Finally, some typical problem classes are examined and some requirements for prototypical structures are discussed. An introduction to problem structuring Decision analysis can be divided into four steps: structuring the problem; formulating inference and preference models; eliciting probabilities and utilities; and exploring the numerical model results. Prac* This research was supported by a grant from the Department of Defense and was monitored by the Engineering Psychology Programs of the Office of Naval Research, under contract
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While writing this paper, the author discussed the problem of structuring extensively with Helmut Jungermann. The present version owes much to his thought. Please don’t take footnote 3 too seriously. It is part of a footnote war between Ralph Keeney and me. ** Presently with the Social Science Research Institute, University of Southern California, University Park, Los Angeles, CA 90007, (213) 741-6955. 12 D. von Winterfeldt /Structuring decision problems titioners of decision analysis generally agree that structuring is the most important and difficult step of the analysis.
Yet, until recently, decision analytic research has all but ignored structuring, concentrating instead on questions of modeling and elicitation. As a result, structuring was, and to some extent still is, considered the ‘art’ part of decision analysis. This paper examines some attempts to turn this art into a science. Trees are the most common decision analytic structures. Decision trees, for example, represent the sequential aspects of a decision problem (see Raiffa 1968; Brown et al. 1974). Other examples are goal trees for the representations of values (Keeney and Raiffa 1976) and event trees for the representation f inferential problem aspects (Kelly and Barclay 1973). In fact, trees so much dominate decision analytic structures that structuring is often considered synonymous to building a tree.