Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
EWNLG '93 Selected papers from the Fourth European Workshop on Trends in Natural Language Generation, An Artificial Intelligence Perspective
A Comparison of Bayesian and Belief Function Reasoning
Information Systems Frontiers
International Journal of Approximate Reasoning
Journal of Management Information Systems
International Journal of Approximate Reasoning
Hi-index | 0.00 |
This paper examines possible differences in auditors' performance when they make belief-based versus probability-based risk assessments by focusing on two phases of the financial statement audit process: the assessment of two attributes of audit evidence ('strength' and 'direction') and the aggregation of evidence. Based on an experiment in which 48 experienced auditors participated, three important findings were observed. First, there was no significant difference in the mean assessment of strength of evidence measured using the likelihood ratio. However, the difference in the assessed direction of evidence, that is whether the evidence is interpreted as being confirming or disconfirming, is significant for one of the cases examined. This result shows that auditors making belief-based assessments are able to assess the direction of the evidence more accurately than auditors making probability-based assessments. Third, the auditors' aggregation of evidence was not in accordance with 'AND' logic for either auditors making belief-based or probability-based assessments. These empirical results raise issues which need to be addressed in practice and in future research.