A unified evaluation of two-candidate ballot-polling election auditing methods

Zhuoqun Huang, Ronald L. Rivest, Philip B. Stark, Vanessa J. Teague, Damjan Vukcevic

Research output: Chapter in Book/Report/Conference proceedingConference PaperOtherpeer-review

4 Citations (Scopus)

Abstract

Counting votes is complex and error-prone. Several statistical methods have been developed to assess election accuracy by manually inspecting randomly selected physical ballots. Two ‘principled’ methods are risk-limiting audits (RLAs) and Bayesian audits (BAs). RLAs use frequentist statistical inference while BAs are based on Bayesian inference. Until recently, the two have been thought of as fundamentally different. We present results that unify and shed light upon ‘ballot-polling’ RLAs and BAs (which only require the ability to sample uniformly at random from all cast ballot cards) for two-candidate plurality contests, that are building blocks for auditing more complex social choice functions, including some preferential voting systems. We highlight the connections between the methods and explore their performance. First, building on a previous demonstration of the mathematical equivalence of classical and Bayesian approaches, we show that BAs, suitably calibrated, are risk-limiting. Second, we compare the efficiency of the methods across a wide range of contest sizes and margins, focusing on the distribution of sample sizes required to attain a given risk limit. Third, we outline several ways to improve performance and show how the mathematical equivalence explains the improvements.

Original languageEnglish
Title of host publicationElectronic Voting
Subtitle of host publication5th International Joint Conference, E-Vote-ID 2020 Proceedings
EditorsRobert Krimmer, Melanie Volkamer, Bernhard Beckert, Ralf Küsters, Oksana Kulyk, David Duenas-Cid, Mikhel Solvak
Place of PublicationCham Switzerland
PublisherSpringer
Pages112-128
Number of pages17
Edition1st
ISBN (Electronic)9783030603472
ISBN (Print)9783030603465
DOIs
Publication statusPublished - 2020
Externally publishedYes
EventInternational Joint Conference on Electronic Voting 2020 - Bregenz, Austria
Duration: 6 Oct 20209 Oct 2020
Conference number: 5th
https://e-vote-id.org/e-vote-id-2020/

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12455 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Joint Conference on Electronic Voting 2020
Abbreviated titleEVOTE 2020
Country/TerritoryAustria
CityBregenz
Period6/10/209/10/20
Internet address

Keywords

  • Bayesian
  • Risk-limiting
  • Statistical audit

Cite this