An Experimental Investigation of Approaches to Audit Decision Making: An Evaluation Using Systems‐Mediated Mental Models*
Bibliographic record
Abstract
Abstract The objective of this research is to articulate a decision‐making foundation for the systems audit approach. Under this audit approach, the auditor first gains an understanding of the auditee's economic environment, strategy, and business processes and then forms expectations about its performance and financial reporting. Proponents of this audit approach argue that decision making is enhanced because the knowledge of the system allows the auditor to focus on the most important risks. However, there has not been an explicit framework to explain how systems knowledge can enhance decision making. To provide such a framework, we combine mental model theory with general systems theory to produce a hypothesis we refer to as a systems‐mediated mental model hypothesis. We test this hypothesis using experimental economics methods. We find that (1) subjects make systematic errors under the setting without an organizing framework provided by the systems information, and (2) the presence of an organizing framework results in lower reporting errors. Importantly, the organizing framework significantly enhances decision making in the settings where the environment changed. Establishing a decision‐making foundation for systems audits can provide an important building block that, in part, can contribute to the development of a more effective and efficient audit technology ‐ an important objective now when audits are facing a credibility crisis.
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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 | 0.015 | 0.086 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".