title: What is an expert? A systems perspective on expertise.
abstract: Expert knowledge is a valuable source of information with a wide range of research applications. Despite the recent advances in defining expert knowledge, little attention has been given to how to view expertise as a system of interacting contributory factors for quantifying an individual's expertise. We present a systems approach to expertise that accounts for many contributing factors and their inter-relationships and allows quantification of an individual's expertise. A Bayesian network (BN) was chosen for this purpose. For illustration, we focused on taxonomic expertise. The model structure was developed in consultation with taxonomists. The relative importance of the factors within the network was determined by a second set of taxonomists (supra-experts) who also provided validation of the model structure. Model performance was assessed by applying the model to hypothetical career states of taxonomists designed to incorporate known differences in career states for model testing. The resulting BN model consisted of 18 primary nodes feeding through one to three higher-order nodes before converging on the target node (Taxonomic Expert). There was strong consistency among node weights provided by the supra-experts for some nodes, but not others. The higher-order nodes, "Quality of work" and "Total productivity", had the greatest weights. Sensitivity analysis indicated that although some factors had stronger influence in the outer nodes of the network, there was relatively equal influence of the factors leading directly into the target node. Despite the differences in the node weights provided by our supra-experts, there was good agreement among assessments of our hypothetical experts that accurately reflected differences we had specified. This systems approach provides a way of assessing the overall level of expertise of individuals, accounting for multiple contributory factors, and their interactions. Our approach is adaptable to other situations where it is desirable to understand components of expertise.
authors.affiliation: Australian Institute of Marine Science PMB 3, Townsville, Qld, 4810, Australia.
authors.forename: Michael Julian
authors.initials: MJ
authors.lastname: Caley
authors.affiliation: Australian Institute of Marine Science, UWA Oceans Institute Crawley, WA, 6009, Australia.
authors.forename: Rebecca A
authors.initials: RA
authors.lastname: O'Leary
authors.affiliation: Australian Institute of Marine Science, UWA Oceans Institute Crawley, WA, 6009, Australia.
authors.forename: Rebecca
authors.initials: R
authors.lastname: Fisher
authors.affiliation: School of Mathematical Sciences, Queensland University of Technology GPO Box 2434, Brisbane, Qld, 4001, Australia.
authors.forename: Samantha
authors.initials: S
authors.lastname: Low-Choy
authors.affiliation: School of Mathematical Sciences, Queensland University of Technology GPO Box 2434, Brisbane, Qld, 4001, Australia.
authors.forename: Sandra
authors.initials: S
authors.lastname: Johnson
authors.affiliation: School of Mathematical Sciences, Queensland University of Technology GPO Box 2434, Brisbane, Qld, 4001, Australia.
authors.forename: Kerrie
authors.initials: K
authors.lastname: Mengersen
keywords: Bayesian network; expert judgement; expert knowledge; expert opinion; hierarchy of classes; supra-expert; taxonomy