高估净值等价风险限制审计：ONEAudit
Overstatement-Net-Equivalent Risk-Limiting Audit: ONEAudit
AI总结：
针对传统卡级比较风险限制审计需选票与CVR一一对应的限制，提出高估净值等价（ONE）的ONEAudit方法，可基于批次结果等开展审计，大幅降低样本量与审计风险，适用于多种社会选择函数。
摘要：
此前，卡级比较风险限制审计（CLCAs）要求每张已投选票都对应一份选民投票记录（CVR），并通过“链接”标识每份CVR对应哪张选票——而许多投票系统无法提供这一条件。所有能产生相同合计结果的CVR集合都会以相同幅度高估竞选得票差：它们属于高估净值等价（ONE）的。因此，CLCAs可使用投票系统生成的任意数量选票的CVR，其余选票则使用ONE CVR。选票抽样型RLA等同于使用ONE CVR的CLCAs。通过为每个批次构建ONE CVR，CLCAs可基于批次级结果（例如选区小计）开展。与批次级比较审计（BLCAs）不同，该方法无需人工统计批次票数，即便上报批次与物理上可识别的选票批次不对应也能适用。若投票系统仅能导出部分选票的带链接CVR，审计人员仍可通过竞选结果或批次小计为其余选票构建ONE CVR来开展CLCA，这就消除了对“混合”审计的需求。该方法适用于所有已有对应RLA方法的社会选择函数，包括排序复选制（IRV）。使用竞选总票数的选票抽样审计（BPA）和ONEAudit的样本量相当。当批次内部同质性远高于选举整体同质性时，使用批次小计的ONEAudit样本量小于选票抽样审计。其样本量可远小于BLCA：若上报结果准确，针对2022年加利福尼亚州总统选举、风险限制为5%的CLCA，使用基于选区结果的ONE CVR时，全州仅需抽取约70张选票，而BLCA则需约26700张。2022年佐治亚州审计统计了超过23.1万张选票，而ONEAudit仅需约1300张。对于2018年密歇根州卡拉马祖市混合RLA试点的数据，ONEAudit的风险约为2%，远低于试点所用方法SUITE测得的3.7%的风险。
英文摘要：
Card-level comparison risk-limiting audits (CLCAs) heretofore required a CVR for each cast card and a "link" identifying which CVR is for which card -- which many voting systems cannot provide. Every set of CVRs that produces the same aggregate results overstates contest margins by the same amount: they are overstatement-net-equivalent (ONE). CLCAs can therefore use CVRs from the voting system for any number of cards and ONE CVRs for the rest. Ballot-polling RLAs are equivalent to CLCAs using ONE CVRs. CLCAs can be based on batch-level results (e.g., precinct subtotals) by constructing ONE CVRs for each batch. In contrast to batch-level comparison audits (BLCAs), this avoids tabulating batches manually and works even when reporting batches do not correspond to physically identifiable batches of cards. If the voting system can export linked CVRs for only some ballot cards, auditors can still use CLCA by constructing ONE CVRs for the rest of the cards from contest results or batch subtotals. This obviates the need for "hybrid" audits. This works for every social choice function for which there is a known RLA method, including IRV. Sample sizes for BPA and ONEAudit using contest totals are comparable. ONEAudit using batch subtotals has smaller sample sizes than ballot-polling when batches are much more homogeneous than the election overall. Sample sizes can be much smaller than for BLCA: A CLCA of the 2022 presidential election in California at risk limit 5% using ONE CVRs for precinct-level results would sample ~70 ballots statewide, if the reported results are accurate, compared to about 26,700 for BLCA. The 2022 Georgia audit tabulated >231,000 cards versus ~1300 for ONEAudit. For data from a pilot hybrid RLA in Kalamazoo, MI, in 2018, ONEAudit gives a risk of ~2%, substantially lower than the 3.7% measured risk for SUITE, the method the pilot used.
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。
