A Cascaded Inference Model for Evaluation of the Internal Audit Report*
为外部审计师评估内部审计工作可靠性提供了规范性框架，使用级联推理模型和贝叶斯规则分析内部审计报告的证据价值，发现报告偏差比真实性对评估影响更大，且偏差影响取决于审计师能力。
Abstract This paper provides a normative framework for how external auditors should evaluate internal audit (IA) work, with a view to assessing the risk of material misstatement. The central issue facing the external auditor when evaluating IA work is the reliability of IA work. Reliability assessments are structured using the cascaded inference framework from behavioral decision theory, in which attributes of source reliability are explicitly modeled and combined using Bayes' rule in order to determine the inferential value of IA work. Results suggest that the inferential value of an IA report is highly sensitive to internal auditor reporting bias, but relatively insensitive to reporting veracity. Veracity refers to internal auditors' propensity to report truthfully, whereas bias refers to the propensity to misreport findings. Results also indicate that this sensitivity to reporting bias is conditional on the level of internal auditor competence, thus suggesting significant interaction effects between the objectivity and competence factors. Collectively, these findings suggest that the impact of source reliability attributes may be more complex than portrayed in the auditing standards and that recognizing these subtleties may lead to greater efficiency and effectiveness.