Expert knowledge elicitation to improve formal and mental models

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 diAcult 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 diAculties 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. * c 1998 John Wiley & Sons, Ltd. Syst. Dyn. Rev. 14, 309‐340, (1998) Many public and private sector systems increasingly depend on knowledge intensive processes managed and operated by interdisciplinary teams. These systems are diAcult to manage. Often formal models such as system dynamics models are used to help managers understand the sources of diAculties and design more eAective policies. Typically, the expert knowledge of the people who actually operate the system is required to structure and parameterize a useful model. To develop a useful model that is also credible in the eyes of the managers, however, modelers must elicit from these experts information about system structure and governing policies, and then use this information to develop the model. While many methods to elicit information from experts have been developed, most assist in the early phases of modeling: problem articulation, boundary selection, identification of variables, and qualitative causal mapping. These methods are often used in conceptual modeling, that is, in modeling eAorts that stop short of the development of a formal model that can be used to test hypotheses and proposed policies. The literature is comparatively silent, however, regarding methods to elicit the information required to estimate the parameters, initial conditions, and behavior relationships that must be specified precisely in formal modeling.