Letter

Confidence matching in group decision-making

  • Nature Human Behaviour 1, Article number: 0117 (2017)
  • doi:10.1038/s41562-017-0117
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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 weight 1,2 . Although an individual’s degree of confidence often reflects the probability that their opinion is correct 3,4 , it can also vary with task-irrelevant psychological, social, cultural and demographic factors 5,​6,​7,​8,​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 metric 10,​11,​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 decisions 10,​11,​12 .

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References

  1. 1.

    & Demonstrability and social combination processes on mathematical intellective tasks. J. Exp. Soc. Psychol. 22, 177–189 (1986).

  2. 2.

    & The social influence of confidence in group decision making. J. Exp. Soc. Psychol. 33, 345–366 (1997).

  3. 3.

    , & Signatures of a statistical computation in the human sense of confidence. Neuron 90, 499–506 (2016).

  4. 4.

    , , & Doubly Bayesian analysis of confidence in perceptual decision-making. PLoS Comput. Biol. 11, e1004519 (2015).

  5. 5.

    , , & Individual consistency in the accuracy and distribution of confidence judgments. Cognition 146, 377–386 (2016).

  6. 6.

    & Effects of loss aversion on post-decision wagering: implications for measures of awareness. Conscious. Cogn. 19, 352–363 (2010).

  7. 7.

    , & Overconfidence, risk perception and the risk-taking behavior of finance professionals. Finance Res. Lett. 11, 64–73 (2014).

  8. 8.

    et al. Cross-cultural differences in self-reported decision-making style and confidence. Int. J. Psychol. 33, 325–335 (1998).

  9. 9.

    & Gender and competition. Annu. Rev. Econom. 3, 601–630 (2011).

  10. 10.

    et al. Optimally interacting minds. Science 329, 1081–1085 (2010).

  11. 11.

    et al. Does interaction matter? Testing whether a confidence heuristic can replace interaction in collective decision-making. Conscious. Cogn. 26, 13–23 (2014).

  12. 12.

    When are two heads better than one and why? Science 336, 360–362 (2012).

  13. 13.

    , & The social Bayesian brain: does mentalizing make a difference when we learn? PLoS Comput. Biol. 10, e1003992 (2014).

  14. 14.

    , , & Neural mechanisms of belief inference during cooperative games. J. Neurosci. 30, 10744–10751 (2010).

  15. 15.

    & Toward a mechanistic psychology of dialogue. Behav. Brain Sci. 27, 169–226 (2004).

  16. 16.

    & Active inference, communication and hermeneutics. Cortex 68, 129–143 (2015).

  17. 17.

    & A duet for one. Conscious. Cogn. 36, 390–405 (2015).

  18. 18.

    The Strategy of Conflict (Harvard Univ. Press, 1980).

  19. 19.

    , , & Learning to make collective decisions: the impact of confidence escalation. PLoS One 8, e81195 (2013).

  20. 20.

    Verification of forecasts expressed in terms of probability. Mon. Weather Rev. 78, 1–3 (1950).

  21. 21.

    & Reinforcement Learning: An Introduction (MIT Press, 1998).

  22. 22.

    , & Birds of a feather: homophily in social networks. Annu. Rev. Sociol. 27, 415–444 (2001).

  23. 23.

    , , , & Relating introspective accuracy to individual differences in brain structure. Science 329, 1541–1543 (2010).

  24. 24.

    et al. Equality bias impairs collective decision-making across cultures. Proc. Natl Acad. Sci. USA 112, 3835–3840 (2015).

  25. 25.

    & A signal detection theoretic approach for estimating metacognitive sensitivity from confidence ratings. Conscious. Cogn. 21, 422–430 (2012).

  26. 26.

    , , & Confidence in value-based choice. Nat. Neurosci. 16, 105–110 (2013).

  27. 27.

    & How to measure metacognition. Front. Hum. Neurosci. 8, 443 (2014).

  28. 28.

    Confidence in judgment. Trends Cogn. Sci. 1, 78–82 (1997).

  29. 29.

    et al. Supra-personal cognitive control and metacognition. Trends Cogn. Sci. 18, 186–193 (2014).

  30. 30.

    , , & Negotiation. Annu. Rev. Psychol. 51, 279–314 (2000).

  31. 31.

    , & Responsibility diffusion in cooperative collectives. Personal. Soc. Psychol. Bull. 28, 54–65 (2002).

  32. 32.

    , & Communicating uncertainty in seasonal and interannual climate forecasts in Europe. Philos. Trans. R. Soc. A 373, 20140454 (2015).

  33. 33.

    , & Improving communication of uncertainty in the reports of the intergovernmental panel on climate change. Psychol. Sci. 20, 299–308 (2009).

  34. 34.

    Grading in groups. Econ. Philos. 32, 323–352 (2016).

  35. 35.

    Sherman Kent and the Board of National Estimates: Collected Essays (History Staff, Center for the Study of Intelligence, Central Intelligence Agency, 1994).

  36. 36.

    et al. What failure in collective decision-making tells us about metacognition. Philos. Trans. R. Soc. B 367, 1350–1365 (2012).

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Acknowledgements

This work was supported by the Calleva Research Centre for Evolution and Human Sciences at Magdalen College (D.B. and J.Y.F.L.), the Gatsby Charitable Foundation (L.A. and P.E.L.), the DAAD (A.M.), the Wellcome Trust (S.H.C.: 099741/Z/12/Z), and the European Research Council (B.B.: 309865-NeuroCoDec; C.S.: 281628-URGENCY). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Affiliations

  1. Department of Experimental Psychology, University of Oxford, Oxford OX1 3UD, UK.

    • Dan Bang
    • , Santiago Herce Castanon
    • , Jennifer Y. F. Lau
    •  & Christopher Summerfield
  2. Calleva Research Centre for Evolution and Human Sciences, University of Oxford, Oxford OX1 4AU, UK.

    • Dan Bang
    •  & Jennifer Y. F. Lau
  3. Interacting Minds Centre, Aarhus University, 8000 Aarhus, Denmark.

    • Dan Bang
    •  & Bahador Bahrami
  4. Wellcome Trust Centre for Neuroimaging, University College London, London WC1N 3BG, UK.

    • Dan Bang
    •  & Rani Moran
  5. Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, UK.

    • Laurence Aitchison
  6. Gatsby Computational Neuroscience Unit, University College London, London W1T 4JG, UK.

    • Laurence Aitchison
    •  & Peter E. Latham
  7. Max Planck University College London Centre for Computational Psychiatry and Ageing Research, University College London, London WC1B 5EH, UK.

    • Rani Moran
  8. Department of Computing Science, University of Alberta, Edmonton, Alberta T6G 2E8, Canada.

    • Banafsheh Rafiee
  9. Control and Intelligent Processing Centre of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, 14395-515 Tehran, Iran.

    • Banafsheh Rafiee
    •  & Ali Mahmoodi
  10. Bernstein Center Freiburg, University of Freiburg, 79104 Freiburg, Germany.

    • Ali Mahmoodi
  11. Department of Psychology, King’s College London, London SE5 8AF, UK.

    • Jennifer Y. F. Lau
  12. Institute of Cognitive Neuroscience, University College London, London WC1N 3AR, UK.

    • Bahador Bahrami

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Contributions

D.B., J.Y.F.L., B.B. and C.S. conceived the study and designed the experiments. D.B., S.H.C., B.R. and A.M. performed the experiments. D.B., L.A., R.M., P.E.L. and C.S. developed the models and the simulations. D.B. analysed the data and performed the simulations. D.B., L.A., R.M., S.H.C., P.E.L., B.B. and C.S. interpreted the results. D.B. drafted the manuscript. D.B., L.A., R.M., P.E.L., B.B. and C.S. wrote the manuscript.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Dan Bang.

Supplementary information

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    Supplementary Information

    Supplementary Methods, Supplementary Notes, Supplementary Figures 1–9, Supplementary Tables 1–2.