Paul R. Watkins

Integrating expert systems and decision support systems

Expert systems are emerging as a powerful tool for decision making. Integrating expert systems with decision support systems may enhance the quality and efficiency of both computerized systems. This article examines possible connections between the two technologies and discusses some issues related to their integration.

Applied Expert Systems

(1990). Applied Expert Systems. Journal of the Operational Research Society: Vol. 41, No. 2, pp. 178-179.

Beyond expert systems—Reasoning, judgment, and wisdom

Abstract Artificial intelligence and expert systems have dealt primarily in the domain of logical or deductive reasoning on well-structured problems. There are, however, a vast number of managerial and other tyoe problems that require creativity, value judgments, and wisdom. As “intelligent” systems begin to be utilized by managers, new or improved methods and approaches will be required to support “rich” and value-laden problem domains. This article discusses issues and focuses attention on the nature of judgement, wisdom, and heuristics in managerial problem solving.

Decision maker preferences for information in complex decision making: New directions for OR intervention

Abstract Operations research models are used in many business and non-business entities to support a variety of decision making activities, primarily well-defined, operational decisions. This is due to the traditional emphasis of these models on optimal solutions to pre-specified problems. Some attempts have been made to use OR models in support of more complex, strategic decision making. Traditionally, these models have been developed without explicit consideration for the information processing abilities and limitations of the decision makers, who interact with, provide input to, and receive output from such models. Research in judgement and decision making show that human decisions are influenced by a number of factors including, but not limited to, information presentation modes; information content, modes, e.g., quantitative versus qualitative; order effects such as primacy, recency; and simultaneous versus sequential presentation of data. This article presents empirical research findings involving executive business decision makers and their preferences for information in decision making scenarios. These preference functions were evaluated using OR techniques. The results indicate that decision makers view information in different ways. Some decision makers prefer qualitative, narrative, social information, whereas other prefer quantitative, numerical, firm specific information. Results also show that decision making tasks influence the preference structure of decision makers, but that in general, the preference are relatively stable across tasks. The results imply that for OR models to be more useful in support of non-routine decision making, attention needs to be focused on the information content and presentation effects of model inputs and outputs.

The impacts of emerging management support systems

Efraim Turban, Professor of Systems Science and Director of Information Systems Research Laboratory, including Expert and Decision Support Systems, at the University of Southern California, is interested in emerging computer technologies and their organizational and managerial impacts. He is the author of several books, including Decision Support Systems and Expert Systems, and Fundamentals of Management Science. . He also authored about 50 papers which appeared in the major professional journals. Graduated from the University of California at Berkeley (MBA, Ph.D.), Dr. Turban held faculty appointments at Lehigh University and Florida International University. He has held a visiting appointment at UCLA. Dr. Turban worked in industry as industrial engineer and management consultant with leading corporations, such as General Electric. He has been a consultant to many corporations and governments.

Knowledge Acquisition for a Diagnosis-Based Task

The basic research task discussed in this paper is the investigation of the relative effectiveness of alternative knowledge acquisition methodologies at eliciting different types of knowledge for a diagnosis task. In order to investigate this task, a field study was used.

AI integration for enhanced decision support

The topic of integration of artificial intelligence with traditional MIS and DSS has generated interest and attention over the past few years. As applied AI has matured and developed beyond expert systems to include neural nets, genetic algorithms, model based systems, fuzzy logic, natural language and case-based reasoning, the need to provide perspective for the integration of these AI technologies with each other has become an issue of interest and concern. This paper provides a perspective that is problem driven and suggests that AI integration is a matching process of problems/sub-problems with appropriate AI or other problem support technologies which then can be integrated to provide enhanced decision support. AI integration is demonstrated within the context of internal control evaluation where the emphasis is on detecting/preventing financial fraud.<<ETX>>

The Advanced Information Systems Program (AISP) at the University of Southern California

Abstract The Advanced Information Systems Program (AISP) is an interdisciplinary approach to research and technology transfer between academia and business organizations. A variety of activities are undertaken by AISP including international research symposia, working paper series, continuing education seminars/workshops, affliations with major professional organizations, sponsorship of an international journal on “intelligent” systems. Ph.D. education and cooperative projects with business organizations.

A portfolio of knowledge acquisition approaches for a knowledge-based system

The authors discuss the knowledge acquisition required for the development of a system to diagnose communication problems for users of a large mainframe time-sharing system. A field study approach was used to exploit the experiences and existing knowledge structures of the technicians. It was found that multiple forms of knowledge acquisition and representation were required for the development of the system, since no one form of knowledge acquisition was found to provide insight into each of the different types of knowledge that ultimately were used in the development of the system. Thus, it is suggested that individual task comparisons of different forms of knowledge acquisition may understate or misstate the problems of interest to developers of expert systems. Individual approaches each have their own strengths and weaknesses, so that, in a portfolio of approaches, weaknesses of one approach can be mitigated by strengths of other approaches.<<ETX>>