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.
Highlights
The use of expert knowledge is gaining currency in scientific research and decision-making (O’Hagan 1998; Ayyub 2001; O’Hagan et al 2006)
Ecology and Evolution published by John Wiley & Sons Ltd
The resulting Bayesian network (BN) consisted of 18 primary nodes feeding through between one and three higher-order nodes before converging on the target node (Taxonomic Expert)
Summary
The use of expert knowledge is gaining currency in scientific research and decision-making (O’Hagan 1998; Ayyub 2001; O’Hagan et al 2006). Expert knowledge is being increasingly used in a diverse range of disciplines where more traditional types of empirical data are insufficient to address particular issues in a specific context and/or in a timely manner. These discipline areas include landscape ecology (Low Choy et al 2009), conservation and management of threatened and endangered species (Campbell 2002; Smith et al 2007; Murray et al 2009; O’Leary et al 2009; James et al 2010; Johnson et al 2010; Martin et al 2012), environmental risk (Hamilton et al 2007; Hoelzer et al 2012; Johnson et al 2013a,b), meteorology (Risk Management Services 2006), climate change (Risbey 2008), health and medicine (Knol et al 2010; Waterhouse and Johnson 2012), knowledge engineering (Kendal and Creen 2007), information technology systems (Franke et al 2012) and industry (Yu 2002).
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