{"id":1508,"date":"2018-03-25T18:21:56","date_gmt":"2018-03-25T18:21:56","guid":{"rendered":"http:\/\/mitmgmtfaculty.mit.edu\/jsterman\/?page_id=1508"},"modified":"2018-04-05T13:52:15","modified_gmt":"2018-04-05T13:52:15","slug":"expertknowledge","status":"publish","type":"page","link":"https:\/\/mitmgmtfaculty.mit.edu\/jsterman\/expertknowledge\/","title":{"rendered":""},"content":{"rendered":"

Expert Knowledge Elicitation to Improve Mental and Formal Models<\/h2>
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D. Ford and J. D. Sterman (1997) Expert Knowledge Elicitation to Improve Mental and Formal Models. MIT Sloan School of Management.<\/p>\n

DOI:\u00a0https:\/\/doi.org\/10.1002\/(SICI)1099-1727(199824)14:4<309::AID-SDR154>3.0.CO;2-5<\/p>\n

Abstract<\/strong><\/p>\n<\/div>\n

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Knowledge intensive processes are often driven and constrained by the mental models of experts acting as direct participants or managers. For example, product development is guided by expert knowledge including critical process relationships which are dynamic, biased by individual perspectives and goals, conditioned by experience, aggregate many system components and relationships and are often nonlinear. Descriptions of these relationships are not generally available from traditional data sources such as company records but are stored in the mental models of the process 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. Formal modeling approaches such as system dynamics are often used to help improve system performance. However, modelers face great difficulties in eliciting and representing the knowledge of the experts in these systems so useful models can be developed. Increased clarity and specificity are required concerning the methods used to elicit expert knowledge for modeling. We propose, describe and illustrate an elicitation method which uses formal modeling and three description format transformations to help experts explicate their tacit knowledge. To illustrate the approach we describe the use of the method to elicit detailed process knowledge describing the development of a new semiconductor chip. The method improved model accuracy and credibility in the eyes of the participants and provided tools for development team mental model improvement. We evaluate our method to identify future research opportunities.<\/p>\n