Improving the Computational Efficiency of Adaptive Audits of IRV Elections

TL;DR

This paper enhances the AWAIRE method for risk-limiting audits of IRV elections, significantly improving its efficiency and scalability to handle contests with up to 55 candidates through algorithmic innovations and optimized testing strategies.

Contribution

The authors introduce three key improvements to the AWAIRE algorithm, enabling practical auditing of large IRV elections with many candidates.

Findings

Audit sample size varies with margins and parameters.

Algorithm can handle up to 55 candidates in simulations.

Open-source implementation available for practical use.

Abstract

AWAIRE is one of two extant methods for conducting risk-limiting audits of instant-runoff voting (IRV) elections. In principle AWAIRE can audit IRV contests with any number of candidates, but the original implementation incurred memory and computation costs that grew superexponentially with the number of candidates. This paper improves the algorithmic implementation of AWAIRE in three ways that make it practical to audit IRV contests with 55 candidates, compared to the previous 6 candidates. First, rather than trying from the start to rule out all candidate elimination orders that produce a different winner, the algorithm starts by considering only the final round, testing statistically whether each candidate could have won that round. For those candidates who cannot be ruled out at that stage, it expands to consider earlier and earlier rounds until either it provides strong evidence that the reported winner really won or a full hand count is conducted, revealing who really won. Second, it tests a richer collection of conditions, some of which can rule out many elimination orders at once. Third, it exploits relationships among those conditions, allowing it to abandon testing those that are unlikely to help. We provide real-world examples with up to 36 candidates and synthetic examples with up to 55 candidates, showing how audit sample size depends on the margins and on the tuning parameters. An open-source Python implementation is publicly available.