Knowledge Mining in DSS Model Analysis

Knowledge Mining in DSS Model Analysis

David M. Steiger (University of Maine, USA) and Natalie M. Steiger (University of Maine, USA)
DOI: 10.4018/978-1-59140-134-6.ch011
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Abstract

The three stages of mathematical modeling include model formulation, solution and analysis. To date, the primary focus of model-based decision support systems (DSS), in general, and Management Science/Operations Research (MS/OR), specifically, has been on model formulation and solution. In fact, with a few notable exceptions, computer-assisted model analysis has been ignored in both information systems (IS) and MS/OR literature (Swanson & Ramiller, 1993). This lack of attention to model analysis is especially noteworthy for two reasons. First, the primary bottleneck of modeling is in the analysis and interpretation of model results (Greenberg, 1993). Second, the basic purpose of DSS and mathematical modeling is insightful understanding of the modeled environment through insightful analysis (Geoffrion, 1976; Steiger, 1998). Developing insight into the complex decision-making environment is ultimately a process of discovery, finding trends and surprising behaviors and comparing the behavior of the model to what is expected or observed in the real system (Jones, 1992). Thus, insightful understanding often entails the inductive analysis of several (if not many) model instances (i.e., what-if cases), each of which has one or more different values for input parameters in an attempt to understand the associated changes in the modeled output.

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