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Expert Status and Performance
Author(s) -
Mark A. Burgman,
Marissa F. McBride,
Raquel Ashton,
Andrew SpeirsBridge,
Louisa Flander,
Bonnie C. Wintle,
Fiona Fidler,
Libby Rumpff,
Charles Twardy
Publication year - 2011
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0022998
Subject(s) - advice (programming) , computer science , data science , expert elicitation , psychology , mathematics , statistics , programming language
Expert judgements are essential when time and resources are stretched or we face novel dilemmas requiring fast solutions. Good advice can save lives and large sums of money. Typically, experts are defined by their qualifications, track record and experience [1] , [2] . The social expectation hypothesis argues that more highly regarded and more experienced experts will give better advice. We asked experts to predict how they will perform, and how their peers will perform, on sets of questions. The results indicate that the way experts regard each other is consistent, but unfortunately, ranks are a poor guide to actual performance. Expert advice will be more accurate if technical decisions routinely use broadly-defined expert groups, structured question protocols and feedback.

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