Don't let the absence of data or the lack of appropriate data affect your decision-making. In this introductory course you will discover the theory behind Structured Expert Judgment.

In an increasingly data-driven world, data and its use aren't always all it's cracked up to be. This course aims to explain how expert opinion can help in many areas where complex decisions need to be made.

For instance, how can you predict volcano activity when no eruptions have been recorded over a long period of time? Or how can you predict how many people will be resistant to antibiotics in a country where there is no available data at national level? Or how about estimating the time needed to evacuate people in flood risk areas?

In situations like these, expert opinions are needed to address complex decision-making problems. This course will show you the basics of various techniques that use expert opinion for uncertainty quantification. These techniques vary from the informal and undocumented opinion of one expert to a fully documented and formal elicitation of a panel of experts, such as the Classical Model (CM) or Cooke's method, which is arguably the most rigorous method for performing Structured Expert Judgment.

CM, developed at TU Delft by Roger Cooke, has been successfully applied for over 30 years in areas as diverse as climate change, disaster management, epidemiology, public and global health, ecology, aeronautics/aerospace, nuclear safety, environment and ecology, engineering and many others.

What you'll learn

By the end of the course all learners will be able to:

• Recognize when and in which settings the Classical Model (CM) can be used for performing Structured Expert Judgment
• Understand when to account for uncertainty assessments in complex decision-making context when data pose issues
• Know how CM can be used to analyze expert data and obtain answers to questions of interest
• Explore an optional IDEA Protocol module, which uses a different method of performing Structured Expert Judgment.

Verified learners will have the added benefit of being able to:

• Get an in-depth perspective on the CM method theory
• Access optional modules about dependence elicitation and eliciting probabilities.