POTENTIAL RESEARCH TOPICS IN DECISION ANALYSIS*

Robert L. Winkler's paper [1] provides a comprehensive overview of challenging research areas for decision making under uncertainty. Hence, rather than try to extend the list of research areas identified, this note will attempt to embellish some that I feel are particularly important. In these areas, I feel the value of systematic research is particularly high. For convenience, the discussion will be organized under the four research categories identified by Winkler with a couple of sugestions following in an “implementation research” category. The reader will note, however, that many of the suggested topics actually relate to more than one research category.     

TOWARD TEACHING EXCELLENCE IN THE DECISION SCIENCES*

Recently the National Commission on Higher Education Issues found serious problems with college-level teaching and recomended educational reforms. This paper describes one attempt at reform—namely, a course at Georgia State University on university teaching for College of Business doctoral students. The 45-hour seminar, based on a university teaching model, covers setting instructional objectives, lecture and discussion methods, teaching methods for higher-level learning, test construction, course evaluation instruments, and instructional and technological innovations. Previous studies have demonstrated repeatedly that teaching improvement seminars can improve teacher and student performance in the classroom. This paper discusses how the seminar could be implemented at other colleges of business administration, extended to faculty development workshops, and ultimately result in a master teacher or instructional professorship.     

DECISION RULES IN PRODUCTION PLANNING UNDER CHANGE-CONSTRAINED SALES*

Alternative decision rules for aggregate production scheduling are specified here under chance-constrained sales and compared with the HMMS model. These rules are evaluated for their performance on the basis of simulated data on sales and it is found that the linear decision rules of the HMMS model can be considerably improved.     

AN ADAPTIVE DECISION SUPPORT SYSTEM FOR ACADEMIC RESOURCE PLANNING

Institutions of higher learning are growing increasingly interested in the use of model-based approaches to their resource allocation problems. Recent modeling approaches, however, have failed to consider that resource allocation planning is not a well-structured decision process. Additionally, many decision makers are necessarily involved in the academic planning process and may assume dissimilar perspectives on the importance of achieving different goals and objectives. Furthermore, satisfactory allocation solutions can be expected to vary considerably from decision maker to decision maker as the individual's cognitive processes, perceptions, and evaluations are taken into consideration. This paper describes a decision support system (DSS) approach that attempts to adapt to a variety of academic decision makers with differing planning views in an environment of multiple conflicting objectives. This DSS, which was successfully tested on four academic decision makers in a large midwestern university, shows considerable promise for providing decision support to decision makers with varied problem-solving styles.     

THE THEORY OF GRAPHS IN BEHAVIORAL SCIENCE*

In this tutorial the author shows how graph theory can be used to model the structural relationships in a behavioral theory. The baśic elements of a graph and its related matrices are defined and illustrated. A survey of applications of graph theory in behavioral science is followed by an extensive bibliography.     

CURRICULUM INTEGRATION IN THE DECISION SCIENCES: AN EVALUATION*

Recent interest in curriculum integration in the decision sciences has led to the proposal that topics in computer science, statistics, quantitative methods, operations research, and possibly mathematics might be integrated into a single unified set of courses. Such a unification may reinforce the learning process of the student and form the basis for the eventual synthesis of tools topics with functional area decision making. This integration proposal and speculation is examined using research in learning and motivation theory, and concludes that the integration of tools concepts with functional area topics is much more promising than the integration of tools topics alone. A pair of example course integrations is provided to illustrate in detail the application of learning and motivation principles to tools-into-functions curriculum integration.     

Notes and Communications ON SENSITIVITY ANALYSIS IN DECISION THEORY*

This note points out a crucial flaw in Evans's recent article [1] on decision sensitivity analysis which was published in this journal. This flaw leads to errors in the majority of his formulae and examples. Correct distance formulae in “probability space,” easily computed from the problem data, are provided. After a critical discussion of some of the concepts Evans employed, we bring to the reader's attention a body of prior work in this area that apparently was overlooked.     

MANAGING ACADEMIC INTERDISCIPLINARY RESEARCH PROJECTS*

This field study provides variables that can be used by practitioners as action levers and by future researchers as the basis for theoretical development. Conclusions from relevant literature and findings from interviews with interdisciplinary research management identified forty variables that were viewed as important to interdisciplinary research project success. After adjusting the data for reliability attenuation, these variables were further analyzed to identify the best prediction equation. The findings suggested that project age or the longevity of the project and open discussion of disagreements were the best predictors of performance.     

HERESY IN DECISION ANALYSIS: MODELING SUBSEQUENT ACTS WITHOUT ROLLBACK *

When making a current decision, like choosing an experiment, a subject will often take into account “subsequent acts” which he does not yet commit to. Common practice requires modeling through preposterior analysis, which treats one act as certain, conditional on the intervening information modeled. This is not logically necessary since the same expected utilities could be obtained by properly conditioning utility on any selection of events (including subsequent acts). The subject could assess utility marginal on subsequent acts or conditional on subsequent acts treated as uncertain events. The preposterior model is a special case of the latter where conditioning information is sufficiently modeled to imply subsequent act probabilities of zero or one. This paper argues that attempts at preposterior modeling are often unsuccessful and have critically flawed much current practice in decision analysis. Simpler approaches such as the “acts-as-events” model are intrinsically less dependent on restrictive assumptions and have been successfully applied to many real-world decisions.     

INFLUENCE OF POWER BASES IN ORGANIZATIONAL DECISION MAKING: A CONTINGENCY MODEL*

The relative influence of four power bases was investigated in four decision-making contexts using data from forty successfully implemented decisions. The analysis suggests that position power decreases in influence while resource power and political power increase in influence as decision contexts vary from less to more uncertainty. Expert power derives its influence when organizational uncertainty is low and when technical uncertainty is reduced, whereas political power thrives when organizational uncertainty is high and when technical uncertainty remains high. Indexing the influence of power bases provides greater understanding of decision processes than the sociological or social-psychological approaches of studying participation.     

DECISION FLEXIBILITY AND PERCEIVED INFORMATION VALUE*

This paper reports the results of an experiment that examined the effects of information evaluators' perceptions of decision flexibility as a determinant of information value. This study suggests that subjects fall into relatively distinct groups that are differentiated by the group members' perceptions of the directionality as well as the magnitude of the effect of decision flexibility on information value. Moreover, subjects' misperceptions of information value reflect individualistic models of information evaluation.     

NONECONOMIC CRITERIA AND THE DECISION PROCESS*

This paper describes a method for systematically including consideration of noneconomic criteria in the decision-making process. It focuses on the Space Applications Program of the National Aeronautics and Space Administration, but is intended as a general outline applicable to decision problems in both government and business. The methodology involves four stages. First, a Criteria Set is developed by a panel of members with diverse backgrounds. Second, the criteria are made operational by posing a series of carefully worded questions to appropriate experts. Then the responses are scaled according to the degree of refinement considered necessary and appropriate. To allow flexibility and to accommodate differences in the degree of precision considered feasible, a number of scaling methods are identified. These range from a simple positive-negative response checkoff to summation of individually-weighted criteria with interaction among criteria. Finally, projects are selected on the basis of predetermined standards of choice.     

COMPUTER-BASED SUPPORT OF ORGANIZATIONAL DECISION MAKING*

The central issue of this research is the extent to which computer facilities can be used to support organizational decision-making processes beyond mere performance of information retrieval. This depends upon the extent to which computers can be made to emulate human perceptual and judgmental processes. We present a framework for understanding these cognitive processes and examine how it applies to organizational decisions. Moreover, the framework furnishes a basis for the design of a generalized, intelligent problem processor. This processor is general in the sense of its ability to support a decision maker's activities, regardless of the decision maker's application area (e.g., urban planning, water-quality planning, etc.). It is intelligent in the sense of its ability to comprehend English-like queries and subsequently formulate models, interface appropriate data with those models, and execute the models to produce some facts or expectations about the problem under consideration.