Two years ago, when I first started learning the academic side of risk analysis at the University of Stavanger, I discovered the gaps between academics and risk management in practice. Most of these gaps can be traced back to the way risk is defined. The risk definition, which involves multiplying probability by consequences (C×P), is common in many industries, although there is, by and large, a consensus among scholars that this is insufficient for defining risk.
One of the ways in which this gap is exemplified is through risk matrices. They are very common in practice and often built on the C×P definition of risk. Yet there is consensus amongst scholars that risk matrices have significant shortcomings. This disconnect between theory and practice became the driving force behind my master’s thesis. As someone who bridges the academic and practice, I felt compelled to address this gap. How could we reconcile the scholarly critique with the popularity of risk matrices in practice?
The answer, I discovered, lies not in abandoning risk matrices altogether, but in understanding the broader family of risk visualisation tools and creating systematic guidance for their use. My research focuses on Probability Consequence Diagrams (PCDs)—a category that encompasses traditional risk matrices and their many adaptations.
Below, you’ll find the complete abstract of my thesis, followed by a link to download the full document.
Abstract
Risk matrices are widely used across industries for visualising risk. Despite their popularity, there is a strong consensus among scholars that traditional risk matrices have significant methodological limitations. This creates a gap between industry practice and scholarly critique. This thesis addresses this gap by investigating:
“What are the different types of Probability Consequence Diagram (PCD) designs, their theoretical foundations, practical applications, and can a structured framework be developed to assist risk assessors in selecting the most appropriate PCD adaptation based on risk characteristics?”
To answer this research question, a scoping review of 1,873 publications was conducted, resulting in twelve distinct, generally applicable PCD adaptations. The review revealed that whilst these adaptations exhibit diverse designs and applications across industries, systematic guidance for their selection remains absent in the literature.
Building upon this scoping review, two frameworks were developed to address this gap. Framework 1 guides the selection from existing PCD adaptations, whilst Framework 2 enables the construction of PCDs through the selection of visual elements. Both frameworks operate based on risk characteristics and the visualisation’s intended purpose.
This research provides the first systematic approach to matching PCD capabilities with risk management requirements, thereby bridging the gap between the theoretical understanding of limitations and the practical application needs. These frameworks form a foundation for more informed PCD selection in practice, whilst acknowledging limitations including reliance on analyst judgement and the need for empirical validation, which represents a critical area for future research.
Why This Research Matters
This thesis doesn’t just critique existing tools—it provides practical solutions. The two frameworks I’ve developed give risk professionals concrete guidance for selecting appropriate visualisation methods based on their specific circumstances. This bridges the academic-practitioner divide and advances the field of risk communication.
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