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Confidence matching in group decision-making

Author

Listed:
  • Dan Bang

    (University of Oxford
    Calleva Research Centre for Evolution and Human Sciences, University of Oxford
    Interacting Minds Centre, Aarhus University
    Wellcome Trust Centre for Neuroimaging, University College London)

  • Laurence Aitchison

    (University of Cambridge
    Gatsby Computational Neuroscience Unit, University College London)

  • Rani Moran

    (Wellcome Trust Centre for Neuroimaging, University College London
    Max Planck University College London Centre for Computational Psychiatry and Ageing Research, University College London)

  • Santiago Herce Castanon

    (University of Oxford)

  • Banafsheh Rafiee

    (University of Alberta
    Control and Intelligent Processing Centre of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran)

  • Ali Mahmoodi

    (Control and Intelligent Processing Centre of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran
    Bernstein Center Freiburg, University of Freiburg)

  • Jennifer Y. F. Lau

    (University of Oxford
    Calleva Research Centre for Evolution and Human Sciences, University of Oxford
    King’s College London)

  • Peter E. Latham

    (Gatsby Computational Neuroscience Unit, University College London)

  • Bahador Bahrami

    (Interacting Minds Centre, Aarhus University
    Institute of Cognitive Neuroscience, University College London)

  • Christopher Summerfield

    (University of Oxford)

Abstract

Most important decisions in our society are made by groups, from cabinets and commissions to boards and juries. When disagreement arises, opinions expressed with higher confidence tend to carry more weight1,2. Although an individual’s degree of confidence often reflects the probability that their opinion is correct3,4, it can also vary with task-irrelevant psychological, social, cultural and demographic factors5–9. Therefore, to combine their opinions optimally, group members must adapt to each other’s individual biases and express their confidence according to a common metric10–12. However, solving this communication problem is computationally difficult. Here we show that pairs of individuals making group decisions meet this challenge by using a heuristic strategy that we call ‘confidence matching’: they match their communicated confidence so that certainty and uncertainty is stated in approximately equal measure by each party. Combining the behavioural data with computational modelling, we show that this strategy is effective when group members have similar levels of expertise, and that it is robust when group members have no insight into their relative levels of expertise. Confidence matching is, however, sub-optimal and can cause miscommunication about who is more likely to be correct. This herding behaviour is one reason why groups can fail to make good decisions10–12.

Suggested Citation

  • Dan Bang & Laurence Aitchison & Rani Moran & Santiago Herce Castanon & Banafsheh Rafiee & Ali Mahmoodi & Jennifer Y. F. Lau & Peter E. Latham & Bahador Bahrami & Christopher Summerfield, 2017. "Confidence matching in group decision-making," Nature Human Behaviour, Nature, vol. 1(6), pages 1-7, June.
    • Handle: RePEc:nat:nathum:v:1:y:2017:i:6:d:10.1038_s41562-017-0117
    DOI: 10.1038/s41562-017-0117
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    Cited by:

    1. Lejarraga, Tomás & Lejarraga, José, 2020. "Confidence and the description–experience distinction," Organizational Behavior and Human Decision Processes, Elsevier, vol. 161(C), pages 201-212.
    2. Quentin Cavalan & Vincent de Gardelle & Jean-Christophe Vergnaud, 2020. "Overestimate yourself or underestimate others? Two sources of bias in bargaining with joint production," Post-Print halshs-02492289, HAL.
    3. Florent Meyniel, 2020. "Brain dynamics for confidence-weighted learning," PLOS Computational Biology, Public Library of Science, vol. 16(6), pages 1-27, June.
    4. Juliet Dunstone & Christine A. Caldwell, 2018. "Cumulative culture and explicit metacognition: a review of theories, evidence and key predictions," Palgrave Communications, Palgrave Macmillan, vol. 4(1), pages 1-11, December.
    5. Basil Wahn & Artur Czeszumski & Peter König, 2018. "Performance similarities predict collective benefits in dyadic and triadic joint visual search," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-14, January.

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