William F. Messier

A Cognitive Computational Model Of Risk Hypothesis Generation - Discussion

1. Introdutction This paper reports on an ambitious project which attempts to model the auditors risk hypothesis generation process that occurs during audit planning. Peters constructs a theory of this process, develops a computational model of the process using a computer program, and evaluates the model using a small group of practicing auditors. The apparent longrun objective of the project is to construct a decision aid or expert system to assist auditors during audit planning (Dhar, Lewis, and Peters [1988]). The conference discussion of the paper revolved mainly around two major issues: (1) the objective of the model and (2) the approach to evaluating the model. While I shall discuss each of these issues separately, they are interdependent. That is, the objective for developing the model directly affects how the resulting program is tested.

A Relational Approach to Monitoring Controls

The integration and coupling that result from networking, database systems, and real-time applications are serving to increase concern about security and control in modem computing environments. The establishment and effective monitoring of controls in such settings is essential to guarantee data integrity and to prevent undesired intrusion. It is proposed that the monitoring of system controls is essentially an information-processing activity and that the relational model is a useful formalization of information gathered from central monitoring. Moreover, we posit that the universal data view facilitates auditor analysis without imposing the requirement that he or she have explicit knowledge of the structure of the relational database of monitoring information.

The Importance of Quantifying Uncertainty: Examining the Effects of Sensitivity Analysis and Audit Materiality Disclosures on Investors’ Judgments and Decisions

Abstract In recent years, standard setters worldwide have considered how to enhance financial statement users’ understanding of the estimation uncertainty contained in many financial statement items. Our study examines two disclosures expected to help investors evaluate the reliability of subjective fair value estimates: a quantitative sensitivity analysis (QSA) and the auditor’s quantitative materiality threshold. Using an experiment, we predict and find that investors judge the reliability of a reported estimate to be higher and are more willing to invest when a QSA disclosure is indicative of low sensitivity (i.e., greater precision) compared to high sensitivity (i.e., greater imprecision), but only if the auditor’s materiality threshold is also disclosed. When materiality is not disclosed, investors fail to recognize differences in reliability between the two levels of sensitivity, even though the amount of imprecision in the low sensitivity condition represents a fraction of materiality, while in the high sensitivity condition, this amount exceeds materiality multiple times over. Furthermore, when both disclosures are absent and only a qualitative description of sensitivity is provided—as required by current standards—investors perceive the disclosure to be relatively uninformative and respond to the ambiguous disclosure by decreasing their willingness to invest. The results of our study should be informative to accounting and auditing standard setters as they continue to consider the types of disclosures that may help investors understand the most complex and subjective aspects of financial reporting.