The Winton Programme for the Public Understanding of Risk has been established, to quote its benefactor, to “help refine our thinking and decision-making within the ever-present context of risk and uncertainty”. The fact that it is based in the Statistical Laboratory of Cambridge University shows that the primary concern is to improve understanding of the more quantitative issues that surround risk and uncertainty, and our website will be deliberately aimed at multiple audiences, using a hierarchy of both mathematical and linguistic sophistication, with an emphasis on attractive graphics and animations backed up by (optional) technical explanation. We also have started workshops and presentations for both GCSE and A level students, the general public as well as being involved in seminars for journalists.

Apart from quantitative issues, we also feel that it is important to understand how people learn about risks through the media, and to understand how media risk stories themselves represent the quantitative aspects of the underlying science being reported. We’ve started by looking at two case studies of recent risk stories and how they developed from the quantitative information as presented in the original scientific paper or report, to the national discussion on the issues through the mass media they fostered, to how the story slowly fizzled out, and lastly, what impact that story has had afterwards.
Through these processes a 600 page study on the nutritional causes of cancer is now, only a few months later, chiefly remembered as the “bacon is bad for you”
story.

The case studies show that the quantitative evidence is strongly tied up with qualitative issues: morality; public health benefits vs. private health benefits; the practicality of precautionary principles; trust in experts and many more. Effectively combining the quantitative element of our project with qualitative understanding is one of the particular challenges we have set for ourselves, and we would like to explore further on how this could be achieved.

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