What is an expert? A systems perspective on expertise.

Area of Science:

• Expertise studies
• Systems science
• Cognitive science

Background:

• Expert knowledge is crucial for research but quantifying it remains challenging.
• Existing methods often overlook the complex interplay of factors contributing to expertise.
• A systems-level understanding is needed to accurately measure an individual's expertise.

Purpose of the Study:

• To develop and validate a systems approach for quantifying individual expertise.
• To model the inter-relationships of contributory factors influencing expertise.
• To apply this approach to assess taxonomic expertise.

Main Methods:

• Utilized a Bayesian network (BN) to model expertise.
• Consulted taxonomists to define model structure and validate its components.
• Assessed model performance using hypothetical career states of taxonomists.

Main Results:

• Developed a BN model with 18 primary nodes converging on 'Taxonomic Expert'.
• Identified 'Quality of work' and 'Total productivity' as key higher-order factors.
• Sensitivity analysis showed relatively equal influence of factors directly impacting the target node.

Conclusions:

• The systems approach effectively quantifies expertise by considering multiple interacting factors.
• The Bayesian network model provides a adaptable framework for assessing expertise in various domains.
• This methodology offers a robust way to evaluate individual expertise levels and their components.