AI 中文总结
研究议会选举中政党赢得多数席位认证问题，基于SHANGRLA框架结合席位统计构建审计统计量，提出自适应抽样策略，模拟表明该方法能大幅减少检查选票数，相比认证每个获胜席位有显著优势。
AI 中文摘要
现有风险限制审计方法通常专注于认证个别竞选结果。然而在议会选举中，政治上相关的结果往往是一个政党是否赢得足够席位以组建政府，而非每个报告的席位结果是否正确。在Mohanty等人（2019年）的工作基础上，我们将议会多数派的认证表述为部分合取检验问题：只需验证报告的获胜政党确实赢得了其报告席位的至少多数。基于SHANGRLA审计框架，通过结合席位级统计数据构建多数结果的顺序审计统计量。然后提出在各席位间分配审计工作的自适应抽样策略，包括能避免在看似不太可能真正获胜的席位上过度投入精力的变体。利用基于合成数据和2014年印度人民院选举真实数据的模拟，我们表明与认证每个报告的获胜席位相比，审计议会多数派可大幅减少检查的选票数量（几乎减少千倍）。
英文摘要
Existing methods for risk-limiting audits typically focus on certifying individual contests. In parliamentary elections, however, the politically relevant outcome is often whether a party has won enough seats to form government, not whether every reported seat outcome is correct. Extending on the work of Mohanty et al. (2019), we formulate the certification of a parliamentary majority as a partial conjunction testing problem: it is enough to verify that the reported winning party truly won at least a majority of its reported seats. Building on the SHANGRLA auditing framework, we construct a sequential audit statistic for the majority outcome by combining seat-level statistics. We then propose adaptive sampling strategies that allocate auditing effort across seats, including variants that learn to avoid spending excessive effort on seats that appear unlikely to have been truly won. Using simulations based on synthetic and real data, from the 2014 Indian Lok Sabha election, we show that auditing the parliamentary majority can substantially reduce the number of ballots inspected (by almost a thousand-fold) compared to certifying every reported winning seat.
Comments22 pages, 7 figures, accepted for E-Vote-ID 2026
Journal refElectronic Voting, E-Vote-ID 2026, Lecture Notes in Computer Science 17083 (2027) 55-69
DOI：10.1007/978-3-032-39512-2_4
