Taming Uncertainty in Mathematical Models Used in the Private and Public Sectors

Background Understanding how uncertainty affects real decision-making is an essential part of developing uncertainty quantification (UQ) as a useful intellectual discipline. On the industrial and commercial side, uncertainty arising from the increasing use of computer modelling affects decisions in areas such as new technology, design, manufacturing, long-term infrastructure investment and insurance. In the public sector, there are very topical issues on health and safety.

Uncertainty quantification is a broad phrase used to describe methodologies for taking account of uncertainties when mathematical and computer models are used to describe real-world phenomena. The scientific challenges of modern life, the recent rapid growth in computing power and the demand for more accurate and precise predictions in areas affecting improved infrastructures, public safety and economic well-being have spawned a recent surge in UQ activity. New UQ methodologies have and are continuing to be developed by statisticians and applied mathematicians independently.

This event is part of the six month Programme at the INI on Uncertainty Quantification for Complex Systems: Theory and Methodologies and concentrated on how to handle uncertainty arising from the use of mathematical models.

Aims and Objectives This knowledge exchange event by the Turing Gateway to Mathematics opened up the discussion to a wide audience, including those working in biotechnology, healthcare, medicine, manufacturing, finance, defence, engineering, security, Government and the public sector.

The introductory talks highlighted the key issues raised from the Programme so far and summarised the outputs from the initial workshop on key Uncertainty Quantification methodologies.

Three end-user sessions included talks from the engineering, financial and healthcare sectors. Speakers described how uncertainty is managed at present in their organisations and the challenges they face. Each session included time for discussion and feedback from the audience.

The main aims of the day were:

Describe how uncertainty is managed at present in a number of organisations. Try to capture what is required from an operationally useful methodology of uncertainty in the future. Explore if we can cross-fertilise, for example, between engineering, finance and medicine. Ask which current methodologies will be most relevant and can we see over the horizon? Discussion was encouraged and the results will feed back into the development of subsequent workshops and also to serve to scope and focus a second satellite meeting that will take place later in the year.