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Public relations decision making by U.S. Air Force public affairs personnel was evaluated by using multiattribute utility theory. Exploratory cluster analysis revealed that only 48% of the decisions adequately fit the public relations excellence criteria of accommodation and long-term relationship building. Fourteen percent of the decisions were based on asymmetrical options with a clear emphasis on leverage for the organization, and 37% of the decisions focused on communication tactics. There was a negative correlation between strength of relationship with the supervisor and preference for symmetrical solutions. Furthermore, female employees reported a weaker relationship with their supervisors than their male counterparts.

AbstractA self‐reflecting signed order is a preference relation that compares relative likes and dislikes for items in a set X by jointly ordering X and a disjoint copy of X. If you would like Jones but not Smith appointed to a committee, and also think it more important to exclude Smith than include Jones, your self‐reflecting signed order records this information. We review basic representational theory for signed orders, then examine them when X is multiattributed. Axioms for additive measurement of multiattribute self‐reflecting signed orders are specified for several X‐structures.

It is well documented that the way a static choice task is "framed" can dramatically alter choice behavior, often leading to observable preference reversals. This framing effect appears to result from perceived changes in the nature or location of a person's initial reference point, but it is not clear how framing effects might generalize to performance on dynamic decision making tasks that are characterized by high workload, time constraints, risk, or stress. A study was conducted to examine the hypothesis that framing can introduce affective components to the decision making process and can influence, either favorably (positive frame) or adversely (negative frame), the implementation and use of decision making strategies in dynamic high-workload environments. Results indicated that negative frame participants were significantly impaired in developing and employing a simple optimal decision strategy relative to a positive frame group. Discussion focuses on implications of these results for models of dynamic decision making.

AbstractResearch indicates that selecting a strategy to best exploit a new technology is a complex decision‐making process. The task involves making a series of decisions with multiple alternatives, each to be evaluated by multiple criteria whose values have high levels of uncertainty. This paper presents a methodology for modelling a new technology decision using decision trees and an optimizing algorithm. A problem of a mining company considering the adoption of new technology is used to illustrate the decision‐making task and modelling methodology. A numerical solution to the case demonstrates the potential of the optimizing technique in strategy selection.

Portfolio decision analysis models support selection of a portfolio of projects with multiple objectives and limited resources. These models often rely on the additive-linear portfolio value function, although empirical evidence suggests that the underlying preference assumptions do not always hold. In this paper we relax these assumptions and derive a more general class of portfolio value functions that deploy symmetric multilinear functions to capture nonlinearities in the criterion-specific portfolio values. These values can be aggregated with an additive or a multilinear function, allowing a rich representation of preferences among the multiple objectives. We develop novel techniques for eliciting these value functions and also discuss the use of existing techniques that are often applied in practice. Furthermore, we demonstrate that the value functions can be maximized for problem sizes of practical relevance using an implicit enumeration algorithm or an approximate mixed-integer linear programming model. Application of the results is illustrated with an example in ecological conservation site selection.

AbstractTwo commonly used elicitation modes on strength of preference, equivalence and ratio judgments, were compared in an experiment. The result from the experiment showed that ratio judgments were less effective than equivalence judgments. Based on an iterative design for eliciting multiattribute preference structures, equivalence judgments outperformed ratio judgments in estimating single‐attribute measurable value functions, while being nearly more effective than ratio judgments in assessing multiattribute preference structures. The implications of the results from the experiment are that multiattribute decision‐making techniques should take advantage of the decision maker's inclination of making effective equivalence trade‐off judgments, and that useful techniques should be devised to incorporate different commonly used techniques, such as multiattribute utility theory and the Analytic Hierarchy Process, to elicit and consolidate equivalence trade‐off judgments.

AbstractStrategic investment decisions entail intertemporal resource allocation. Given relative resource availability and favourable environmental dynamics, effective strategic investment planning is a major determinant of overall corporate financial performance. This paper advances a methodology for optimizing intertemporal investment decisions through a consensus‐oriented process. This process entails identifying the strategic investment factors, their relative importance, and their assumed values relevant to the timing of each investment program. The paper also provides a methodology for determining the system's priority formulation as the model's objective function. This information generates an optimization matrix designated by investment programs and time. This matrix is then optimized through a hierarchical goal‐programming model that maximizes overall strategic investment decisions. The model was applied to a major capital investment program in the state of New York.RésuméLes décisions d'investissements stratégiques impliquent une allocation de ressources intertemporelles. étant donné la disponibilité relative des ressources et de bonnes dynamiques environnementales, une planification efficace des investissements stratégiques est le déterminant principal de l'ensemble des résultats financiers d'une entreprise. La présente recherche propose une méthodologie pour optimiser les décisions d'investissements intertemporels au moyen d'un processus orienté vers le consensus (consensus‐oriented). Ce processus comprend tout d'abord l'identification des facteurs d'investissements stratégiques, puis leur importance relative et leur valeur supposée en rapport avec l'échelonnage dans le temps de chaque programme d'investissement. Elle propose également une méthodologie pour déterminer la formulation de la priorité du système ainsi que la fonction objective du modèle. Cette information conduit à la construction d'une matrice d'optimisation par programmes et par temps d'investissement. Cette matrice est ensuite optimisée grǎce à un modèle de programmation à but hiérarchique qui maximise les décisions globales d'investissements stratégiques. Ce modèle à été appliqué à un programme d'investissement en immobilisations dans l'état de New York.