Expert elicitation and its interface with technology : a review with a view to designing Elicitator
Low Choy, Samantha, James, Allan, & Mengersen, Kerrie (2009) Expert elicitation and its interface with technology : a review with a view to designing Elicitator. In Expert elicitation and its interface with technology: a review with a view to designing Elicitator, Modelling and Simulation Society of Australia and New Zealand Inc., Cairns, Queensland.
Expert knowledge is valuable in many modelling endeavours, particularly where data is not extensive or sufficiently robust. In Bayesian statistics, expert opinion may be formulated as informative priors, to provide an honest reflection of the current state of knowledge, before updating this with new information. Technology is increasingly being exploited to help support the process of eliciting such information. This paper reviews the benefits that have been gained from utilizing technology in this way. These benefits can be structured within a six-step elicitation design framework proposed recently (Low Choy et al., 2009). We assume that the purpose of elicitation is to formulate a Bayesian statistical prior, either to provide a standalone expert-defined model, or for updating new data within a Bayesian analysis. We also assume that the model has been pre-specified before selecting the software. In this case, technology has the most to offer to: targeting what experts know (E2), eliciting and encoding expert opinions (E4), whilst enhancing accuracy (E5), and providing an effective and efficient protocol (E6).
Benefits include: -providing an environment with familiar nuances (to make the expert comfortable) where experts can explore their knowledge from various perspectives (E2); -automating tedious or repetitive tasks, thereby minimizing calculation errors, as well as encouraging interaction between elicitors and experts (E5); -cognitive gains by educating users, enabling instant feedback (E2, E4-E5), and providing alternative methods of communicating assessments and feedback information, since experts think and learn differently; and -ensuring a repeatable and transparent protocol is used (E6).
Impact and interest:
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|Item Type:||Conference Paper|
|Keywords:||Expert elicitation, Bayesian analysis|
|Subjects:||Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400) > Applied Statistics (010401)|
Australian and New Zealand Standard Research Classification > ENVIRONMENTAL SCIENCES (050000) > ECOLOGICAL APPLICATIONS (050100)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > COMPUTER SOFTWARE (080300) > Computer Software not elsewhere classified (080399)
|Divisions:||Current > Schools > School of Curriculum|
Current > QUT Faculties and Divisions > Division of Technology, Information and Learning Support
Current > Research Centres > High Performance Computing and Research Support
Current > Schools > School of Mathematical Sciences
|Deposited On:||23 Nov 2010 11:27|
|Last Modified:||25 Mar 2013 18:11|
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