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Geological Society, London, Special Publications; 2004; v. 239; p. 15-27;
DOI: 10.1144/GSL.SP.2004.239.01.02
© 2004 Geological Society of London

Introduction and Reviews

An introduction to prior information derived from probabilistic judgements: elicitation of knowledge, cognitive bias and herding

Michelle C. Baddeley1, Andrew Curtis2,3 & Rachel Wood2,4

1 Faculty of Economics and Politics, Gonville & Caius College, Cambridge CB2 1TA, UK mb150{at}econ.cam.ac.uk
2 Schlumberger Cambridge Research, High Cross, Madingley Road, Cambridge CB3 0EL, UK
3 Grant Institute of Earth Science, School of GeoSciences, University of Edinburgh, West Mains Road, Edinburgh EH9 3JW, UK
4 Department of Earth Sciences, Cambridge University, Downing Street, Cambridge CB2 3EQ, UK

Opinion of geological experts is often formed despite a paucity of data and is usually based on prior experience. In such situations humans employ heuristics (rules of thumb) to aid analysis and interpretation of data. As a result, future judgements are bootstrapped from, and hence biased by, both the heuristics employed and prior opinion.

This paper reviews the causes of bias and error inherent in prior information derived from the probabilistic judgements of people. Parallels are developed between the evolution of scientific opinion on one hand and the limits on rational behaviour on the other. We show that the combination of data paucity and commonly employed heuristics can lead to herding behaviour within groups of experts. Elicitation theory mitigates the effects of such behaviour, but a method to estimate reliable uncertainties on expert judgements remains elusive.

We have also identified several key directions in which future research is likely to lead to methods that reduce such emergent group behaviour, thereby increasing the probability that the stock of common knowledge will converge in a stable manner towards facts about the Earth as it really is. These include: (1) measuring the frequency with which different heuristics tend to be employed by experts within the geosciences; (2) developing geoscience-specific methods to reduce biases originating from the use of such heuristics; (3) creating methods to detect scientific herding behaviour; and (4) researching how best to reconcile opinions from multiple experts in order to obtain the best probabilistic description of an unknown, objective reality (in cases where one exists).





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