Decision Making Under Uncertainty

This module introduces the concept of Structured Expert Judgment (SEJ) and explores scenarios where it is particularly useful. Students will learn to identify situations where expert opinions are crucial for decision-making in the absence of sufficient data.

In this module, students will learn about the key components of evaluating expert performance in SEJ. The focus will be on understanding statistical accuracy through calibration and the concept of information score, which are crucial for assessing the reliability of expert judgments.

This module explores methods for combining expert opinions, with a focus on performance-based weighting. Students will learn about the concept of the Decision Maker in the context of SEJ and how to aggregate expert judgments effectively.

In this module, students will delve into the techniques for analyzing data obtained from expert judgments. They will learn how to interpret and utilize the collected information to support decision-making processes under uncertainty.

This module showcases practical applications of the Classical Model (CM) in various fields. Students will explore case studies demonstrating how SEJ has been applied to solve complex problems in areas such as climate change, disaster management, and public health.

The final module focuses on practical aspects of conducting SEJ. Students will learn about potential biases in expert judgments, techniques for selecting appropriate experts, and best practices for eliciting expert opinions effectively.