How the wisdom of crowds, and of the crowd within, are affected by expertise

AbstractWe investigated the effect of expertise on the wisdom of crowds. Participants completed 60 trials of a numerical estimation task, during which they saw 50–100 asterisks and were asked to estimate how many stars they had just seen. Experiment 1 established that both inner- and outer-crowd wisdom extended to our novel task: Single responses alone were less accurate than responses aggregated across a single participant (showing inner-crowd wisdom) and responses aggregated across different participants were even more accurate (showing outer-crowd wisdom). In Experiment 2, prior to beginning the critical trials, participants did 12 practice trials with feedback, which greatly increased their accuracy. There was a benefit of outer-crowd wisdom relative to a single estimate. There was no inner-crowd wisdom effect, however; with high accuracy came highly restricted variance, and aggregating insufficiently varying responses is not beneficial. Our data suggest that experts give almost the same answer every time they are asked and so they should consult the outer crowd rather than solicit multiple estimates from themselves.

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When social influence promotes the wisdom of crowds

10.31234/osf.io/f5ky3 ◽

2020 ◽

Author(s):

Abdullah Almaatouq ◽

M. Amin Rahimian ◽

Abdulla Alhajri

Keyword(s):

Social Influence ◽

Network Structure ◽

Human Subjects ◽

Wisdom Of Crowds ◽

Social Network Structure ◽

Crowd Wisdom ◽

The Social ◽

Computational Sciences ◽

The Impact ◽

The Wisdom Of Crowds

Whether, and under what conditions, groups exhibit "crowd wisdom" has been a major focus of research across the social and computational sciences. Much of this work has focused on the role of social influence in promoting the wisdom of the crowd versus leading the crowd astray, resulting in conflicting conclusions about how the social network structure determines the impact of social influence. Here, we demonstrate that it is not enough to consider the network structure in isolation. Using theoretical analysis, numerical simulation, and reanalysis of four experimental datasets (totaling 4,002 human subjects), we find that the wisdom of crowds critically depends on the interaction between (i) the centralization of the social influence network and (ii) the distribution of the initial, individual estimates, i.e., the estimation context. Specifically, we propose a feature of the estimation context that measures the suitability of the crowd to benefit from influence centralization and show its significant predictive powers empirically. By adopting a framework that integrates both the structure of the social influence and the estimation context, we bring previously conflicting results under one theoretical framework and clarify the effects of social influence on the wisdom of crowds.

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Under- and Over-Estimation

Experimental Psychology (formerly Zeitschrift für Experimentelle Psychologie) ◽

10.1027/1618-3169/a000064 ◽

2011 ◽

Vol 58 (1) ◽

pp. 39-49 ◽

Cited By ~ 34

Author(s):

Virginie Crollen ◽

Julie Castronovo ◽

Xavier Seron

Keyword(s):

Empirical Data ◽

Accurate Estimation ◽

Numerical Estimation ◽

Production Task ◽

Estimation Task ◽

Reproduction Task ◽

Perception Task ◽

Adult Participants ◽

Mapping Hypothesis ◽

Task Perception

Over the last 30 years, numerical estimation has been largely studied. Recently, Castronovo and Seron (2007) proposed the bi-directional mapping hypothesis in order to account for the finding that dependent on the type of estimation task (perception vs. production of numerosities), reverse patterns of performance are found (i.e., under- and over-estimation, respectively). Here, we further investigated this hypothesis by submitting adult participants to three types of numerical estimation task: (1) a perception task, in which participants had to estimate the numerosity of a non-symbolic collection; (2) a production task, in which participants had to approximately produce the numerosity of a symbolic numerical input; and (3) a reproduction task, in which participants had to reproduce the numerosity of a non-symbolic numerical input. Our results gave further support to the finding that different patterns of performance are found according to the type of estimation task: (1) under-estimation in the perception task; (2) over-estimation in the production task; and (3) accurate estimation in the reproduction task. Moreover, correlation analyses revealed that the more a participant under-estimated in the perception task, the more he/she over-estimated in the production task. We discussed these empirical data by showing how they can be accounted by the bi-directional mapping hypothesis ( Castronovo & Seron, 2007 ).

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