In the early 1970s Tversky and Kahneman published a series of papers on 'heuristics and biases' describing human inadequacies in assessing probabilities, culminating in a highly popular article in Science. This seminal research has been heavily cited in many fields, including statistics, as the definitive research on probability assessment. Curiously, although this work was debated at the time and more recent work has largely refuted many of the claims, this apparent heuristics and biases bias in elicitation research has gone unremarked. Over a decade of research into the frequency effect, the importance of framing, and cognitive models more generally, has been almost completely ignored by the statistical literature on expert elicitation. To remedy this situation, this review offers a guide to the psychological research on assessing probabilities, both old and new, and gives concrete guidelines for eliciting expert knowledge.
SUMMARYNumerous expert elicitation methods have been suggested for generalised linear models (GLMs). This paper compares three relatively new approaches to eliciting expert knowledge in a form suitable for Bayesian logistic regression. These methods were trialled on two experts in order to model the habitat suitability of the threatened Australian brush-tailed rock-wallaby (Petrogale penicillata). The first elicitation approach is a geographically assisted indirect predictive method with a geographic information system (GIS) interface. The second approach is a predictive indirect method which uses an interactive graphical tool. The third method uses a questionnaire to elicit expert knowledge directly about the impact of a habitat variable on the response. Two variables (slope and aspect) are used to examine prior and posterior distributions of the three methods. The results indicate that there are some similarities and dissimilarities between the expert informed priors of the two experts formulated from the different approaches. The choice of elicitation method depends on the statistical knowledge of the expert, their mapping skills, time constraints, accessibility to experts and funding available. This trial reveals that expert knowledge can be important when modelling rare event data, such as threatened species, because experts can provide additional information that may not be represented in the dataset. However care must be taken with the way in which this information is elicited and formulated.
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