EIGENVALUES FOR THE DECISION SCIENCES

This paper gives an example of how to structure a set of lectures on eigenvalues and eigenvectors for students of economics, finance, or any of the other decision sciences. It is presented in a fairly self-contained style, suitable for the relative beginner, but its main purpose is to present the instructor with a way to motivate definitions and tie key concepts together.     

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.     

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.     

Education CREATIVE THINKING AND INNOVATIVE EDUCATION IN THE DECISION SCIENCES

Creative problem solving is seldom addressed directly in the decision sciences literature. The first half of this paper reviews current thinking about creativity and its educational importance. The remainder addresses creative problem-solving processes and, in particular, the process developed by Parnes, Noller and Biondi [24] and Osborn [23]. This process has been integrated into an OR/MS problem-solving course which is described. Conclusions and implications for decision sciences education and practice in general are discussed.     

TEACHING EFFECTIVENESS IN THE DECISION SCIENCES: THE USE OF STUDENT EVALUATIONS

This article presents the results of an AIDS-sponsored investigation of teaching effectiveness in the decision sciences. Two national samples were employed in the study: a sample of 247 business school deans and a sample of 3,292 students from 32 different business schools. Included among the findings are that 87 percent of the deans responding indicate they require some sort of faculty teaching effectiveness evaluation, and that great care must be exercised when interpreting student ratings of an individual instructor's teaching effectiveness.     

SEASONAL ADJUSTMENT FOR THE DECISION MAKER

A procedure is presented for removing the influence of seasonal variation from an observed time series. The procedure leads to the development of moving average filters which optimally adjust the data based upon their unique characteristics. In this paper optimal adjustments are determined by the requirements of the decision maker.     

Applications and Implementation DECISION SUPPORT SYSTEMS: AN EMPIRICAL ASSESSMENT FOR DECISION MAKING

A laboratory experiment examined the effects of applying decision support system (DSS) technology to decision making in ill-structured problem environments under varying information conditions. Marketing executives participated in the experiment which investigated the effects of DSS availability, DSS training, and data availability on dependent variables that included: (1) the number of alternatives considered by a subject during decision making, (2) the period of time spent by a subject to complete the decision-making process, (3) the subject's perceived confidence in the decisions he or she had made, (4) the amount of data considered by a subject's during decision making, (5) the individual subject's decision processing, and (6) the subject's performance overall. Our results indicate that all three factors significantly affect the number of alternatives considered by subjects during the decision-making process. We therefore suggest that DSS training be coordinated with decision training in order to realize the potential of DSSs as described in the DSS literature.     

A DECISION MODEL FOR SELECTING ALTERNATIVE HYPOTHESES

One important aspect of experimental design is the decision whether to use a one- or two-tailed hypothesis test. Assuming that the sample size and the level of significance have been specified, the selection of an alternative hypothesis can materially affect the conclusion drawn from the experiment. In the past, this selection was made on a subjective basis or for pragmatic reasons which can bias the experiment. This paper suggests an expected value approach, maximizing the expected power of the test, to place the selection of alternative hypotheses in an objective and reliable decision framework.     

ADVERTISING DECISION RULES FOR MARKET SHARE MODELS

This paper develops rules for setting advertising budgets when long term effects of advertising and competitive promotional spending are considered in a market share model. In both deterministic and probabilistic cases, a firm's optimal advertising budget is shown to be relatively insensitive to the level of competitive advertising. The key determinant of the advertising level is long run expected profit, and the budgeting rule developed is to spend a constant percentage of long run profit on advertising. Reasons for this result are explored.     

RAPID CONVERGENCE TECHNIQUES FOR MARKOV DECISION PROCESSES*

When a person is working with large scale Markov Decision Processes, he normally uses the policy iteration approach developed by Howard [1] and modified by White [3]. White's modification makes use of the method of successive approximations. Computational experience has shown that for many processes, the rate of convergence of the successive approximation is very slow. In this paper, techniques for speeding convergence are discussed. Numerical examples and computational experience which show the relative merits of the various approaches are presented.     

A METHODOLOGY FOR DECISION MAKING THROUGH QUEUING ANALYSIS

Many queuing situations present themselves in which multiple alternatives should be evaluated. This is especially true in cases where the alternatives refer to the number of service channels and types of service channels to be utilized. In such cases, it is appropriate to develop suitable queuing models and introduce the additional exogenous variables in the model graphically. This paper presents an example where such a methodology of model construction is feasible and helpful in deciding guidelines for making decisions. The working methodology is explained with a numerical illustration and guidelines for using the proposed methodology are described.     

A PENSION DECISION MODEL FOR THE EMPLOYER*

Recently, the Financial Accounting Standards Board (FASB) reevaluated accounting for pension plans. The issue is emotional and highly political in nature. The FASB attempted to justify its approach on the basis of measuring economic activity, but it failed to provide much in the way of analytical support. This paper provides a managerial decision model and an economic basis for the existence of pension plans. A pension plan is described as a cost-saving, risk-sharing, incentive contract. The analysis is developed using agency theory. The model presented here meets three suggested objectives of an employer: 1. Maximization of utility through the maximization of profit 2. Ability to conform the risk characteristics of an employment contract to the risk characteristics of the employer 3. Diversification of the risk inherent in the employment contract Profit is maximized by producing cost savings associated with employee tenure and loyalty. Sharing cost savings with employees (i.e., offering a pension plan) meets the above objectives. The employer determines the optimal sharing rate for the expected cost savings. An examination of the employer's underlying decision process reveals implications for pension plan accounting which generally are consistent with and support the FASB's Statement of Financial Accounting Standards No. 87 [5].     

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.     

A MODEL-BASED DECISION SUPPORT SYSTEM FOR SCHEDULING LUMBER DRYING OPERATIONS

Raw lumber must be dried to a specified level of moisture content before it can be used to make furniture. This paper deals with a model-based decision support system (DSS) for a local furniture manufacturing company to assist its management in scheduling lumber drying operations. In addition to buying ready-to-use dried lumber from vendors at a premium, the company processes raw lumber in house using two production process that require various lengths of processing time in predryers and dry-kilns. Given the demand for various types of dried lumber over a specified planning horizon, the processing times and costs for each production process, technological restrictions, and management policies, the problem of interest is to satisfy the demand at a minimum cost. The DSS incorporates the mathematical formulation of this problem, is user friendly, maintains model and data independence, and generates the necessary reports, including loading and unloading schedules for the equipment.     

DECISION SUPPORT SYSTEMS FOR ILL-STRUCTURED PROBLEMS: AN EMPIRICAL STUDY

Decision support systems (DSSs) are more complex than most other traditional decision-aid systems. For what types of problems are they more effective, and what design characteristics make them more effective? The laboratory experiment reported here examined the effect of three design characteristics of these systems in the context of decision makers faced with ill-structured problems. The characteristics were presence or absence of decision-aid heuristics, degree of interaction between the user and the system, and whether or not the system was computerized. The dependent variables were (1) quality of user performance, (2) user productivity of ideas, (3) user confidence in the quality of his/her performance, (4) user satisfaction with the decision aid or support system, (5) changes in user attitude toward the problem addressed, and (6) changes in user attitude toward computers. Use of heuristics and increased interaction had positive effects on decision quality, user productivity, and attitude toward computers; they had negative effects on user confidence, satisfaction, and attitude toward the problem addressed. Whether or not the system was computerized did not have a significant effect on any dependent variable. The findings concerning negative effects, in particular, suggest the need for research on the design of heuristics for addressing ill-structured problems—heuristics that will deliver the positive but not the negative effects observed in this study. The findings also suggest the need for research on how to benefit from computers in the context of solving ill-structured problems.     

A DYNAMIC PROGRAMMING MODEL FOR OPTIMAL ALLOCATION OF DECISION TIME

Recurrent decision making by a lower-level manager can be viewed as a sequential decision process in which time and uncertainty are limiting factors. Under these conditions, the manager must determine how to best utilize his decision making time consistent with his own particular set of decision values. A dynamic programming model was devised to determine the optimal (consistent) allocation of decision time among five different types of problems for a sequence of simulated recurrent decision situations. Fifty-one lower-level managers were interviewed about their use of decision time and decision procedures. The model was validated by comparing model assumptions and results with the findings from the interviews. The model was used to determine the effects of variations in the levels of time available and uncertainty upon the optimal allocation of decision time.     

COMPUTER GRAPHICS AS DECISION AIDS: DIRECTIONS FOR RESEARCH*

Computer-generated graphics are becoming increasingly available to decision makers. Despite claims on the part of vendors that the use of graphics will improve decision speed and quality over traditional methods of data display, the available evidence is far from supportive. Initial studies show graphics to be no more effective in communicating information than tables. Correct interpretation of graphical displays appears to require training, which most users lack. Furthermore, there is evidence that those features that make a graph visually attractive—such as color, design complexity, and realism—may actually detract from accurate comprehension. This paper summarizes the literature dealing with the human use of graphics, develops several propositions based on persistent trends in the literature, and suggests directions for future research.