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Expert Status and Performance

Author

Listed:
  • Mark A Burgman
  • Marissa McBride
  • Raquel Ashton
  • Andrew Speirs-Bridge
  • Louisa Flander
  • Bonnie Wintle
  • Fiona Fidler
  • Libby Rumpff
  • Charles Twardy

Abstract

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.

Suggested Citation

  • Mark A Burgman & Marissa McBride & Raquel Ashton & Andrew Speirs-Bridge & Louisa Flander & Bonnie Wintle & Fiona Fidler & Libby Rumpff & Charles Twardy, 2011. "Expert Status and Performance," PLOS ONE, Public Library of Science, vol. 6(7), pages 1-7, July.
    • Handle: RePEc:plo:pone00:0022998
    DOI: 10.1371/journal.pone.0022998
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    References listed on IDEAS

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    1. Willy Aspinall, 2010. "A route to more tractable expert advice," Nature, Nature, vol. 463(7279), pages 294-295, January.
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    Cited by:

    1. Bolger, Fergus & Wright, George, 2017. "Use of expert knowledge to anticipate the future: Issues, analysis and directions," International Journal of Forecasting, Elsevier, vol. 33(1), pages 230-243.
    2. Miles Parker & Andrew Acland & Harry J Armstrong & Jim R Bellingham & Jessica Bland & Helen C Bodmer & Simon Burall & Sarah Castell & Jason Chilvers & David D Cleevely & David Cope & Lucia Costanzo & , 2014. "Identifying the Science and Technology Dimensions of Emerging Public Policy Issues through Horizon Scanning," PLOS ONE, Public Library of Science, vol. 9(5), pages 1-17, May.
    3. Yang Song & Yawen Wang & Dahai Jin, 2020. "A Bayesian Approach Based on Bayes Minimum Risk Decision for Reliability Assessment of Web Service Composition," Future Internet, MDPI, vol. 12(12), pages 1-20, December.
    4. Fergus Bolger & Gene Rowe, 2015. "The Aggregation of Expert Judgment: Do Good Things Come to Those Who Weight?," Risk Analysis, John Wiley & Sons, vol. 35(1), pages 5-11, January.
    5. Bolger, Fergus & Rowe, Gene & Belton, Ian & Crawford, Megan M & Hamlin, Iain & Sissons, Aileen & Taylor Browne Lūka, Courtney & Vasilichi, Alexandrina & Wright, George, 2020. "The Simulated Group Response Paradigm: A new approach to the study of opinion change in Delphi and other structured-group techniques," OSF Preprints 4ufzg, Center for Open Science.
    6. Christopher W. Karvetski & Kenneth C. Olson & David R. Mandel & Charles R. Twardy, 2013. "Probabilistic Coherence Weighting for Optimizing Expert Forecasts," Decision Analysis, INFORMS, vol. 10(4), pages 305-326, December.
    7. Mauksch, Stefanie & von der Gracht, Heiko A. & Gordon, Theodore J., 2020. "Who is an expert for foresight? A review of identification methods," Technological Forecasting and Social Change, Elsevier, vol. 154(C).
    8. Jean Hugé & Nibedita Mukherjee & Camille Fertel & Jean-Philippe Waaub & Thomas Block & Tom Waas & Nico Koedam & Farid Dahdouh-Guebas, 2015. "Conceptualizing the Effectiveness of Sustainability Assessment in Development Cooperation," Sustainability, MDPI, vol. 7(5), pages 1-17, May.
    9. McKellar, Jennifer M. & Sleep, Sylvia & Bergerson, Joule A. & MacLean, Heather L., 2017. "Expectations and drivers of future greenhouse gas emissions from Canada's oil sands: An expert elicitation," Energy Policy, Elsevier, vol. 100(C), pages 162-169.
    10. Thomas Allen & Paolo Prosperi & Bruce Cogill & Martine Padilla & Iuri Peri, 2019. "A Delphi Approach to Develop Sustainable Food System Metrics," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 141(3), pages 1307-1339, February.
    11. Xu, Yingchun & Yao, Wen & Zheng, Xiaohu & Chen, Xiaoqian, 2020. "An iterative information integration method for multi-level system reliability analysis based on Bayesian Melding Method," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    12. A. Charisse Farr & Kerrie Mengersen & Fabrizio Ruggeri & Daniel Simpson & Paul Wu & Prasad Yarlagadda, 2020. "Combining Opinions for Use in Bayesian Networks: A Measurement Error Approach," International Statistical Review, International Statistical Institute, vol. 88(2), pages 335-353, August.
    13. repec:cup:judgdm:v:15:y:2020:i:5:p:783-797 is not listed on IDEAS
    14. Lyon, Aidan & Wintle, Bonnie C. & Burgman, Mark, 2015. "Collective wisdom: Methods of confidence interval aggregation," Journal of Business Research, Elsevier, vol. 68(8), pages 1759-1767.
    15. Kylie Davies & Max K. Bulsara & Anne‐Sylvie Ramelet & Leanne Monterosso, 2018. "Reliability and criterion‐related validity testing (construct) of the Endotracheal Suction Assessment Tool (ESAT©)," Journal of Clinical Nursing, John Wiley & Sons, vol. 27(9-10), pages 1891-1900, May.
    16. Warwick Smith & Anca M. Hanea & Mark A. Burgman, 2022. "Can Groups Improve Expert Economic and Financial Forecasts?," Forecasting, MDPI, vol. 4(3), pages 1-18, August.
    17. Anca M. Hanea & Marissa F. McBride & Mark A. Burgman & Bonnie C. Wintle, 2018. "The Value of Performance Weights and Discussion in Aggregated Expert Judgments," Risk Analysis, John Wiley & Sons, vol. 38(9), pages 1781-1794, September.
    18. Woike, Jan K. & Hoffrage, Ulrich & Petty, Jeffrey S., 2015. "Picking profitable investments: The success of equal weighting in simulated venture capitalist decision making," Journal of Business Research, Elsevier, vol. 68(8), pages 1705-1716.
    19. Abigail R Colson & Roger M Cooke, 2018. "Expert Elicitation: Using the Classical Model to Validate Experts’ Judgments," Review of Environmental Economics and Policy, Association of Environmental and Resource Economists, vol. 12(1), pages 113-132.
    20. David R. Mandel & Robert N. Collins & Evan F. Risko & Jonathan A. Fugelsang, 2020. "Effect of confidence interval construction on judgment accuracy," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 15(5), pages 783-797, September.
    21. Galparsoro, I. & Korta, M. & Subirana, I. & Borja, Á. & Menchaca, I. & Solaun, O. & Muxika, I. & Iglesias, G. & Bald, J., 2021. "A new framework and tool for ecological risk assessment of wave energy converters projects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    22. Anna Chrysafi & Vili Virkki & Mika Jalava & Vilma Sandström & Johannes Piipponen & Miina Porkka & Steven J. Lade & Kelsey Mere & Lan Wang-Erlandsson & Laura Scherer & Lauren S. Andersen & Elena Bennet, 2022. "Quantifying Earth system interactions for sustainable food production via expert elicitation," Nature Sustainability, Nature, vol. 5(10), pages 830-842, October.
    23. James L Anderson & Christopher M Anderson & Jingjie Chu & Jennifer Meredith & Frank Asche & Gil Sylvia & Martin D Smith & Dessy Anggraeni & Robert Arthur & Atle Guttormsen & Jessica K McCluney & Tim W, 2015. "The Fishery Performance Indicators: A Management Tool for Triple Bottom Line Outcomes," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-20, May.

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