RSS Merseyside Meeting – Perspectives on Risk

 
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RSS local group meetings

Thursday 14 March 2019, 02:00pm - 04:00pm

 
Location Seminar Room 1, Life Sciences Building (LIFS SR1), University of Liverpool

The meeting will showcase how risk is considered and modelled in different areas of research.  We welcome confirmed speakers Dr Jet Sanders from the London School of Economics, Dr Edoardo Patelli from the Risk Institute, University of Liverpool, and Dr Helen Clough from the Farr Institute, University of Liverpool.  More information and registration link for the event can be found at https://sites.google.com/site/rssmerseyside/research-meetings/perspectives-on-risk.

14.00-14.45 Dr Jet Sanders (London School of Economics): What burst balloons can teach us about real-world risk taking

The weekly cycle is normally seen as the backdrop against which human affairs unfold. Using balloon-based lab experiments, I show that risk tolerance changes over the weekly cycle. Re-analysis of large data sets confirms that real-world decision making also varies through the week in (almost) exactly the same way. This suggests that the weekly backdrop itself shapes our thinking in important ways, with serious implication across health, economic, and political domains. In this lecture, I will discuss what it is about the week that could be causing these effects, what this means for our understanding of risk tolerance and how we can use weekly fluctuations to allocate resources more efficiently, make better predictions and smarter decisions.

14.45-15.00 Tea and Coffee

15.00-15.30 Dr Edoardo Patelli (Risk Institute, University of Liverpool): DO WE HAVE ENOUGH DATA? Robust approaches for dealing with uncertainty

In modern engineering systems, and in particular for critical infrastructures, uncertainty quantification must be performed to assure an adequate level of safety and reliability. Sources of uncertainty can be the inherent variability of physical quantities or events, missing or insufficient data, and lack of knowledge. The former type of uncertainty is known as aleatory uncertainty (Type I or irreducible uncertainty) and representative examples are future weather conditions, stock market prices or chaotic and inherently variable phenomena. Lack of knowledge is generally referred as epistemic uncertainty (Type II or reducible uncertainty) and is considered to be reducible since further data collection (although not always practical or feasible) can decrease the level of uncertainty.

Especially for cases affected by a lack of data, where imprecise information or expert judgement is utilised, and there is a poor understanding of all the relevant underlying process, strong initial assumptions may be needed to use classical probabilistic methods. To meet this need artificial model assumptions are commonly accepted, although hardly justifiable, as they enable the probabilistic assessment to be performed. However, these assumptions can deeply influence the final results and lead to severe risk misjudgement by distorting the information regarding the true level of uncertainty affecting the system safety. Furthermore, this classical implementation does not differentiate between aleatory and epistemic uncertainty. This is a severe limitation as it makes the analyst unable to grasp how much of the uncertainty is due to inherent variability and to what extent the uncertainty is due to poor data quality (therefore suitable to be reduced in principle).

Generalised probabilistic approaches have been introduced to better deal with scarce or limited information by adopting a more robust characterization and propagation of epistemic uncertainties (i.e. with less artificial assumptions for the characterisation of imprecise information). Some example of intensively applied concepts are: Dempster–Shafer theory of Evidence, interval probabilities, Fuzzy-based approaches, info-gap approaches, Bayesian Networks (BNs) and Bayesian approaches. The use of generalized method to quantify uncertainty generally changes the representation of the output of interest from point-valued probabilistic estimators to non-crisp, imprecise (e.g. interval-valued or fuzzy) probabilistic estimators, truthfully representing the available (but incomplete) input data.

15.30-16.00 Dr Helen Clough (University of Liverpool) Title to be confirmed

All are welcome to attend the meeting, however we ask that people register in advance so that we can organise sufficient refreshments.  The registration form can be found at https://docs.google.com/forms/d/e/1FAIpQLSezoDa2Y1mWyf6xLGjyDX1P4qfWOF-PLoNYZ6rn4YvRmnRpjw/viewform?usp=sf_link .

The meeting room can be found in building 215 in gridsquare F8 on campus map https://www.liverpool.ac.uk/files/docs/maps/liverpool-university-campus-map.pdf.

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