This module introduces Bayesian methods for integrating prior knowledge with new data to improve statistical inference and decision-making. Learners will explore real-world applications, such as market testing and risk assessment, and address common misconceptions about Bayesian reasoning. The module also examines the philosophical debates surrounding the use of prior probabilities.

This module delves into the complexities of measuring human preferences and attitudes, focusing on subjective valuation, risk tolerance, and the quantification of trade-offs in decision-making. Learners will explore practical tools and frameworks for translating intangible preferences into actionable data, and understand how these measurements inform both personal and business decisions.

This module explores how human judgment serves as a measurement tool, examining its strengths, limitations, and the impact of biases and heuristics. Learners will discover structured models like Rasch and the Lens Model to enhance decision-making accuracy and reliability. Practical examples and comparisons of different evaluation methods are provided to illustrate best practices in measurement.

This module explores innovative measurement tools and prediction markets, emphasizing their impact on decision-making and risk management in organizations. Learners will analyze real-world case studies, including the National Leisure Group and the DARPA 'Terrorism Market' project, to understand both the potential and challenges of these instruments.

This module introduces the principles and real-world applications of Applied Information Economics (AIE) for making data-driven decisions in IT and public sector projects. Learners will explore case studies on quantifying value, risk, and uncertainty, and practice methods for measuring intangible factors such as innovation and flexibility. By the end, participants will be equipped to apply AIE techniques to improve decision-making outcomes.