Decision Making Under Uncertainty
Master the art of leveraging expert knowledge for strategic decision-making, enabling confident choices even when faced with limited or missing data insights.
Master the art of leveraging expert knowledge for strategic decision-making, enabling confident choices even when faced with limited or missing data insights.
Discover the power of Structured Expert Judgment in decision-making under uncertainty. This course introduces you to techniques for quantifying expert opinions, focusing on the Classical Model (CM) developed by Roger Cooke. Learn when and how to apply CM in diverse fields like climate change, disaster management, and public health. Ideal for professionals and researchers dealing with complex problems where traditional data is scarce, this course equips you with tools to make informed decisions in uncertain scenarios.
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What you'll learn
Recognize appropriate settings for using the Classical Model in Structured Expert Judgment
Understand when to incorporate uncertainty assessments in complex decision-making contexts
Learn how to analyze expert data using the Classical Model
Explore the IDEA Protocol as an alternative method for Structured Expert Judgment
Gain insights into the theory behind the Classical Model method
Understand techniques for dependence elicitation and eliciting probabilities
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 6 modules in this course
This course provides a comprehensive introduction to Structured Expert Judgment (SEJ), focusing on the Classical Model (CM) developed by Roger Cooke. Students will learn when and how to use expert opinions for decision-making in situations where traditional data is limited or unavailable. The curriculum covers the theoretical foundations of CM, including statistical accuracy, information scoring, and performance-based weighting. Practical applications in fields such as climate change, disaster management, and public health are explored. The course also addresses practical matters like managing expert biases and conducting effective elicitations. Optional modules on dependence elicitation and probability elicitation are available for verified learners.
Why and when to use SEJ?
Module 1
Statistical accuracy (calibration) and information score
Module 2
Performance-based weights and the Decision Maker
Module 3
Data analysis
Module 4
Applications of CM
Module 5
Practical matters (biases, experts, elicitation)
Module 6
Fee Structure
Instructors
1 Course
Expert in Applied Probability and Structured Expert Judgment
Tina Nane serves as an Assistant Professor of Applied Probability at Delft University of Technology since 2015, where she has established herself as a leading authority in uncertainty quantification and structured expert judgment methods. Her academic credentials include a PhD in Statistics and a cum laude MSc in Risk and Environmental Modelling from TU Delft. Her expertise spans both theoretical and applied aspects of uncertainty analysis, with significant contributions to various fields including food safety, flood risk assessment, and aviation safety. Her early career was marked by collaboration with NASA Langley Research Centre, where she worked on risk assessment of merging and spacing airborne protocols, leading to her conducting uncertainty quantification training workshops for NASA. As the principal lecturer for BSc and MSc courses in decision theory and structured expert judgment, she has developed comprehensive training programs and workshops. Her research applications have made significant impact across diverse areas, including source attribution of food and waterborne illnesses in the U.S., dike failure assessments in The Netherlands, and citation analysis. She has further expanded her influence through the development of online courses in decision making under uncertainty, demonstrating her commitment to knowledge dissemination and practical application of statistical methods.
2 Courses
Leading Expert in Probabilistic Modeling and Structured Expert Judgment
Anca Hanea serves as a Senior Researcher at the Centre of Excellence for Biosecurity Risk Analysis (CEBRA) at the University of Melbourne, where she combines her mathematical expertise with practical applications in risk analysis. After earning her PhD in Applied Probability from TU Delft, she has dedicated a decade to advancing probabilistic modeling and uncertainty quantification. Her significant contributions include developing Non Parametric Bayesian Networks (NPBNs) and playing a crucial role in establishing the COST European network for Structured Expert Judgment (SEJ). She has co-developed the IDEA protocol for SEJ and contributed to its implementation across various international organizations including WHO, EFSA, DARPA, and the European Framework 7 Programme. Her work spans multiple domains, from biosecurity risk analysis to food safety assessment, demonstrating the versatility of her mathematical approaches. As an educator and researcher, she divides her time between teaching, student supervision, and research, while actively participating in expert elicitations worldwide. Her collaboration with colleagues has resulted in numerous guidelines and standards for expert judgment elicitation, particularly for the European Food Safety Authority, making her a pivotal figure in the field of structured expert judgment and probabilistic modeling.
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