2023/03/16 by Jacob V. Spertus, Spertus, Jacob · 1 citation
Computer Science · Economics, Econometrics and Finance · #Applications (stat.AP) #FOS: Computer and information sciences #Game Theory and Voting Systems #Internet Traffic Analysis and Secure E-voting #Methodology (stat.ME) #Sports Analytics and Performance
paper · pdf · doi:10.48550/arxiv.2304.01010
openalex publication_date 2023/03/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Risk-limiting audits (RLAs) can provide routine, affirmative evidence that reported election outcomes are correct by checking a random sample of cast ballots. An efficient RLA requires checking relatively few ballots. Here we construct highly efficient RLAs by optimizing supermartingale tuning parameters--bets--for ballot-level comparison audits. The exactly optimal bets depend on the true rate of errors in cast-vote records (CVRs)--digital receipts detailing how machines tabulated each ballot. We evaluate theoretical and simulated workloads for audits of contests with a range of diluted margins and CVR error rates. Compared to bets recommended in past work, using these optimal bets can dramatically reduce expected workloads--by 93% on average over our simulated audits. Because the exactly optimal bets are unknown in practice, we offer some strategies for approximating them. As with the ballot-polling RLAs described in ALPHA and RiLACs, adapting bets to previously sampled data or diversifying them over a range of suspected error rates can lead to substantially more efficient audits than fixing bets to a priori values, especially when those values are far from correct. We sketch extensions to other designs and social choice functions, and conclude with some recommendations for real-world comparison audits.