How Social Influence Can Undermine The Wisdom Of Crowd Effect

social influence

Based on the wisdom of crowd effect, groups can be remarkably accurate in estimating vaguely known facts. From the perspective of decision-makers, it would be valuable to request multiple independent opinions and aggregate these as the basis of their judgements. Real-life examples are predictions of economic growth rates, market potentials, the increase of the world temperature, tax estimations, the assessment of the impact of new technologies, or estimating the amount of finite natural resources.

However, it is hardly feasible to receive independent opinions in society, because people are embedded in social networks and typically influence each other to a certain extent. It is remarkable how little social influence is required to produce herding behavior and negative side effects for the mechanism underlying the wisdom of crowds. In our experiment, we provided just the bare information of the estimates of others (in a similar way as the previous stock price is known to traders trying to make money with their estimates of the fundamental value of a stock). We did not allow for group leader effects, persuasion, or any other kind of social psychological influence. We just provided noncompetitive monetary incentives for the estimation of correct values. These incentives were designed such that the information of others could just be used to update the own knowledge. There was no premium to coordinate with others’ opinions.

Our experimental results show that social influence triggers the convergence of individual estimates and substantially reduces the diversity of the group without improving its accuracy. The remaining diversity is often so small that the correct value shifts from the center to outer regions of the range of estimates. Thus, when taking committee decisions or following the advise of an expert group that was exposed to social influence, their opinions may result in a set of predictions that does not even enclose the correct value anymore. From the perspective of decision-makers, such advice may be thoroughly misleading, because closely related, seemingly independent advice may pretend certainty despite substantial deviations from the correct solution.

Psychologically, however, the convergence of estimates significantly boosts individuals’ confidence. This confidence gain happens despite a lack of improvements, giving evidence for a psychological trap whereby individuals are led into the false belief of collective accuracy as a result of their convergence. Nevertheless, the statistical effects of undermining are less severe for easier questions and if individuals are more confident in their answers (SI Appendix). This gives weight to the conclusion that the negative effects of social influence occur especially in a certain range of question difficulty and individuals’ confidence, a conjecture that should be explored in follow-up studies. Our results underpin the value of collecting individuals’ estimates in the absence of social influence. However, in democratic societies, it is difficult to accomplish such a collection of independent estimates, because the loss of diversity in estimates appears to be a necessary byproduct of transparent decision-making processes. For example, opinion polls and the mass media largely promote information feedback and therefore trigger convergence of how we judge the facts. The wisdom of crowd effect is valuable for society, but using it multiple times creates collective overconfidence in possibly false beliefs.

Presumably, herding is even more pronounced for opinions or attitudes for which no predefined correct answers exist. For example, prospective research may investigate herding and consensus formation on predictions of climate change or election outcomes. However, long-term predictions may have short-term consequences on the system itself: pessimistic predictions for climate change may entail international political consequences, or election polls may change the popularity of parties that have been exposed as those with the least support. These feedback loops hinder the disentanglement of herding behavior from the wisdom of crowds.


Still Curious? Read The Wisdom of Crowds and The Expert Squeeze next.

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