COBRA：用于风险限制审计的比较最优下注
COBRA: Comparison-Optimal Betting for Risk-limiting Audits
AI总结：
本文提出通过优化超鞅调整参数（下注）构建高效的选票级别比较风险限制审计，使模拟审计工作量平均减少93%，并提供近似最优下注策略及现实审计建议。
摘要：
风险限制审计（RLAs）可以通过检查已投票选票的随机样本来提供常规的、肯定性的证据，证明报告的选举结果是正确的。高效的RLA需要检查相对较少的选票。在这里，我们通过优化超鞅调整参数——即“下注”——来构建高效的RLA，用于选票级别的比较审计。精确的最优下注取决于已投票记录（CVRs，即详细说明机器如何统计每张选票的数字凭证）中的真实错误率。我们评估了一系列稀释差距和CVR错误率下的竞赛审计的理论和模拟工作量。与过去工作中推荐的下注相比，使用这些最优下注可以大幅减少预期工作量——在我们的模拟审计中平均减少了93%。由于在实践中精确的最优下注是未知的，我们提供了一些近似它们的策略。正如ALPHA和RiLACs中描述的选票轮询RLAs一样，将下注适应于先前采样的数据，或将其在一系列可疑的错误率上进行多样化，可以比将下注固定为先验值带来更高效的审计，尤其是当这些值远离正确值时。我们概述了对其他设计和社会选择函数的扩展，并以一些针对现实世界比较审计的建议作为结论。
英文摘要：
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--$\textit{bets}$--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 $\textit{a priori}$ 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
