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System dynamics depends heavily upon quantitative data to generate feedback models. Qualitative data and their analysis also have a central role to play at all levels of the modeling process. Although the classic literature on system dynamics strongly supports this argument, the protocols to incorporate this information during the modeling process are not detailed by the most influential authors. Data-gathering techniques such as interviews and focus groups, and qualitative data analysis techniques such as grounded theory methodology and ethnographic decision models could have a strong, critical role in rigorous system dynamics efforts. This article describes some of the main qualitative, social science techniques and explores their suitability in the different stages of the modeling process. Additionally, the authors argue that the techniques described in the paper could contribute to the understanding of the modeling process, facilitate communication among modelers and clients, and set up a methodological framework to promote constructive discussion around the merits of qualitative versus quantitative modeling. Copyright © 2003 John Wiley & Sons, Ltd.Syst. Dyn. Rev. 19, 271-296 (2003) System dynamics is a powerful tool in the creation of feedback theories. Since its beginnings, the founders of the field have developed a series of guidelines for the model building process (Randers 1980;Richardson and Pugh 1981;Roberts et al. 1983;Wolstenholme 1990;Sterman 2000) and a series of tests to build confidence in the models created (Forrester and Senge 1980;Sterman 2000). As depicted by the classical literature, the development of system dynamics models is an iterative process. Each iteration results in a better and more robust model. Although system dynamics models are mathematical representations of problems and policy alternatives, it is recognized that most of the information available to the modeler is not numerical in nature, but qualitative. For example, while describing the information sources for the model building process, Forrester (1994) suggested that these qualitative data reside in the actors' heads (mental database) and in the form of written text (written database). Moreover, he recognized that the most important source, both in quantity and significance for the modeler, is the mental database (Figure 1): 1 As suggested by the figure, the amount of available information declines, probably by many orders of magnitude, in going from mental to written information and again by

The system dynamics group at the Rockefeller College of the University at Albany has been developing techniques to create system dynamic models with groups of managers during the last 25 years. Building upon their tradition in decision conferencing, the group has developed a particular style that involves a facilitation team in which people play different roles. Throughout these years of experience, the group has also developed several "scripts" to elicit knowledge from experts based on small-groups research, and well-established practices in the development of system dynamics models. This paper constitutes a detailed documentation of a relatively small-scale modeling effort that took place in early 2001, offering a "soup to nuts" description of group model building at Albany. The paper describes in detail nine of the scripts that the group has developed, offering some reflections about their advantages and limitations. Copyright methods. This particular GMB effort was completed over a 4-month period in 2001, and it was designed following the procedures and methods developed at the University at Albany. In addition to presenting a detailed description of the process, and the products associated with the project, the paper also documents the effort needed to accomplish the objectives by both modeling and client teams. The paper extends the discussion about the use of scripts to develop system dynamic models with groups, as initiated by , presenting two new scripts and detailed descriptions and process products of seven scripts more.The case presentation constitutes a "soup to nuts" description of the Albany GMB approach. The description also includes some process-related products published for the first time. 2 These products illustrate the results obtained in the case and assist other system dynamics practitioners to replicate the experience. The perceived success of the experience reported in this paper encouraged continued effort in model building that has extended into 2005, including GMB to examine the dynamics of information integration in intergovernmental projects.Following this brief introduction, the paper is organized into four sections. The first of them consists of a review of previous research in GMB. The next section includes a description of the specific GMB effort documented in this paper. The following section contains a description of the eight scripts used in the GMB, and the final section includes a series of concluding remarks.Published online in Wiley InterScience (www.interscience.wiley.com)

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