ESRA Webinar - Alternative approaches to the treatment of epistemic uncertainties in risk assessment
Webinar held 2 November 2016 'Alternative approaches to the treatment of epistemic uncertainties in risk assessment' by Professor Michael Beer


Download slides here (PDF)


See the recorded video here (Youtube)

About the speaker

Michael Beer is Professor and Head of the Institute for Risk and Reliability, Leibniz Universität Hannover, Germany, since 2015. He is also part time Professor at the Institute for Risk and Uncertainty, University of Liverpool and in the Shanghai Institute of Disaster Prevention and Relief, Tongji University, China. He obtained a doctoral degree from the Technische Universität Dresden and pursued research at Rice University, supported with a Feodor-Lynen Fellowship from the Alexander von Humboldt-Foundation. From 2007 to 2011 Dr. Beer worked as an Assistant Professor at National University of Singapore. In 2011 he joined the University of Liverpool as Chair in Uncertainty in Engineering and Founding Director of the Institute for Risk and Uncertainty. In 2014 he established the EPSRC and ESRC Centre for Doctoral Training in Quantification and Management of Risk & Uncertainty in Complex Systems & Environments. Among other activities Dr. Beer is Editor in Chief (jointly) of the Encyclopedia of Earthquake Engineering (Springer) as well as Associate Editor of the ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems and Associate Editor of the International Journal of Reliability and Safety. Dr. Beer’s research is focused on non-traditional uncertainty models in engineering with emphasis on reliability and risk analysis.  



Epistemic uncertainties appear across all engineering fields to quite some significant extent. Although they can often be described phenomenologically and qualitatively, they counteract a rigorous quantitative description, which is needed as a basis for a realistic risk assessment. In the presence of epistemic uncertainties the specification of a probabilistic model and the associated risk analysis lead to hypothetical results presuming some intuitive guess to capture the influence of the epistemic uncertainty. That is, we quantify risk based on conditions that represent assumptions rather than facts. Such results can be significantly misleading. It is thus of paramount importance to quantify epistemic uncertainties most realistically. This quantification should neither introduce unwarranted information nor should it neglect information. On this basis there is a clear consensus that epistemic uncertainties need to be taken into account for a realistic assessment of risk and reliability. However, there is no clearly defined procedure to master this challenge. There are rather a variety of concepts and approaches available to deal with epistemic uncertainties, from which the engineer can chose. This choice is made difficult by the perception that the available concepts are competing and opposed to one another rather than being complementary and compatible. Clearly, the first consideration should be devoted to a probabilistic modelling, naturally through subjective probabilities, which express a belief of the expert and can be integrated into a fully probabilistic framework in a coherent manner via a Bayesian approach. While this pathway is widely accepted and recognized as being very powerful, the potential of set-theoretical approaches and imprecise probabilities has only been utilized to some minor extent. Those approaches, however, attract increasing attention in cases when available information is not rich enough to meaningfully specify subjective probability distributions. In this talk, we discuss the phenomena