniel Perez

  7. Imperial College London, London, UK

    Sam Werner

  8. Stirling University, Stirling, UK

    Andrea Bracciali

  9. University College London, London, UK

    Geoff Goodell

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© 2024 International Financial Cryptography Association

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Blom, M., Stuckey, P.J., Teague, V., Vukcevic, D. (2024). Risk-Limiting Audits for Condorcet Elections. In: Essex, A., et al. Financial Cryptography and Data Security. FC 2023 International Workshops. FC 2023. Lecture Notes in Computer Science, vol 13953. Springer, Cham. https://doi.org/10.1007/978-3-031-48806-1_6

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  • DOI: https://doi.org/10.1007/978-3-031-48806-1_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-48805-4

  • Online ISBN: 978-3-031-48806-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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