Dynamic analysis of Bayesian audit strategies with tests of controls and reliability modeling
Bibliographic record
Abstract
Purpose The purpose of this paper is to summarize a simulation study that analyzed the performance of Bayesian audit strategies in a novel fashion – dynamically and with varying sample sizes depending on the extent of an auditor's prior information. Design/methodology/approach The prior information for the Bayesian strategies arises from a set of control tests that are evaluated making use of reliability theory. The entire audit strategy is simulated under systematically different control reliabilities and related amounts of total misstatements in an accounting population. Findings The major finding is that robust Bayesian audit strategies that have recently been developed in auditing research are more sensitive to non‐sampling errors than existing strategies of audit practice. Practical implications The authors find that there are differential effects of sampling error vs non‐sampling error on the Bayesian strategies and that controls testing does not need to be extensive to get full internal control reliance. Originality/value The paper adds to existing research by examining the performance of various Bayesian audit strategies under more realistic audit conditions of sampling and non‐sampling uncertainty.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.179 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".