BUS 650 - Decision Modeling
Advanced topics and tools for analysis of decision problems, focusing on modeling the real-world complications that are simplified away when introducing decision analysis. In particular, we address the issues of: too many alternatives (leading us to resource pricing, linear programming using Solver, and other optimization techniques); aversion to risk (utility, using PrecisionTree); multiple, conflicting objectives (multi-attribute decision making and value-focused thinking); and too many, complex outcomes (Monte Carlo simulation using @RISK). In addition, we look at the special case of risks involving threats to life & limb, and we examine the special features of dynamic models and complex systems. The primary course objective is to improve managerial effectiveness through clearer thinking about complex decision issues, and through the application of powerful analytical tools to a wide variety of common management problems. This course will include advanced topics and tools for decision analysis of problems with “real-world” complications. First of all, complex problems may overwhelm the simple decision tree methodology; here we consider influence diagrams (an alternative representation of decision problems) and approaches that permit examination of larger scale problems. Further, we cannot always adopt the simplifying “billionaire’s perspective”; this course examines risk aversion, multiple decision criteria, and value tradeoffs. Finally, complex problems may be interactive and dynamic; the course explores the behavior of dynamic systems, including discussions of complexity and chaos. Essentially this means learning how to extend decision analysis concepts and techniques to include attitudes about risk, multiple conflicting objectives, complex uncertainties (requiring Monte Carlo simulation), complex alternatives (requiring linear programming and other optimization techniques), and dynamic interactions.