A number of multi‐criteria decision support techniques have emerged in recent years that use varying computational approaches to arrive at the most desirable solution and thereby ‘recommend’ a course of action. Decision makers who use the results of this analytic work should be assured that the computational schemes used by their supporting analysts or decision support software produce the appropriate solutions. We conducted a series of simulation experiments that compared the top‐ranked options resulting from the computational algorithms that support Multi‐Attribute Value Theory (MAVT) and three methods that are reported in the literature that allow rank reversals, the change in rank order of two options when an unrelated option is added or deleted from the analysis: the Analytical Hierarchy Process (AHP), Percentaging and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). We also included a Fuzzy algorithm proposed by Yager to gauge its consistency with the other algorithms, even though it is not subject to rank reversals. These experiments demonstrated that the MAVT and AHP techniques, when provided with the same decision outcome data, very often identify the same alternative as ‘best’. The other techniques are noticeably less consistent with MAVT, the Fuzzy algorithm being the least consistent. The situations under which the most frequent and significant differences occurred were dependent upon the method.
The results of our experiments indicate that other issues (e.g. the processes used for problem structuring and the elicitation of value weights) are likely to be of greater significance to problem outcome (based on our experience) than the choice between the computational algorithms of MAVT and AHP. The results cause us to be concerned about the use of the other methods.
Over the past three decades, significant improvements in the computer and computational sciences have enabled automated support for increasingly complex decision situations. One example of this progress is the influence diagram, which is simultaneously a graphical and mathematical model of a decision situation. Influence diagrams are a proven asset in the tool kit of decision analysts and they are making the power of decision analytic modelling more accessible to professionals in other disciplines.To develop influence diagrams, decision analysts guide decisionmakers and subject matter experts through a discovery process that necessarily migrates from the unstructured to the structured. These highly complex decision situations often require inputs from many different people with diverse expertise. One shortcoming of influence diagrams (and other methods) is that they do not specify a cohesive and comprehensive process for structuring the interactions of options, values, and uncertainties during the initial development of a decision model. This complicates (and sometimes precludes) the process of developing a comprehensive model because the model reaches a level of complexity that is very difficult for individual domain experts to think about. This paper introduces a formally specified method for eliciting influence diagram structure. We extend influence diagram notation to include special categories of value nodes that more explicitly define fundamental objectives hierarchy components. We then provide provably correct methods for decomposing the model, eliciting additional detail, and reassembling the model into a single graph. These methods make two important contributions to the modelling science: first, it is a necessary step toward providing automated model elicitation tools; second, it is an example of technology transfer from Bayesian Belief Networks to Influence Diagrams.
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