Going Concern Judgments as a Multiple Constraint Satisfaction Process

Abstract

Key components of the multiple-constraint satisfaction framework are explored in an extensive experiment set in the complex and ambiguous domain of external auditing. The results show the prevalence and importance of a purposeful, albeit unconscious, structuring of the information by both the professional auditors and the auditing students as they gradually generate coherence and sense. In accordance with multiple constraint satisfaction predictions, the assessments of inferences increasingly spread apart. Also, the correlations between the dependent variable (the decision) and the independent variables, as well as between the independent variables, consistently grow stronger as the participants progress through the decision stages. The information structuring - a gradual simplification of the component structure - is captured as principal components associated with the various decision stages. Neural networks capture the judgments in the various decision stages quite well. Finally, the role of the ongoing structuring of the underlying information was explored through the application of trained networks to data originating in other decision stages.