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Automated Discovery in Managerial Problem Formulation: Formation of Causal Hypotheses for Cognitive Mapping
Author(s) -
Billman Beth,
Courtney James F.
Publication year - 1993
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.1540-5915.1993.tb00460.x
Subject(s) - computer science , probabilistic logic , causality (physics) , artificial intelligence , data science , causal model , cognitive map , domain (mathematical analysis) , knowledge acquisition , machine learning , cognition , domain knowledge , expert system , knowledge management , psychology , medicine , mathematical analysis , physics , mathematics , pathology , quantum mechanics , neuroscience
Development of knowledge acquisition techniques known as automated discovery systems has occurred in deep and narrow domains of knowledge. Automated discovery is the generation of new knowledge by a computer system on its own, without the help of another knowledge source. This paper describes research and validation of an automated discovery system for a wide and shallow domain—business management. The system continues recent advances in expert systems research which have enhanced cognitive mapping, a problem formulation tool. The system perceives the behavior of distal variables in the environment through probabilistic cues‐to‐causality, and generates previously unknown hypotheses by aggregating the probabilities into a single criterion of causal relatedness. The system is validated against the source code of a simulated managerial environment, and causal relationships posited by decision makers experienced in the play of the gaming simulator.