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.     

Notes and Communications “A DECISION THEORY EXAMPLE IN FOOTBALL”: A COMMENT*

Janssen and Daniel analyzed the choice between a one- or a two-point conversion for a particular game situation in college football. Their decision criteria was maximum expected utility based on a von Neumann-Morgenstern utility function defined over the games outcomes. An alternative approach based on a stochastic dominance criterion is presented that does not rely on knowledge of the relative importance of tying vs. winning; rather, it relies on a notion of consistency in the sequential problem.     

Concepts,Theory, and Techniques PRINCIPLES OF MULTIOBJECTIVE OPTIMIZATION*

This paper attempts to isolate and analyze the principal ideas of multiobjective optimization. We do this without casting aspersions on single-objective optimization or championing any one multiobjective technique. We examine each fundamental idea for strengths and weaknesses and subject two—efficiency and utility—to extended consideration. Some general recommendations are made in light of this analysis. Besides the simple advice to retain single-objective optimization as a possible approach, we suggest that three broad classes of multiobjective techniques are very promising in terms of reliably, and believably, achieving a most preferred solution. These are: (1) partial generation of the efficient set, a rubric we use to unify a wide spectrum of both interactive and analytic methods; (2) explicit utility maximization, a much-overlooked approach combining multiattribute decision theory and mathematical programming; and (3) interactive implicit utility maximization, the popular class of methods introduced by Geoffrion, Dyer, and Feinberg [24] and extended significantly by others.     

A DECISION THEORY EXAMPLE IN FOOTBALL

In the 1967 Harvard-Cornell football game, Harvard was ahead 14–0 late in the game when Cornell scored two touchdowns. On both occasions, Cornell tried and failed on the two-point conversion attempt and lost the game 14–12. Postgame arguments were divided on the merits of Cornell's strategy. For this frequently occurring scenario in college football, we derive a maximum expected utility decision rule for the decision of kicking versus running/passing based on the relative utility of a win, a tie, or a loss and the probabilities of success with a kick as opposed to a run/pass.     

PREFERENCE MAPPING OF PERCEIVED INFORMATION STRUCTURE: IMPLICATIONS FOR DECISION SUPPORT SYSTEMS DESIGN

In a recent issue of this journal, Watkins [13] presented an approach for discovery of decision-maker perceptions of the complexity (dimensionality) of information items that might be supplied by a decision support system. Through use of multidimensional scaling and cluster analysis, relatively homogeneous groups of decision makers, sharing common perceptions of various information items, were formed. This prior research was referred to as a first step in suggesting that information reports could be tailored to groups of decision makers classified on the basis of common perceptions of information. The current research extends the prior study by evaluating decision maker preferences for information in a variety of decision-making scenarios in relation to the previously identified perceptions of the information. Based on the results of the study, conclusions are made which suggest that the tailoring of information to groups of decision makers should be based on both perceptions and preferences for information. Even so, it is demonstrated that the decision tasks have an impact on the preferences for information which may affect the attempt to tailor information to groups of decision makers.     

DEVIL'S ADVOCACY AND DIALECTICAL INQUIRY EFFECTS ON PREDICTION PERFORMANCE: TASK INVOLVEMENT AS A MEDIATING VARIABLE

Critics of previous laboratory experiments comparing devil's advocacy (DA) to dialectical inquiry (DI) have suggested that these experiments produced misleading results because (1) they used subjects who had low levels of task involvement and (2) the DI treatment used was confusing to subjects and required further explanation to be useful. The present study examines the effects of four inquiry methods—expert (E), DA, DI, and DI with explanatory statement (DI+)—on subjects' performance at a financial prediction task. Results show that DA, DI, and DI + were superior to E when the state of the world differed significantly from assumptions underlying the expert's plan. For subjects with high task involvement, DI and DI + were more effective than E and DA. The results support some of the criticisms of previous laboratory research and suggest that future research on these decision aids should include task involvement as a factor.     

Decision settings analysis – a tool for analysis and design of human activity systems

The paper describes a methodology to be used for analysis and design of human activity systems. The methodology is based on an analysis of the decision settings whereas most other decision analysis methodologies are analysing the process. The decision concept is analysed and discussed. A distinction between programmed and programmable as well as non-programmed and non-programmable decisions is proposed. A classification of different information types for decision making is presented. A methodology based on a systemic and systematic analysis of the information requirements of an organization is proposed. This methodology also indicates organizational discrepancies and information imbalances. The methodology focuses the settings of the decisions on all levels of organizations. The methodology can be regarded as a dynamic, learning system. The author proposes further research on the individuals decision making abilities.     

Estimation of the smallest scale parameter of two-parameter exponential distributions

Improved point and interval estimation of the smallest scale parameter of n independent populations following two-parameter exponential distributions are studied. The model is formulated in such a way that allows for treating the estimation of the smallest scale parameter as a problem of estimating an unrestricted scale parameter in the presence of a nuisance parameter. The classes of improved point and interval estimators are enriched with Stein-type, Brewster and Zidek-type, Maruyama-type and Strawderman-type improved estimators under both quadratic and entropy losses, whereas using as a criterion the coverage probability, with Stein-type, Brewster and Zidek-type, and Maruyama-type improved intervals. The sampling framework considered incorporates important life-testing schemes such as i.i.d. sampling, type-II censoring, progressive type-II censoring, adaptive progressive type-II censoring, and record values.     

The Career Decision‐Making Difficulties Questionnaire: A Case for Item‐Level Interpretation

The Career Decision‐Making Difficulties Questionnaire (CDDQ; Gati, Krausz, & Osipow, 1996) has long been demonstrated as a useful instrument for career counseling practice. Several case studies have demonstrated how and why using the CDDQ facilitates the career counseling process (e.g., Amir, Gati, & Kleiman, 2008; Gati & Levin, 2014; Levin & Gati, 2014). This study explores how an in‐depth analysis of a case study conducted at the item level can provide career counselors with a richer understanding of their clients’ needs. The case study also emphasizes how administering and providing feedback on the CDDQ in career counseling sessions is likely to foster working alliances. The study concludes by presenting an intervention map based on the CDDQ taxonomy.     

Testing Prospect Theory in a Deterministic Multiple Criteria Decision-Making Environment*

Prospect theory by Kahneman and Tversky [7] is tested in a deterministic multiple criteria decision-making context. In two experiments conducted in classroom settings subjects made pairwise preference comparisons of condominiums for sale. The results of the experiments indicate that the traditional value model did not explain the subjects' revealed preferences as well as the prospect model. We conclude that prospect theory is a reasonable model of choice for many individuals in such a context.