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Recent successes in the design and implementation of expert systems in non-managerial problem domains has spurred interest in the proposition that expert systems may be viable in managerial problem domains as well. Continuing research in the design, implementation, and testing of a prototype managerial expert system is described in this paper. [ABSTRACT FROM AUTHOR]

Information on cause-effect relationships among variables in the problem domain is one type of knowledge required in expert decision support systems for problem formulation. This knowledge must be acquired from "expert" managers and stored in the system's knowledge base. Unfortunately even experienced managers may be biased in their beliefs about cause-effect relationships. We present a system which uses causal modeling, path analysis, and an historical database to statistically verify asserted relationships as they are entered into the system. Since it is possible that a valid assertion is not statistically supported, the user has the option to insert a relationship into the knowledge base even though the analysis may not indicate statistical validity. Information on rejected relationships is maintained in a "rejection base" which is used later to retest assertions whose validity may have changed due to updates to the database. The intent is to provide a system which helps the user learn, in an unbiased manner, about the true nature of causal relationships in the problem domain. [ABSTRACT FROM AUTHOR]

A knowledge-based system supporting managerial problem diagnosis is described. The system provides the capability to monitor values of selected variables for problem situations. When problems are located, a list of problem symptoms is delivered to a problem processor for structuring and diagnosis. Problem structuring is based on a combination of concepts from expert systems and structural modeling. User assertions about cause-effect relationships between pairs of variables are maintained in a semantic network. Problem diagnosis uses the relationships in the semantic network to construct causation trees, the branches of which represent potential explanations of the problem symptoms. Mathematical models are constructed based on causation-tree branches, and values from the data base are used to test whether the model confirms the diagnosis. If so, the source of the problem has been located and it is then up to the user to resolve the problem. If the model fails to explain the problem, the model apparently is deficient and the user may perform "what if …" type scenarios in attempts to improve the model and search for problem causes. Realistic applications in the accounting and health care areas are discussed. [ABSTRACT FROM AUTHOR]

To date, case analyses and field studies have predominated in MIS/DSS research. Laboratory studies seem not to have been given the attention they deserve. This trend is partially due to: (1) the high costs associated with developing decision simulators and related software to support laboratory research and (2) difficult problems with external validity because of unrealistically simple simulated decision environments and DSS software. Since MIS/DSS laboratory research has been most successful when studies have occurred in an interrelated stream, the development of common, portable, and flexible simulators seems desirable. Common simulators would reduce the time and cost of developing experiments, provide for research continuity, comparability, and replication, and facilitate researcher to researcher communication. Sufficiently complex simulators and DSS software would reduce problems with external validity by providing more realistic laboratory models. This paper presents a software package that offers considerable potential as a common tool for DSS research. [ABSTRACT FROM AUTHOR]

As an approach to teaching management information systems, students are required to play a management game. The students become aware of information needs through participation in the decision process of the game. The student then develops an integrated system to assist in playing this management game. This new approach, the gaming method, is considered an effective way of teaching integrated systems because the intimate interrelationships between decision-making processes and information systems can be easily illustrated. [ABSTRACT FROM AUTHOR]

Recent efforts in the area of modelling activities in the urban environment have been concentrated on the use of simulation techniques. In this paper the problem of population location in a metropolitan area is treated in an analytic model employing a goal programming formulation. A target population is broken into groups which then compete for a share of limited capacity in a number of urban sectors. The possible usefulness of this approach to urban planning, real estate development, and transportation systems design is illustrated through the theoretical development and the presentation of a numerical model. [ABSTRACT FROM AUTHOR]