Risk-Limiting Bayesian Polling Audits for Two Candidate Elections
This work addresses election integrity for auditors and policymakers by providing a more efficient audit method, though it is incremental as it builds on existing risk-limiting and Bayesian audit concepts.
The paper tackles the problem of auditing two-candidate plurality elections by proposing a Bayesian Risk-Limiting Audit framework, which combines Bayesian and risk-limiting approaches to improve computational efficiency through a simple comparison test and pre-computation without simulations.
We propose a simple common framework for Risk-Limiting and Bayesian (polling) audits for two-candidate plurality elections. Using it, we derive an expression for the general Bayesian audit; in particular, we do not restrict the prior to a beta distribution. We observe that the decision rule for the Bayesian audit is a simple comparison test, which enables the use of pre-computation---without simulations---and greatly increases the computational efficiency of the audit. Our main contribution is a general form for an audit that is both Bayesian and risk-limiting: the {\em Bayesian Risk-Limiting Audit}, which enables the use of a Bayesian approach to explore more efficient Risk-Limiting Audits.