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.     

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.     

SENSITIVITY ANALYSIS IN DECISION THEORY

This paper explores linear programming-like sensitivity analysis in decision theory. In particular, the paper considers the sensitivity of an optimal decision to changes in probabilities of the states of nature and the development of “confidence spheres” to bound arbitrary parametric changes in the probability vector. Such information can be used to assess the accuracy required in assigning probabilities and the confidence in the maximumutility decision.     

AN EMPIRICAL ASSESSMENT OF COMPUTER-ASSISTED DECISION ANALYSIS*

Decision analysis tools often are used in semistructured and ill-structured situations. While some researchers have argued that computerized decision analysis programs may improve decision quality in such situations, research support for this assertion is weak. In this laboratory study, business students used a computerized decision-analysis program with short strategic-management cases to prepare decision reports. Independent raters' perceptions of aided and unaided decision performance were contrasted, attitudes of students toward the program were assessed, individual differences were correlated with attitudes, and the program's impact on students' perceptions of their decision processes and performance was examined. Student attitudes toward the computerized aid were favorable, and individual differences in reactions generally were as predicted. However, the program did not improve the independent ratings of students' decision reports and recommendations. These findings suggest that computerized decision aids should be adopted cautiously. If such aids result in positive user affect and heightened decision confidence without corresponding improvements in decision quality, they may be dysfunctional. Rigorous research methodologies which incorporate independent evaluations of analyses and decisions are recommended for use in future research on computerized decision-analysis programs.     

ADAPTIVE CONTROL THEORY: SURVEY AND POTENTIAL APPLICATIONS TO DECISION PROCESSES*

An important and distinct feature of a class of decision processes discussed in this paper is the possibility of gathering additional information between decisions and using it to improve future decisions. The analysis of these so-called sequential decision processes is both complicated and far-reaching in its implications. The purpose of this paper is to describe this class of decision processes to make its potential both more readily understood and better appreciated.     

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.     

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.     

EMPIRICAL AND JUDGMENT-BASED SALES-FORCE DECISION MODELS: A COMPARATIVE ANALYSIS*

Multiple-predictor empirical and single-predictor judgment-based models represent the two basic types of sales-force decision models (SDMs). The important similarities and differences between the two modeling approaches are identified and discussed. The SDM literature is reviewed to determine how each model type has been applied and to evaluate previous attempts at model validation. A research study designed to compare the normative sales-call allocations generated from each modeling approach is presented. Study results indicate that empirical and judgment-based SDMs would produce similar normative guidelines. The implications of this finding are discussed.     

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.     

USING FISHBURN'S TECHNIQUES FOR ANALYSIS OF DECISION TREES: SOME EXAMPLES*

Techniques for the analysis of payoff matrices with “imprecise” assessment of state probabilities are applied to the analysis of decision trees in normal form. The analytical techniques, due to Peter Fishburn, are summarized and then illustrated by analyzing two decision trees. Problem I, a two state problem, provides geometrical analogs for the two most “imprecise” probability assessments and simple analytical and geometrical analogs for the least “imprecise” probability assessment. Problem II, a three state problem, illustrates the application of all four precision levels, from most “imprecise” through least “imprecise.”     

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.     

THE INDUCE VERSUS PURCHASE DECISION: AN EMPIRICAL ANALYSIS OF INDUSTRIAL R&D*

With the recent slowdown in productivity growth within the economy, R&D has come under scrutiny as a policy target variable. If such targeting is to be effective, it must be realized that not all innovations employed within a firm are induced by the firm through its own R&D: many innovations are purchased through technological licensing or in the form of new capital equipment. Here, interfirm differences in this “make” versus “buy” strategy are analyzed within the context of the Utterback-Abernathy production process lifecycle. Our findings suggest that (1) alternative sources to a firm's R&D for stimulating innovation may prove a viable strategy for federal targeting and (2) extrapolating the Utterback-Abernathy model to an industry formulation has empirical validity.     

A CASE IN ON-LINE DECISION ANALYSIS FOR PRODUCT PLANNING1

This report presents an application of quantitative decision-analytic techniques to consider an opportunity for capital investment in a new product. Decision analysis is a well-established technique for evaluating major decisions in which substantial resources and time are available for the analysis and the problem is definitely formulated at the outset. This case study, however, illustrates how decision-analytic techniques can be used on-line to improve decision making in a situation in which time and resources are limited, and the manager's perception of the problem changes in response to feedback from the analysis. The basic decision problem was whether or not the AIL division of Cutler-Hammer, Incorporated should purchase a six-month option on a flight-safety system patent. The president of AIL had only a few weeks in which to make a decision. The analytic approach focused on a probabilistic discounted earnings model. This model was refined over a two-week period through a sequence of iterations which incorporated the results of both direct and indirect assessments of probabilities and values. The analysis affected the company's decision process by providing a vehicle for structuring the ongoing communication between AIL's president and his advisors, and by focusing their attention on the most important issues. In the end, the analysis facilitated a unanimous decision from the decision-making group not to purchase the option, but to consider participating in the license as a subcontractor at a later date. This strategy had not been considered at the outset.     

决策支持理论研究

本文提出了开环与闭环决策系统的概念,在此基础上初步讨论了决策支持理论,并提出了决策支持系统工程与系统集成的研究框架。在决策科学中,现在绝大部分的研究工作集中在对开环决策系统的研究上,这对于研究决策与决策支持是必须的,但这还远不够,如果要真正提高决策及决策支持的有效性,就必须研究闭环决策系统,必须把决策问题识别与求解放在统一的框架中进行研究。