a post with bibliography | Sarah M. Morin

This post shows how to add bibliography to simple blog posts. We support every citation style that jekyll-scholar does. That means simple citation like (missing reference), multiple citations like [1,2], long references like Sarah Morin, Grant McClearn, Neal McBurnett, Poorvi L. Vora, and Filip Zagórski. 2020. A Note on Risk-Limiting Bayesian Polling Audits for Two-Candidate Elections. In Fifth Workshop on Advances in Secure Electronic Voting, Financial Cryptography, Financial Cryptography. or also quotes:

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If you would like something more academic, check the distill style post.

References

VOTING 20

A Note on Risk-Limiting Bayesian Polling Audits for Two-Candidate Elections

Sarah Morin, Grant McClearn, Neal McBurnett, Poorvi L. Vora, and Filip Zagórski

In Fifth Workshop on Advances in Secure Electronic Voting, Financial Cryptography, 2020

Abs PDF

This short paper provides a general form for a polling audit that is both Bayesian and risk-limiting: the Bayesian Risk-Limiting (Polling) Audit, which enables the use of a Bayesian approach to explore more efficient Risk-Limiting Audits. A numerical example illustrates the implications to practice.
The Athena Class of Risk-Limiting Ballot Polling Audits

Filip Zagórski, Grant McClearn, Sarah Morin, Neal McBurnett, and Poorvi L. Vora

CoRR, 2021

Abs arXiv PDF

The main risk-limiting ballot polling audit in use today, BRAVO, is designed for use when single ballots are drawn at random and a decision regarding whether to stop the audit or draw another ballot is taken after each ballot draw (ballot-by-ballot (B2) audits). On the other hand, real ballot polling audits draw many ballots in a single round before determining whether to stop (round-by-round (R2) audits). We show that BRAVO results in significant inefficiency when directly applied to real R2 audits. We present the ATHENA class of R2 stopping rules, which we show are risk-limiting if the round schedule is pre-determined (before the audit begins). We prove that each rule is at least as efficient as the corresponding BRAVO stopping rule applied at the end of the round. We have open-source software libraries implementing most of our results.

We show that ATHENA halves the number of ballots required, for all state margins in the 2016 US Presidential election and a first round with 90% stopping probability, when compared to BRAVO (stopping rule applied at the end of the round). We present simulation results supporting the 90% stopping probability claims and our claims for the risk accrued in the first round. Further, ATHENA reduces the number of ballots by more than a quarter for low margins, when compared to the BRAVO stopping rule applied on ballots in selection order. This implies that keeping track of the order when drawing ballots R2 is not beneficial, because ATHENA is more efficient even without information on selection order. These results are significant because current approaches to real ballot polling election audits use the B2 BRAVO rules, requiring about twice as much work on the part of election officials. Applying the rules in selection order requires fewer ballots, but keeping track of the order, and entering it into audit software, adds to the effort.