Quantifying expert opinion in the UK water industry: an experimental study
By Paul H. Garthwaite, Anthony OʼHagan. 2000
64 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/4805baf58351f841fd6900a051de29281eedbe78
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- T. Stewart
- Ian N. Durbach
- M. Kynn
- Joseph B. Kadane
- Paul H. Garthwaite
- Anthony OʼHagan
- Karen E. Willcox
- Loic Le Gratiet
In Management Science and Operations Research
- Ian N. Durbach
- T. Stewart
- M. Kynn
- Anthony OʼHagan
- Joseph B. Kadane
- Luigi Cavaleri
- Tilmann Gneiting
- Loic Le Gratiet
Citations in the corpus
- The ‘heuristics and biases’ bias in expert elicitation (2007, background): “…1996; Coles and Tawn, 1996; Al-Awadhi and Garthwaite, 1998; Craig et al., 1998; Kadane and Wolfson, 1998; Moreno and Girn, 1998; O’Hagan, 1998; Garthwaite and O’Hagan, 2000; Al-Awadhi and Garthwaite, 2001) which discuss prior or expert elicitation, published in statistical journals in the…”
- Statistical Methods for Eliciting Probability Distributions (2005, background): “Murphy and Winkler, 1974; Peterson et al, 1972), other empirical work has found that overconfidence is less if the 33 and 67 percentiles are assessed (Barclay and Peterson, 1973; Garthwaite and O’Hagan, 2000).”
- Modeling uncertainty in multi-criteria decision analysis (2012, methodology): “Bisection methods can be used to obtain medians and upper and lower quartiles, with good results reported in [104]; other studies find that overconfidence is reduced if the 33% and 67% quantiles are used [58].”