The effects of alternative forms of knowledge representation on decision-making consensus

• Published in: International journal of man-machine studies Vol. 38; no. 3; pp. 489 - 507
• Main Authors: Gowan, Jack Arthur, McNichols, Charles W.
• Format: Journal Article
• Language: English
• Published: London Elsevier Ltd 01.03.1993; New York, NY Academic Press
• Subjects: Applied sciences; Artificial intelligence; Computer science; control theory; systems; Exact sciences and technology; Problem solving, game playing; Data analysis; Methodology; Feedback; Decision making; Expert system; Knowledge representation; Artificial intelligence
• ISSN: 0020-7373
• DOI: 10.1006/imms.1993.1023

This research compared three different forms of feedback in a Delphi exercise, and demonstrated the consensus building effectiveness of feedback represented by if-then rules. The rules, similar to those employed in expert system knowledge bases, were induced from experts' decisions on a sequence of test cases and used to summarize the group's decision policy. Consensus was measured in terms of pairwise subject agreement after each round of decisions, and the measurement was obtained from panels of experts making decisions with three forms of feedback: if-then rules, frequency distributions or regression weights. The going concern decision scenario provided test cases derived from small business data and required evaluation of the likelihood of a company's economic viability 1 year in the future. Subjects were experienced bank loan officers and small business development center consultants. A three-round Delphi experiment accomplished by mail showed that if-then rules were more effective in motivating consensus than either frequency distributions or regression weights. As a result of this research, a prototype Group Decision Support System (GDSS) has been implemented to facilitate repetitive group decisions such as those required in evaluating loan applications or capital investment projects.