Modeling Uncertainty and Risk
Rarely does a single factor alone predict an outcome, which is why decision making is never as simple as we would like it to be. In a competitive business environment, not taking this uncertainty into account has serious costs. In this course, you will use foundations in probability to describe risk mathematically and incorporate those calculations into your decisions so you can take them to the next level. Working through increasingly complex modeling situations, you will learn to use estimates of probable future outcomes for Go/No-Go decisions and to run a Monte Carlo simulation allowing you to examine outcomes that vary based on multiple, interdependent decisions.
Who Should Take this Course?
This course is appropriate for anyone from analyst to SVP-level who is looking for a deeper understanding of how to perform the statistical analyses that support key business decisions. Course content draws on examples across all business types.
Calculating marginal value for a binary decision
Determining optimal values for a repeating, sequential decision
Building risk aversion into your model
Calculating utility for a given decision
Developing and using a Monte Carlo simulation
Performing sensitivity analysis
Using expected utility to accommodate risk
- Christopher Anderson, Professor, School of Hotel Administration