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![]() Book Contents
Ch. 9
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Decision Trees and Multi-attribute Utility ModelsA decision tree uses two types of nodes: choice nodes, represented by a square and chance nodes, represented by a circle. An analyst constructs a decision tree. For the chance nodes the probabilities along outgoing branch must sum to one. One then calculates the expected payoffs for each branch in the tree. A decision tree has two major advantages. First, a decision tree shows graphically the relationships among the problem elements. Second, it can deal with more complex situations in a compact form. Multi-attribute utility analysis (MAUA) is a popular decision analysis tool. When this tool is used the attributes are sometimes called decision factors or criteria. The attributes are then given importance weights. The decision-maker provides information about each alternative on each attribute. This step involves measuring the decision-maker's utility or perception of usefulness of an alternative in terms of the desired attributes. There is an extensive specialized literature on Multi-Attribute Utility Analysis (cf., Watson and Buede, 1987; Golub, 1997). MAUA has traditionally been used in selection problems in which there is certainty regarding the attribute levels of the alternatives. Another operations research technique, subjective probability assessment, can be used to develop a distribution of attribute levels when there is uncertainty in these values. These probability distributions can be used in conjunction with MAUA to provide a consistent framework for making selection decisions. |
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