Decision Support System

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Decision support systems (DSSes) combine information with information visualization strategies (like expert systems) and collaborative technologies to enhance human decision-making (Aiken, Sheng, & Vogel, 1991).

The term “decision support system” refers to a conceptual role for computers in the decision-making process (Keen, 1980, p. 15). These are interactive systems that may provide relevant information to both single users and group ones (Bui & Jarke, 1986). These systems enable various types of decision-making, whether centralized or democratized; local or remote; synchronous or non-synchronous. A group decision support system’s purpose is to support “problem formulation and solution in group meetings” (Poole, Holmes, & DeSanctis, 1998, p. 227). Many DSSes strive for cognitive flexibility—drawing on both the “left” (logical) and “right” (creative) sides of the brain (Young, 1983).

Contents

Classifications of Decision Support Systems

The design and building of decision support systems have been described as a “deceptively complex, error-prone, and expensive” process (Mahmood, Courtney, & Burns, 1983, p. 23). A range of factors inform the design methodology—such as decision incidence (Are the decisions recurring or non-recurring?), the impact of the decision (Are the decisions critical or non-critical ones?), and time frame for the decision (Is the time frame short-term, medium-term or long-term?) (Mahmood, Courtney, & Burns, 1983, p. 24).

Decision support systems may be classified in a number ways—those based on models, data, decision-orientations, and general DSSes (Chen, Chau, & Kabat, 1985). Most such systems consist of a user interface, databases, and some modeling. Some systems involve simulations to inform users.

Solicited and Unsolicited Interventions

Some feedback may be solicited by users, but others may be offered in an unsolicited way for decision-making. One researcher based the uses of decision support systems on the Blake & Mouton (1976) intervention styles. On a continuum from non-directive to directive interventions, the continuum includes the following intervention styles: acceptant, catalytic, confrontational, prescriptive, and theory-based. The “acceptant” intervention style leaves users free to try out their own processes. A “catalytic” intervention style collects data to reinterpret and prioritize the problem for users. A “confrontational” intervention style identifies and analyzes the assumptions and weaknesses. A “prescriptive” style provides answers and solutions using pre-defined contents and processes. A “theory-based” intervention style internalizes empirically tested understandings (Wysk, 1990).

Recent DSS Developments

Decision support systems have been evolving to integrate more functionalities, such as geographic information systems (Medeiros, de Souza, Strauch, & Pinto, 2001), into the information streams. Other spatio-temporal data captured through “remote sensing, scientific simulation, telescope scanning, and other survey technologies” enhance these systems (Harms, S., Li, D., & Tadesse, T., 2002, p. 1). There are endeavors to make these systems more adaptive for a range of users, in defining and solving ill-structured tasks with undefined decision-making processes. These systems focus also on enhancing group coordination for decision-making (Medeiros, de Souza, Strauch, & Pinto, 2001).

There are other endeavors to meld and interface databases of information for more complex modeling and human decision-making with more data streams (Lee, 1983). Some of these involve branching logic and portrayal of various decision junctures, with both

Decision support systems have also been built for delivery to portable devices for in-field uses for convenience.

Fields Using Decision Support Systems

A wide range of domain fields use decision support systems. There are unique systems for agricultural decision-making in conditions of drought (Cottingham, Goddard, Zhang, Wu, Lu, Rutledge, & Waltman, 2004); field-based crop management (Sha & Zhang, 2007); airport safety (Pestana, da Silva, Casaca, & Nunes, 2005); risk management regarding drought situations over time (Li, Harms, Goddard, Waltman, & Deogun, 2003); shipyard manufacturing and resource allocation (Greenwood, Vanguri, Eksioglu, Jain, Hill, Miller, & Walden, 2005); agriculture (Goddard, Zhang, Waltman, Lytle, & Anthony, 2002); war gaming (Huang, Wu, Wai, Tung, & Tsai, 2006), traffic and transportation (Peytchev & Claramunt, 2001) ; healthcare (Xiao, Lewis, & Gibb, 2008); a new law-based system (van der Meer, Stavrinidou, & Andersen, 2003); land assessment (Nehme & Simōes, 1999), and fisheries management (Truong, Rothschild, & Azadivar, 2005).

In academic settings, DSSes are used for academic advising (Murray & Le Blanc, 1995); educational multimedia evaluations and selections (Abdelhakim & Shirmohammadi, 2007);

DSSes are used in consumer relations and marketing: the personalization of electronic commerce services (Yu, 2004) and customer decision support systems (O’Keefe & McEachern, 1998), as well as for efficiencies related to online purchasing for distributed retail chain stores (Yusof, 2004).

See Also

"Decision Support System": http://en.wikipedia.org/wiki/Decision_support_system

References

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Cottingham, I.J., Goddard, S., Zhang, S., Wu, X., Lu, K., Rutledge, A., & Waltman, W.J. (2004). Demonstration of the National Agricultural Decision Support System. Proceedings of the 2004 Annual National Conference on Digital Government Research: Seattle, Washington. ACM. 1 – 2.

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Wysk, R.B. (1990). Expert systems in the context of decision support related interventions. ACM. 475 – 490.

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