Week 4 Assignm
Systems Modeling Theory
Instructor:

Philip McIntire
July 27, 2013 The environment in which decisions must be made is more complex than ever before. Informed consumers, employees, and shareholders demand greater public consciousness, responsibility, and accountability from corporate and governmental decision makers.
Decision analysis captures the dynamic nature of decision processes by prescribing a decision strategy that indicates what action should be chosen initially and what further actions should be selected for each subsequent event that could occur. For instance, a decision strategy might suggest an initial test market for a new product and then, based on the results, either cancel the product, initiate further testing, or begin a full scale marketing and sales effort. Thus, in describing the alternatives, one must simultaneously specify the decision points, events that may occur between them, and the information that can be learned in the process. Decision analysis can be divided into four steps: structuring the problem; formulating inference and preference models; eliciting proba- bilities and utilities; and exploring the numerical model results. Structuring the problem is the most important and difficult part of the analysis process. Trees are the most common decision analytic structures. Decision trees are produced by algorithms that identify various ways of splitting a data set into branch-like segments. These segments form an inverted decision tree that originates with a root node at the top of the tree. The object of analysis is reflected in this root node as a simple, one-dimensional display in the decision tree interface. The name of the field of data that is the object of analysis is usually displayed, along with the spread or distribution of the values that are contained in that field. A decision tree shows