Various risk-limiting audit (RLA) methods have been developed for instant-runoff voting (IRV) elections. A recent method, AWAIRE, is the first efficient approach that can take advantage of but does not require cast vote records (CVRs). AWAIRE involves adaptively weighted averages of test statistics, essentially "learning" an effective set of hypotheses to test. However, the initial paper on AWAIRE only examined a few weighting schemes and parameter settings. We explore schemes and settings more extensively, to identify and recommend efficient choices for practice. We focus on the case where CVRs are not available, assessing performance using simulations based on real election data. The most effective schemes are often those that place most or all of the weight on the apparent "best" hypotheses based on already seen data. Conversely, the optimal tuning parameters tended to vary based on the election margin. Nonetheless, we quantify the performance trade-offs for different choices across varying election margins, aiding in selecting the most desirable trade-off if a default option is needed. A limitation of the current AWAIRE implementation is its restriction to a small number of candidates -- up to six in previous implementations. One path to a more computationally efficient implementation would be to use lazy evaluation and avoid considering all possible hypotheses. Our findings suggest that such an approach could be done without substantially compromising statistical performance.

翻译：针对即时决选（IRV）选举的各种风险限制审计（RLA）方法已被开发。最新提出的AWAIRE方法是首个高效方案，能利用但并非必须依赖选票记录（CVR）。AWAIRE采用自适应加权平均检验统计量，本质上是"学习"一组待检验的有效假设。然而，AWAIRE的初始论文仅考察了少数加权方案与参数设置。我们更广泛地探索了各种方案与参数配置，以识别并推荐适用于实践的效率最优方案。我们聚焦于无CVR可用的场景，基于真实选举数据的模拟评估性能表现。最有效的方案通常是那些基于已有数据将大部分或全部权重分配给表观"最优"假设的方案。与此同时，最优调参参数往往随选举差距而变化。尽管如此，我们量化了不同参数选择在不同选举差距下的性能权衡，有助于在需要默认选项时选择最恰当的折衷方案。当前AWAIRE实现的一个局限在于仅支持少量候选者——此前实现中最多支持六位。若要实现更高计算效率，一个可行路径是采用惰性求值并避免穷举所有可能假设。我们的研究表明，此方法可在不过度牺牲统计性能的前提下实现。