Publisher's Synopsis
In the last two decades, the field of decision analysis has had a fast-increasing impact in the way organizations, private and public, are making strategic and tactical decisions. Major advances in theory, modeling tools and computational techniques, made possible by leaps in computer science, have made decision analysis increasingly essential in business and government decision making. These techniques are being used routinely by some of the largest firms in business and in a myriad of applications, from yield management to product development to investment to medicine, to name a few. Decision analysis is a prescriptive discipline that is designed to assist people in making better decisions. It is prescriptive in the sense that is uses a normative framework, and a set of tools and procedures to help the decision maker model, optimize and analyze complex, hard decisions. In contrast, there exists a descriptive view of decision making, which focuses on how people actually make decisions. This view, which relies heavily on psychology, provides ample experimental evidence that people generally process information, assess probabilities, and make decisions in ways inconsistent with the rational prescription of decision analysis. These findings only emphasize the importance of using the tools of decision analysis in making good decisions, particularly when they are difficult and important. Decision Analytics- Theoretical, Empirical and Analytical Research endorses the applications of computer technology, operations research, statistics, and simulation to decision making and problem-solving in all organizations and enterprises within the private and public sectors. It focuses on logical as well as dogmatic analytics taking organizations to a higher degree of intelligence and competitive advantage. While predictive analytics, such as forecasting, emphasize the future, prescriptive analytics, such as optimization, enable organizations to choose the best course of action. The combination of predictive and prescriptive analytics can help organizations achieve both efficiency and effectiveness. The purpose of this compilation is to assist decision makers in making better decisions in complex situations, usually under uncertainty. The quality of the decisions is measured by their expected consequences and the stated preferences of the decision maker(s). The decision analytic framework helps the decision maker think systematically about his or her objectives, preferences, and the structure and uncertainty in the problem, then model quantitatively these and other important aspects of the problem and their interrelationship.
