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AbstractThe problem of uncertainty in the outcome of decisions has tended to be glossed over by many MCDM methods, with the exception of multiattribute utility theory (MAUT). MAUT does, however, require quite complicated preference elicitations and knowledge of the full multivariate distribution of outcomes. Results from a series of simulation studies indicate that the preference orderings of MAUT are only minimally changed when using a simple additive aggregation of marginal utilities, especially in relation to the natural imprecisions inherent in preference elicitation. It is shown in the simulations that by far the most critical aspect of multicriteria decision analysis under uncertainty is not the form of aggregation, but the correct elicitation of marginal utilities which properly represent decision maker preferences over gambles. We relate the results obtained here to other results on the approximation of distributions by three‐ or five‐point discrete distributions, and suggest that the use of deterministic MCDM methods of any form (not necessarily value function techniques), applied to an extended formulation in which each criterion measure is repeated for three or five 'scenarios', can be justified.

AbstractA search model is formulated in which positive information may be obtained, through the detection of trails, as to the target's earlier whereabouts. The corresponding Bayesian update formulas for target location probabilities are derived. The model does not appear to be amenable to rigorous optimization. A moving‐horizon rule, and a heuristic simplification thereof, are, however, derived. In two numerical examples it is demonstrated that actively designing for detecting trail information, through use of these moving‐horizon rules, has substantial potential advantage over using, for example, myopic rules even if the positive information is adaptively incorporated into location probabilities before applying the latter rules in each time period.

AbstractThis paper concerns the integration of goal programming and scenario planning as an aid to decision making under uncertainty. Goal programming as a methodology emphasises the resolution of conflict among criteria; scenario planning focuses on the treatment of uncertainty relating to future states of the world. Integrating the two methodologies is based on the simple formulation of a super‐goal programme consisting of one scenario‐specific goal program in each scenario. Issues relating to the structuring of the super‐problem, aggregation both within and over scenarios, and the incorporation of probabilistic information are discussed. Copyright © 2005 John Wiley & Sons, Ltd.