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Expert knowledge elicitation to improve formal and mental models
Author(s) -
Ford David N.,
Sterman John D.
Publication year - 1998
Publication title -
system dynamics review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.491
H-Index - 57
eISSN - 1099-1727
pISSN - 0883-7066
DOI - 10.1002/(sici)1099-1727(199824)14:4<309::aid-sdr154>3.0.co;2-5
Subject(s) - computer science , tacit knowledge , credibility , process (computing) , knowledge integration , knowledge management , face (sociological concept) , explicit knowledge , interpersonal communication , mental model , expert elicitation , artificial intelligence , knowledge engineering , psychology , cognitive science , social psychology , statistics , mathematics , social science , sociology , political science , law , operating system
Knowledge intensive processes are often driven and constrained by the mental models of experts acting as direct participants or managers. Descriptions of these relationships are not generally available from traditional data sources but are stored in the mental models of experts. Often the knowledge is not explicit but tacit, so it is difficult to describe, examine, and use. Consequently, improvement of complex processes is plagued by false starts, failures, institutional and interpersonal conflict, and policy resistance. Modelers face difficulties in eliciting and representing the knowledge of experts so that useful models can be developed. We describe and illustrate an elicitation method that uses formal modeling and three description format transformations to help experts explicate their tacit knowledge. We use the method to elicit detailed process knowledge describing the development of a new semiconductor chip. The method improved model accuracy and credibility and provided tools for development team mental model improvement. © 1998 John Wiley & Sons, Ltd.