An election audit is risk-limiting if the audit limits (to a pre-specified threshold) the chance that an erroneous electoral outcome will be certified. Extant methods for auditing instant-runoff voting (IRV) elections are either not risk-limiting or require cast vote records (CVRs), the voting system's electronic record of the votes on each ballot. CVRs are not always available, for instance, in jurisdictions that tabulate IRV contests manually. We develop an RLA method (AWAIRE) that uses adaptively weighted averages of test supermartingales to efficiently audit IRV elections when CVRs are not available. The adaptive weighting 'learns' an efficient set of hypotheses to test to confirm the election outcome. When accurate CVRs are available, AWAIRE can use them to increase the efficiency to match the performance of existing methods that require CVRs. We provide an open-source prototype implementation that can handle elections with up to six candidates. Simulations using data from real elections show that AWAIRE is likely to be efficient in practice. We discuss how to extend the computational approach to handle elections with more candidates. Adaptively weighted averages of test supermartingales are a general tool, useful beyond election audits to test collections of hypotheses sequentially while rigorously controlling the familywise error rate.

翻译：选举审计若能将错误选举结果被认证的概率限制在（预设阈值内），则称为风险限制审计。现有针对即时决选投票（IRV）选举的审计方法要么不具备风险限制特性，要么要求获取选票记录（CVR）——即投票系统对每张选票的电子记录。CVR并非总是可用，例如在手动计票的IRV选举选区中。我们提出了一种无需CVR即可高效审计IRV选举的风险限制审计方法（AWAIRE），该方法利用检验超鞅的自适应加权平均。自适应加权能“学习”出需要检验的假设集，从而高效确认选举结果。当存在精确CVR时，AWAIRE可借助其提升效率，达到现有依赖CVR方法的性能水平。我们提供了开源原型实现，可处理最多六名候选人的选举。基于真实选举数据的模拟表明，AWAIRE在实践中具有高效性。我们进一步讨论了如何扩展该计算方法以应对更多候选人的选举场景。检验超鞅的自适应加权平均是一种通用工具，其应用范围不限于选举审计，还可用于在严格控制族系误差率的前提下对假设集合进行序贯检验。