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An election audit is risk-limiting if the audit limits (to a pre-specifiedthreshold) the chance that an erroneous electoral outcome will be certified.Extant methods for auditing instant-runoff voting (IRV) elections are eithernot risk-limiting or require cast vote records (CVRs), the voting system'selectronic 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 oftest supermartingales to efficiently audit IRV elections when CVRs are notavailable. The adaptive weighting 'learns' an efficient set of hypotheses totest to confirm the election outcome. When accurate CVRs are available, AWAIREcan use them to increase the efficiency to match the performance of existingmethods that require CVRs.  We provide an open-source prototype implementation that can handle electionswith up to six candidates. Simulations using data from real elections show thatAWAIRE is likely to be efficient in practice. We discuss how to extend thecomputational 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 sequentiallywhile rigorously controlling the familywise error rate.

Adaptively Weighted Audits of Instant-Runoff Voting Elections: AWAIRE