Abstract

Abstract:

The incorporation of uncertainty in assessments and
predictions from mathematical models is critical, especially if the models are
to be used to support real-world decisions. In fast moving situations such as a
global pandemic of an infectious disease, then data to parameterise models are
typically patchy and incomplete, and sufficient suitable data may not exist for
many parameters in model runs of possible future scenarios. In such cases
expert judgements can play an important role, both to specify uncertainty
distributions for parameters with no available data and to supplement data
where they are available (via Bayes Theorem, or more informally). In this talk
I will discuss the elicitation of uncertainty distributions for individual
unknowns from a single expert, the combination of the opinions of multiple
experts on an unknown into a single uncertainty distribution and the
elicitation of graphical models, with an emphasis on Bayesian networks, to
produce suitable model structures from experts over multiple dependent
unknowns. I will emphasise a behavioural aggregation approach, the SHeffield
Elicitation Framework (SHELF), for the combination of the opinions of multiple
experts, which will complement the talk from Prof Aspinall on a mathematical
aggregation approach, the Classical Method. A running example on the
development of a diagnostic test will be used to illustrate the ideas, and I
will try to bring out particular issues surrounding infectious disease
modelling.



Resources:

·A probabilistic judgements e-learning course, aimed at
explaining elicitation generally and the Sheffield ELicitation Framework
specifically:http://www.tonyohagan.co.uk/shelf/ecourse.htm

·A textbook providing comprehensive coverage of
elicitation: O'Hagan et al (2006). Uncertain Judgements: Eliciting Experts'
Probabilities, Wiley.

·Resources to conduct an elicitation using SHELF
including slide sets, advice and document templates:http://www.tonyohagan.co.uk/shelf/

·A series of short videos for an online course on
Structured Expert Judgment provided by TU Delft (links are near the top of the
page):http://rogermcooke.net/

·A textbook discussing the elicitation of probabilistic
models: J. Q. Smith (2010). Bayesian decision analysis: principles and
practice. Cambridge University Press.