Quantifying Future Uncertainty
Workshop
Quantifying Future Uncertainty
Instructors: Prof. Bahman Rostami-Tabar, Cardiff University; Harsha Halgamuwe Hewage, Cardiff University
Summary
This workshop focuses on understanding, quantifying, and interpreting uncertainty in forecasts and predictive models. Drawing on statistics and probabilistic forecasting, it explores how uncertainty arises, how it can be formally represented, and how probabilistic forecasts can support better analysis and decision-making.
Through practical examples, hands-on exercises, and discussion, participants will learn to generate, assess, interpret, and use probabilistic forecasts in both research and applied settings.
Instructor Bio
Bahman Rostami-Tabar is Professor of Analytics and Decision Sciences at Cardiff Business School and founder of the Data Lab for Social Good Research Group. His work focuses on how advanced analytical methods can support better choices in complex and uncertain futures, and his contributions have been recognised by the OR Society with the Goodeve Medal and the Lyn Thomas Impact Medal.
Harsha Halgamuwe Hewage is a PhD student at Cardiff University and contributes to the workshop’s practical and applied perspective on forecasting, modelling, and uncertainty.
Learning Outcomes
Explain key sources and types of uncertainty and their implications for modelling.
Apply appropriate methods to quantify and represent future uncertainty.
Evaluate forecast quality using proper statistical metrics and scoring rules.
Critically assess the assumptions and limitations of uncertainty-quantification methods.
Interpret and use probabilistic forecasts to support analysis and decision-making.
Session Structure
30 mins: Sources of uncertainty.
60 mins: Approaches to quantifying uncertainty.
30 mins: Break.
60 mins: Assessing forecast quality and using probabilistic forecasts.
15 mins: Closing thoughts, resources, and questions.
Pre-Reading
Materials
Slides
Hands-on exercises and code examples in R and Python