Design of a Self-evolving Decision Support System.

Author List: Liang, Ting-peng; Jones, Christopher;
Journal of Management Information Systems, 1987, Volume 4, Issue 1, Page 59-82.
The paper presents a self-evolving approach to decision support systems (DSS) design. The basic premise of this approach is that a DSS should be aware of how it is being used and, then, automatically adapt to the evolution of its users. With self-evolving capabilities, a DSS will be able to provide a flexible menu hierarchy and a dynamic user interface. The major difference between the self-evolving design and a DSS developed by current approaches such as system development life cycle and user-involved evolutionary design is that the former has an extra component—the evolutionary mechanism—to control the evolution of the system. In order to develop self-evolving capabilities, the following three components must be developed: (1) a database of user profiles to keep track of related system usage data, (2) a knowledge base to store rules for determining appropriate system default policy, and (3) a control mechanism to control the evolution of the system.
Keywords: decision support systems; information systems development.; Self-evolving design
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#287 0.254 design systems support development information proposed approach tools using engineering current described developing prototype flexible built architecture environment integrated designing
#113 0.210 support decision dss systems guidance process making environments decisional users features capabilities provide decision-making user paper findings systems.decision components computer-based
#283 0.162 interface user users interaction design visual interfaces human-computer navigation human need cues studies guidelines laboratory functional developed restricted know guided
#280 0.098 control controls formal systems mechanisms modes clan informal used internal literature outsourced outcome theory configuration attempts evolution authority complementary little
#97 0.093 set approach algorithm optimal used develop results use simulation experiments algorithms demonstrate proposed optimization present analytical distribution selection number existing