Decision Support System

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Decision Support Systems
Marek J. Druzdzel and Roger R. Flynn
Decision Systems Laboratory School of Information Sciences and Intelligent Systems Program University of Pittsburgh Pittsburgh, PA 15260 {marek,flynn}@sis.pitt.edu http://www.sis.pitt.edu/∼dsl

To appear in Encyclopedia of Library and Information Science, Second Edition, Allen Kent (ed.), New York: Marcel Dekker, Inc., 2002

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Contents
Introduction Decisions and Decision Modeling Types of Decisions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Human Judgment and Decision Making . . . . . . . . . . . . . . . . . . . . . . . . . . . . Modeling Decisions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Components of Decision Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Decision Support Systems Normative Systems Normative and Descriptive Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Decision-Analytic Decision Support Systems . . . . . . . . . . . . . . . . . . . . . . . . . . Equation-Based and Mixed Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . User Interfaces to Decision Support Systems Support for Model Construction and Model Analysis . . . . . . . . . . . . . . . . . . . . . 3 4 4 4 5 5 6 7 7 8 10 11 11

Support for Reasoning about the Problem Structure in Addition to Numerical Calculations 11 Support for Both Choice and Optimization of Decision Variables . . . . . . . . . . . . . . Graphical Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary 12 12 12

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Introduction
Making decisions concerning complex systems (e.g., the management of organizational operations, industrial processes, or investment portfolios; the command and control of military units; or the control of nuclear power plants) often strains our cognitive capabilities. Even though individual interactions among a system’s variables may be well understood, predicting how the system will react to an external manipulation such as a policy decision is often difficult. What will be, for example, the effect of introducing the third shift on a factory floor? One might expect that this will increase the plant’s output by roughly 50 percent. Factors such as additional wages, machine weardown, maintenance breaks, raw material usage, supply logistics, and future demand need also be considered, however, as they all will impact the total financial outcome of this decision. Many variables are involved in complex and often subtle interdependencies and predicting the total outcome may be daunting. There is a substantial amount of empirical evidence that human intuitive judgment and decision making can be far from optimal, and it deteriorates even further with complexity and stress. Because in many situations the quality of decisions is important, aiding the deficiencies of human judgment and decision making has been a major focus of science throughout history. Disciplines such as statistics, economics, and operations research developed various methods for making rational choices. More recently, these methods, often enhanced by a variety of techniques originating from information science, cognitive psychology, and artificial intelligence, have been implemented in the form of computer programs, either as stand-alone tools or as integrated computing environments for complex decision making. Such environments are often given the common name of decision support systems (DSSs). The concept of DSS is extremely broad, and its definitions vary, depending on the author’s point of view. To avoid exclusion of any of the existing types of DSSs, we will define them roughly as interactive computer-based systems that aid users in judgment and choice activities. Another name sometimes used as a synonym for DSS is knowledge-based systems, which refers to their attempt to formalize domain knowledge so that it is amenable to mechanized...
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