Decision Analytics and Modeling

Catalog description

Successful management requires the ability to recognize a decision problem, understand its essential features, and make a smart choice. However, many decision problems — particularly those involving uncertainty or many variables — are difficult to grasp intuitively. In these cases we may benefit from using a computer-based mathematical model to explore and evaluate the possibilities in a systematic fashion. This course introduces several commonly used modeling frameworks and provides an introduction to the art and science of modeling decisions. The ideas and skills learned in this course are applicable in most areas of business. The course is divided into 3 parts: (a) the use of decision trees for structuring decision problems under uncertainty; (b) Monte Carlo simulation, a technique for simulating decision situations with many uncertainties; (c) optimization, an approach for finding the best possible solution in problems with many decision variables and constraints. This course is for MQM: Business Analytics students.