This course highlights the analytical methods that decision makers use to gain insight into risk and uncertainty to help students develop the skill and sophistication to artfully use them. Students will become better decision makers as they learn to proactively manage risk in creative ways.
The course emphasizes the design of analyses to fit circumstances and interpretation of results; it does not emphasize the mastery of sophisticated mathematical techniques. It studies and integrates individual judgment and personal intuition in realistic business situations with the most widely applicable methodologies of decision and risk analysis, probability and statistics, competitive analysis, and management science. The goal is always the quality of the decisions made in light of better analysis and deeper thinking, rather than simply the analysis itself.
The first half of this course focuses more on a general framework for thinking about and managing risk. Students use the language of probability distributions to describe the uncertainty they face and simulation as a tool to explore the impact of that uncertainty for the decisions they face. Situations involving uncertain future cash-flow streams and the role of the timing of those cash flows on the valuing of opportunities will be examined. The role data can play in shaping calibration of future uncertainties, specifically touching on the use of parametric probability distributions as a way to describe uncertainty, sampling as a source of data, and regression as a tool to capture the relationship between uncertain quantities to produce better forecasts are considered. Students will also gain skill in spreadsheet modeling.
The second half of the course builds on the first by focusing more on ways to proactively manage risk, particularly through identifying and/or creating opportunities to add value and/or reduce risk through the sequencing of decisions. Students will consider the value of acquiring additional information before decisions have to be made, as well as the value of strategies to reduce (or eliminate) risk at the time of decision. Gaining experience with assessing uncertainties and forecasting probability distributions and learning ways to address competitive situations, where uncertainty includes not knowing how a competitor might behave, will be topics. Students will learn how to influence active competitors that are capable of anticipating their actions and responding to them (in part, via analysis of matrix games). Finally, the use of linear constrained optimization models (and Excel Solver) to aid decisions with a large number of decision variables and constraints will be studied.
- To develop a process for knowing when and how to do managerially relevant analysis under conditions of uncertainty, many decision variables, and unstructured contexts, using both data and personal judgment.
- To develop a framework for understanding uncertainty, a language for describing uncertainty, and methodologies for making decisions in light of uncertainty, all through field-based case studies about practicing managers.
- To provide the basic skills and conceptual understanding of the most widely applicable methodologies of decision and risk analysis, probability and statistics, competitive analysis, optimization, and management science.