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Artificial Intelligence and Generalized Qualitative‐Response Models: An Empirical Test on Two Audit Decision‐Making Domains
Author(s) -
Hansen James V.,
McDonald James B.,
Stice James D.
Publication year - 1992
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/j.1540-5915.1992.tb00413.x
Subject(s) - computer science , machine learning , artificial intelligence , audit , empirical research , dynamic decision making , management science , mathematics , management , economics , statistics
Machine learning methods are currently the object of considerable study by the artificial intelligence community. Research on machine learning carries implications for decision making in that it seeks computational methods that mimic input‐output behaviors found in classes of decision‐making examples. At the same time, research in statistics and econometrics has resulted in the development of qualitative‐response models that can be applied to the same kind of problems addressed by machine‐learning models—particularly those that involve a classification decision. This paper presents the theoretical structure of a generalized qualitative‐response model and compares its performance to two seminal machine‐learning models in two problem domains associated with audit decision making. The results suggest that the generalized qualitative‐response model may be a useful alternative for certain problem domains.