Evaluation of Decision-Making Support Systems

Evaluation of Decision-Making Support Systems

Gloria E. Phillips-Wren (Loyola College in Maryland, USA), Manuel Mora (Autonomous University of Aguascalientes, Mexico) and Guisseppi Forgionne (University of Maryland, Baltimore County, USA)
DOI: 10.4018/978-1-59904-843-7.ch037
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Decision support systems (DSSs) have been researched extensively over the years with the purpose of aiding the decision maker (DM) in an increasingly complex and rapidly changing environment (Sprague & Watson, 1996; Turban & Aronson, 1998). Newer intelligent systems, enabled by the advent of the Internet combined with artificial-intelligence (AI) techniques, have extended the reach of DSSs to assist with decisions in real time with multiple informaftion flows and dynamic data across geographical boundaries. All of these systems can be grouped under the broad classification of decision-making support systems (DMSS) and aim to improve human decision making. A DMSS in combination with the human DM can produce better decisions by, for example (Holsapple & Whinston, 1996), supplementing the DM’s abilities; aiding one or more of Simon’s (1997) phases of intelligence, design, and choice in decision making; facilitating problem solving; assisting with unstructured or semistructured problems (Keen & Scott Morton, 1978); providing expert guidance; and managing knowledge. Yet, the specific contribution of a DMSS toward improving decisions remains difficult to quantify.

Key Terms in this Chapter

Multicriteria Method: This is the methodology that integrates two or more criteria to form a single value.

Evaluation Criteria: These are qualitative or quantitative metrics on which the DMSS is evaluated.

Decision Support System (DSS): A DSS is an information system that utilizes database or model-base resources to provide assistance to decision makers through analysis and output.

Decision-Making Support System (DMSS): A DMSS is an information system whose purpose is to provide partial or full support for decision-making phases: intelligence, design, choice, implementation, and learning.

Decision Value: It is the metric provided by a multicriteria model of the DMSS that quantitatively combines both process and outcome criteria to form a single measure.

Analytic Hierarchy Process (AHP): AHP is a multicriteria model that provides a methodology for comparing alternatives by structuring criteria into a hierarchy, providing for pair-wise comparisons of criteria at the lowest level of the hierarchy to be entered by the user, and synthesizing the results into a single numerical value.

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