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ABSTRACTAlternatives involving many factors arc difficult to evaluate because of multiple underlying competing objectives. If evaluation is based on an incomplete set of factors, and if the purpose of the evaluation is to select a single overall best alternative, inferior alternatives may be selected with surprising frequency and/or severe negative impact. At the same time, sensitivity analysis of evaluation scores based on statistical criteria can easily mask the impact and the frequency of selection of inferior alternatives. In this paper, criteria appropriate to reflect the decision impact are developed and both the frequency and impact of the selection of inferior alternatives are demonstrated empirically. Previous studies based on statistical criteria [1] [9] indicated minimal impact on overall evaluation and selection. This paper demonstrates that high statistical criterion values coexist with frequent and/or serious errors of selection.

AbstractThe HOPE (Holistic Orthogonal Parameter Estimation) elicitation procedure assesses multiattribute utility functions via sample holistic judgments. Analyses of actual judgmental data are presented that support two simple assertions: (1) HOPE‐derived utilities are valid and (2) the resulting utility functions are useful for decision making.

ABSTRACTThis paper is a tutorial on axiomatic conjoint measurement. It summarizes recent developments, illustrates the use of these developments in investigating ordinal data relations, relates axiomatic and numerical scaling procedures, describes previous applications of conjoint measurement, and suggests future applications in the decision sciences. Emphasis is placed on the contributions axiomatic conjoint measurement could make to the analyses of ordinal data structures.

AbstractTechniques 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."