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Social Influence Undermines the Wisdom of the Crowd in Sequential Decision Making

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  • Vincenz Frey

    (Department of Sociology, University of Groningen, 9712 TG Groningen, Netherlands)

  • Arnout van de Rijt

    (Department of Political and Social Sciences, European University Institute, 50014 Florence, Italy; ; Department of Sociology, Utrecht University, 3584 CH Utrecht, Netherlands)

Abstract

Teams, juries, electorates, and committees must often select from various alternative courses of action what they judge to be the best option. The phenomenon that the central tendency of many independent estimates is often quite accurate—“the wisdom of the crowd”—suggests that group decisions based on plurality voting can be surprisingly wise. Recent experimental studies demonstrate that the wisdom of the crowd is further enhanced if individuals have the opportunity to revise their votes in response to the independent votes of others. We argue that this positive effect of social information turns negative if group members do not first contribute an independent vote but instead cast their votes sequentially such that early mistakes can cascade across strings of decision makers. Results from a laboratory experiment confirm that when subjects sequentially state which of two answers they deem correct, majorities are more often wrong when subjects can see how often the two answers have been chosen by previous subjects than when they cannot. As predicted by our theoretical model, this happens even though subjects’ use of social information improves the accuracy of their individual votes. A second experiment conducted over the internet involving larger groups indicates that although early mistakes on easy tasks are eventually corrected in long enough choice sequences, for difficult tasks wrong majorities perpetuate themselves, showing no tendency to self-correct.

Suggested Citation

  • Vincenz Frey & Arnout van de Rijt, 2021. "Social Influence Undermines the Wisdom of the Crowd in Sequential Decision Making," Management Science, INFORMS, vol. 67(7), pages 4273-4286, July.
  • Handle: RePEc:inm:ormnsc:v:67:y:2021:i:7:p:4273-4286
    DOI: 10.1287/mnsc.2020.3713
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    References listed on IDEAS

    as
    1. Ben Greiner, 2015. "Subject pool recruitment procedures: organizing experiments with ORSEE," Journal of the Economic Science Association, Springer;Economic Science Association, vol. 1(1), pages 114-125, July.
    2. Alpern, Steve & Chen, Bo, 2017. "The importance of voting order for jury decisions by sequential majority voting," European Journal of Operational Research, Elsevier, vol. 258(3), pages 1072-1081.
    3. Richard P. Larrick & Jack B. Soll, 2006. "Erratum--Intuitions About Combining Opinions: Misappreciation of the Averaging Principle," Management Science, INFORMS, vol. 52(2), pages 309-310, February.
    4. Martin Spann & Bernd Skiera, 2003. "Internet-Based Virtual Stock Markets for Business Forecasting," Management Science, INFORMS, vol. 49(10), pages 1310-1326, October.
    5. Arthur, W Brian, 1989. "Competing Technologies, Increasing Returns, and Lock-In by Historical Events," Economic Journal, Royal Economic Society, vol. 99(394), pages 116-131, March.
    6. Hunt Allcott & Matthew Gentzkow, 2017. "Social Media and Fake News in the 2016 Election," NBER Working Papers 23089, National Bureau of Economic Research, Inc.
    7. Nofer, Michael & Hinz, Oliver, 2014. "Are Crowds on the Internet Wiser than Experts? The Case of a Stock Prediction Community," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 69935, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    8. Berno Buechel & Stefan Klößner & Martin Lochmüller & Heiko Rauhut, 2020. "The strength of weak leaders: an experiment on social influence and social learning in teams," Experimental Economics, Springer;Economic Science Association, vol. 23(2), pages 259-293, June.
    9. repec:cup:judgdm:v:8:y:2013:i:2:p:91-105 is not listed on IDEAS
    10. Bikhchandani, Sushil & Hirshleifer, David & Welch, Ivo, 1992. "A Theory of Fads, Fashion, Custom, and Cultural Change in Informational Cascades," Journal of Political Economy, University of Chicago Press, vol. 100(5), pages 992-1026, October.
    11. Gabriel Madirolas & Gonzalo G de Polavieja, 2015. "Improving Collective Estimations Using Resistance to Social Influence," PLOS Computational Biology, Public Library of Science, vol. 11(11), pages 1-16, November.
    12. Robert T. Clemen & Robert L. Winkler, 1985. "Limits for the Precision and Value of Information from Dependent Sources," Operations Research, INFORMS, vol. 33(2), pages 427-442, April.
    13. Urs Fischbacher, 2007. "z-Tree: Zurich toolbox for ready-made economic experiments," Experimental Economics, Springer;Economic Science Association, vol. 10(2), pages 171-178, June.
    14. Albert E. Mannes, 2009. "Are We Wise About the Wisdom of Crowds? The Use of Group Judgments in Belief Revision," Management Science, INFORMS, vol. 55(8), pages 1267-1279, August.
    15. Friedemann Polzin & Helen Toxopeus & Erik Stam, 2018. "The wisdom of the crowd in funding: information heterogeneity and social networks of crowdfunders," Small Business Economics, Springer, vol. 50(2), pages 251-273, February.
    16. Gale, Douglas, 1996. "What have we learned from social learning?," European Economic Review, Elsevier, vol. 40(3-5), pages 617-628, April.
    17. See, Kelly E. & Morrison, Elizabeth W. & Rothman, Naomi B. & Soll, Jack B., 2011. "The detrimental effects of power on confidence, advice taking, and accuracy," Organizational Behavior and Human Decision Processes, Elsevier, vol. 116(2), pages 272-285.
    18. Kevin E. Levay & Jeremy Freese & James N. Druckman, 2016. "The Demographic and Political Composition of Mechanical Turk Samples," SAGE Open, , vol. 6(1), pages 21582440166, March.
    19. Kenneth C. Lichtendahl & Yael Grushka-Cockayne & Phillip E. Pfeifer, 2013. "The Wisdom of Competitive Crowds," Operations Research, INFORMS, vol. 61(6), pages 1383-1398, December.
    20. Christian Ganser & Marc Keuschnigg, 2018. "Social Influence Strengthens Crowd Wisdom Under Voting," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 21(06n07), pages 1-23, September.
    21. Julia A. Minson & Jennifer S. Mueller & Richard P. Larrick, 2018. "The Contingent Wisdom of Dyads: When Discussion Enhances vs. Undermines the Accuracy of Collaborative Judgments," Management Science, INFORMS, vol. 64(9), pages 4177-4192, September.
    22. repec:nas:journl:v:115:y:2018:p:8734-8739 is not listed on IDEAS
    23. Ladha, Krishna K., 1995. "Information pooling through majority-rule voting: Condorcet's jury theorem with correlated votes," Journal of Economic Behavior & Organization, Elsevier, vol. 26(3), pages 353-372, May.
    24. Jacob K. Goeree & Thomas R. Palfrey & Brian W. Rogers & Richard D. McKelvey, 2007. "Self-Correcting Information Cascades," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 74(3), pages 733-762.
    25. Abhijit V. Banerjee, 1992. "A Simple Model of Herd Behavior," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 107(3), pages 797-817.
    26. Bo Cowgill & Eric Zitzewitz, 2015. "Corporate Prediction Markets: Evidence from Google, Ford, and Firm X," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 82(4), pages 1309-1341.
    27. Murr, Andreas E., 2015. "The wisdom of crowds: Applying Condorcet’s jury theorem to forecasting US presidential elections," International Journal of Forecasting, Elsevier, vol. 31(3), pages 916-929.
    28. Eric K. Kelley & Paul C. Tetlock, 2013. "How Wise Are Crowds? Insights from Retail Orders and Stock Returns," Journal of Finance, American Finance Association, vol. 68(3), pages 1229-1265, June.
    29. Hunt Allcott & Matthew Gentzkow, 2017. "Social Media and Fake News in the 2016 Election," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 211-236, Spring.
    30. Richard P. Larrick & Jack B. Soll, 2006. "Intuitions About Combining Opinions: Misappreciation of the Averaging Principle," Management Science, INFORMS, vol. 52(1), pages 111-127, January.
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    1. Tang, Ming & Liao, Huchang, 2024. "Group efficiency and individual fairness tradeoff in making wise decisions," Omega, Elsevier, vol. 124(C).

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