TABLE OF CONTENTS After completing this chapter, you should be able to:
Explain what "decision analysis" is and how it can be used in IT investment decision-making.
Explain how to compute answers for a variety of "decision analysis" methods.
Define the decision environments under which "decision analysis" can operate, and explain how differing environments require differing methodology.
Explain what "goal programming" is and how as a multi-objective programming approach can be used in IT investment decision-making.
Understand how to formulate "goal programming" models.
This chapter describes two commonly used sets of decision-making methodologies: decision theory and multi-objective programming. Decision theory is really a collection of methodologies and principles used to make single, alternative choice decisions (i.e., where the decision is to select one alternative from a set of others). The procedural mathematics used to render a decision using decision theory methods and their application in IT decision-making will be presented.
Multi-objective programming (MOP) methods are also a collection of decision-making methods, capable of choosing any number of alternatives from a set of alternatives or proportions of alternatives where the problem requires that type of solution. Due to the complexity o f MOP methods, our discussion in this chapter will be limited to one type of MOP (i.e., goal programming) and only the formulation and solution interpretation.
Decision theory (DT) is a field of study that applies mathematical and statistical methodologies to help provide information on which decisions can be made (Meredith et al.
