How social influence can undermine the wisdom of crowd effect

Edited by Burton H. Singer, University of Florida, Gainesville, FL, and approved April 13, 2011 (received for review June 23, 2010)
May 16, 2011
108 (22) 9020-9025


Social groups can be remarkably smart and knowledgeable when their averaged judgements are compared with the judgements of individuals. Already Galton [Galton F (1907) Nature 75:7] found evidence that the median estimate of a group can be more accurate than estimates of experts. This wisdom of crowd effect was recently supported by examples from stock markets, political elections, and quiz shows [Surowiecki J (2004) The Wisdom of Crowds]. In contrast, we demonstrate by experimental evidence (N = 144) that even mild social influence can undermine the wisdom of crowd effect in simple estimation tasks. In the experiment, subjects could reconsider their response to factual questions after having received average or full information of the responses of other subjects. We compare subjects’ convergence of estimates and improvements in accuracy over five consecutive estimation periods with a control condition, in which no information about others’ responses was provided. Although groups are initially “wise,” knowledge about estimates of others narrows the diversity of opinions to such an extent that it undermines the wisdom of crowd effect in three different ways. The “social influence effect” diminishes the diversity of the crowd without improvements of its collective error. The “range reduction effect” moves the position of the truth to peripheral regions of the range of estimates so that the crowd becomes less reliable in providing expertise for external observers. The “confidence effect” boosts individuals’ confidence after convergence of their estimates despite lack of improved accuracy. Examples of the revealed mechanism range from misled elites to the recent global financial crisis.

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This paper benefited from comments made by Stefan Herzog, Michael Mäs, Ryan O. Murphy, two anonymous reviewers, and research assistance by Hanna Thorn and Silvana Jud. This study was supported by funding from Eidgenössische Technische Hochschule Zürich. Preliminary versions of the experiment were designed and conducted by J.L. in 2006 (36) and 2005.

Supporting Information

Appendix (PDF)
Supporting Information


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Information & Authors


Published in

Go to Proceedings of the National Academy of Sciences
Proceedings of the National Academy of Sciences
Vol. 108 | No. 22
May 31, 2011
PubMed: 21576485


Submission history

Published online: May 16, 2011
Published in issue: May 31, 2011


  1. collective judgment
  2. estimate aggregation
  3. experimental social science
  4. swarm intelligence
  5. overconfidence


This paper benefited from comments made by Stefan Herzog, Michael Mäs, Ryan O. Murphy, two anonymous reviewers, and research assistance by Hanna Thorn and Silvana Jud. This study was supported by funding from Eidgenössische Technische Hochschule Zürich. Preliminary versions of the experiment were designed and conducted by J.L. in 2006 (36) and 2005.


This article is a PNAS Direct Submission.
*In real-life situations with social influence, there may be additional effects, from which our experiment has abstracted: this includes competition, group pressure, and authority effects. For example, a criminologist could say: “I know the number of victims.” In contrast to such possibilities, our comparably mild and parsimonious kind of information feedback has the advantage that it enables a particularly controlled experimental setting in which there is little ambiguity about which kind of information feedback and social influence played a role.
Note that the framework of the diversity prediction theorem (9) can also be applied to logarithmically transformed data. For the case of logarithmically transformed data, the collective error of the logarithms is the logarithm of the geometric mean and one SD is the logarithm of the geometric SD. Considering the logarithmic nature of our data, one may argue that the geometric mean would have been a better design choice than the arithmetic mean for the information feedback in the aggregated information condition. However, this measure is hard to understand for most subjects because it necessitates confidence with logarithmic transformations. As the simple average (i.e., arithmetic mean) is known from daily life, this information is more meaningful for subjects. Hence, we decided for the arithmetic mean.
The empirical measurement of the social influence effect requires questions with moderate difficulty. In particular, subjects should not have a precise factual knowledge of an issue, because this would prevent adaption and social influence. We can empirically confirm that this was not the case for our questions and subjects: in only 1.5% of all cases, subjects responded at all five times in the most inner payment range of one particular question. This means in absolute values that 13 of 864 consecutive response runs were responded in the full payment range (144 subjects responded to six questions in a run of five consecutive responses). Three of these 13 “high-success runs” were performed by the same person and two from another person. All other high-success runs were performed by different persons.
It deserves to be mentioned that the initial diversity seems to be higher in the no information condition. It could be that subjects anticipate to feel uneasy if their published estimates are too distant from those of others. This could foster that their initial estimates tend to be more “conservative” in the conditions with information feedback. Interestingly, this discrepancy in initial variance is mainly caused by the questions about crime statistics and not about geographical facts.
Note that the collective error slightly declines under social influence, especially in the aggregated information condition, which is partially supported by the significance tests (SI Appendix). This is a result of two empirical facts. First, the distributions of estimates are right-skewed. As a consequence, the arithmetic mean is usually much larger than most estimates and also much larger than the true value. Second, it is an empirical fact for our choice of questions that the geometric mean (which is our aggregation measure to compute the collective error) is always slightly lower than the true value (Table 1). The mechanism of presenting the arithmetic mean in the aggregated condition thus triggers an upward drift toward the true value. This issue is interesting but deserves future studies, as this effect may be different for different sets of questions.



Chair of Systems Design, ETH Zürich, CH-8032 Zurich, Switzerland;
Heiko Rauhut2,1 [email protected]
Chair of Sociology, in particular of Modeling and Simulation, ETH Zürich, CH-8092 Zurich, Switzerland;
Frank Schweitzer
Chair of Systems Design, ETH Zürich, CH-8032 Zurich, Switzerland;
Dirk Helbing
Chair of Sociology, in particular of Modeling and Simulation, ETH Zürich, CH-8092 Zurich, Switzerland;
Santa Fe Institute, Santa Fe, NM 87501; and
Collegium Budapest–Institute for Advanced Study, 1014 Budapest, Hungary


To whom correspondence may be addressed. E-mail: [email protected] or [email protected].
Author contributions: J.L., H.R., F.S., and D.H. designed research; J.L. and H.R. performed research; J.L. and H.R. analyzed data; and J.L. and H.R. wrote the paper.
J.L. and H.R. contributed equally to this work.

Competing Interests

The authors declare no conflict of interest.

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    How social influence can undermine the wisdom of crowd effect
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    • Vol. 108
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