CEEM593 - Accounting for Subjectivity and Valuing Information

Course Description

When you conduct your quantitative analysis, the fact that it is quantitative may seem to imply that the data is objectively set in a definitive manner and not suited for subjective analysis. In reality, quantifiable metrics include subjectivity at several points, including how the problem is approached and how you elicit the observations of knowledgeable individuals. It may surprise you, but you will often encounter subjectivity while you're trying to frame a decision process that is objective. Many times you'll discover that the questions that you're asking, the types of probabilities you need, and other information won't involve perfectly clear observations. Making a decision, therefore, isn't always as simple as picking the "best" quantitative option.

When you consider potential solutions, how does the elicitation of subjective data come into play? In this course, you will analyze the processes and theories you must consider as you begin to explore the subjectivity of objectivity in decision making and how they are related. You can potentially elicit subjective expertise to get a sense of the demand for a certain approach. You can also think about the overall riskiness of your potential choice as you deliberate over options. And there are biases and heuristics that may affect your decision. This all points to the importance of understanding the value of information and how it might influence your decisions.

Evaluating how the subjectivity of objectivity can affect how you view options will provide added context and tools for decision making. These elements will help guide you as you further consider how to frame your choices.

Faculty Author

Benefits to the Learner

• Identify where subjectivity may affect quantitative decision analysis and explore how to turn the subjective into objective analysis
• Elicit subjective probabilities from expert analysis to move toward an objective decision
• Assess existing guidelines and challenges when eliciting subjective probabilities
• Examine the value of perfect information versus imperfect information while conducting a perfect-information analysis to quantify the value of additional information then applying these additional considerations to a decision tree framework

Target Audience

• Engineers in any field, including mechanical, electrical, civil, and environmental
• Aspiring data scientists, data analysts, and market analysts
• Anyone who needs to make highly consequential decisions or inform solutions in the face of uncertainty or multiple and conflicting objectives

Applies Towards the Following Certificates

• Decision Analysis : Core Courses