As computational simulation becomes more commonplace in design and decision processes, it is important for you to consider how uncertainty could be impacting your perceptions of performance, trade-offs, and consequences. A single simulation can be seen as mapping from your design alternative to its performance objectives based on a single set of assumptions and choices used in the model's representation of the system of interest. Monte Carlo simulation can be thought of as accounting for uncertainties in your modeling assumptions and choices where you can simulate performance if your design resides in many different but plausible alternative worlds (i.e., many states of the world).

In this course, you will broaden the types of performance measures that can be used in your decision framings to include risks and vulnerabilities. You will assess the value of Monte Carlo simulation in better understanding the sensitivities, risks, and consequences of your candidate design alternatives. You will also explore the emerging insights andanalytics associated with decision making under deep uncertainty. Given the many ways that our decisions shape concerns surrounding people, profit, and planet, finding solutions that maintain acceptable performance across many plausible futures then explicitly mapping their vulnerabilities is extremely valuable.