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 language | English |
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Title of host publication | Electronic Voting |
Subtitle of host publication | 5th International Joint Conference, E-Vote-ID 2020 Proceedings |
Editors | Robert Krimmer, Melanie Volkamer, Bernhard Beckert, Ralf Küsters, Oksana Kulyk, David Duenas-Cid, Mikhel Solvak |
Place of Publication | Cham Switzerland |
Publisher | Springer |
Pages | 112-128 |
Number of pages | 17 |
Edition | 1st |
ISBN (Electronic) | 9783030603472 |
ISBN (Print) | 9783030603465 |
DOIs | |
Publication status | Published - 2020 |
Externally published | Yes |
Event | International Joint Conference on Electronic Voting 2020 - Bregenz, Austria Duration: 6 Oct 2020 → 9 Oct 2020 Conference number: 5th https://e-vote-id.org/e-vote-id-2020/ |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 12455 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | International Joint Conference on Electronic Voting 2020 |
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Abbreviated title | EVOTE 2020 |
Country/Territory | Austria |
City | Bregenz |
Period | 6/10/20 → 9/10/20 |
Internet address |
Keywords
- Bayesian
- Risk-limiting
- Statistical audit