Ballot-Polling Audits of Instant-Runoff Voting Elections with a Dirichlet-Tree Model

作     者：Everest, Floyd Blom, Michelle Stark, Philip B. Stuckey, Peter J. Teague, Vanessa Vukcevic, Damjan 

作者机构：School of Mathematics and Statistics University of Melbourne Parkville Australia School of Computing and Information Systems University of Melbourne Parkville Australia Department of Statistics University of California BerkeleyCA United States Department of Data Science and AI Monash University Clayton Australia Thinking Cybersecurity Pty. Ltd. Melbourne Australia The Australian National University Canberra Australia Melbourne Integrative Genomics University of Melbourne Parkville Australia 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2022年

核心收录：

主　　题：Runoff 

摘      要：Instant-runoff voting (IRV) is used in several countries around the world. It requires voters to rank candidates in order of preference, and uses a counting algorithm that is more complex than systems such as first-past-the-post or scoring rules. An even more complex system, the single transferable vote (STV), is used when multiple candidates need to be elected. The complexity of these systems has made it difficult to audit the election outcomes. There is currently no known risk-limiting audit (RLA) method for STV, other than a full manual count of the ballots. A new approach to auditing these systems was recently proposed, based on a Dirichlet-tree model. We present a detailed analysis of this approach for ballot-polling Bayesian audits of IRV elections. We compared several choices for the prior distribution, including some approaches using a Bayesian bootstrap (equivalent to an improper prior). Our findings include that the bootstrap-based approaches can be adapted to perform similarly to a full Bayesian model in practice, and that an overly informative prior can give counter-intuitive results. Via carefully chosen examples, we show why creating an RLA with this model is challenging, but we also suggest ways to overcome this. As well as providing a practical and computationally feasible implementation of a Bayesian IRV audit, our work is important in laying the foundation for an RLA for STV elections. Copyright © 2022, The Authors. All rights reserved.