Naïve Learning in Social Networks and the Wisdom of Crowds

We study learning in a setting where agents receive independent noisy signals about the true value of a variable and then communicate in a network. They naïvely update beliefs by repeatedly taking weighted averages of neighbors' opinions. We show that all opinions in a large society converge to the truth if and only if the influence of the most influential agent vanishes as the society grows. We also identify obstructions to this, including prominent groups, and provide structural conditions on the network ensuring efficient learning. Whether agents converge to the truth is unrelated to how quickly consensus is approached. (JEL D83, D85, Z13)

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The Use of Social Networks to Employ the Wisdom of Crowds for Teaching

TechTrends ◽

10.1007/s11528-016-0020-0 ◽

2016 ◽

Vol 60 (2) ◽

pp. 124-128 ◽

Cited By ~ 2

Author(s):

Aviva Klieger

Keyword(s):

Social Networks ◽

Wisdom Of Crowds ◽

The Wisdom Of Crowds

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Counteracting estimation bias and social influence to improve the wisdom of crowds

10.1101/288191 ◽

2018 ◽

Author(s):

Albert B. Kao ◽

Andrew M. Berdahl ◽

Andrew T. Hartnett ◽

Matthew J. Lutz ◽

Joseph B. Bak-Coleman ◽

...

Keyword(s):

Social Influence ◽

Estimation Error ◽

Social Information ◽

Estimation Bias ◽

General Strategy ◽

Wisdom Of Crowds ◽

Sources Of Error ◽

True Value ◽

Wide Range ◽

The Wisdom Of Crowds

AbstractAggregating multiple non-expert opinions into a collective estimate can improve accuracy across many contexts. However, two sources of error can diminish collective wisdom: individual estimation biases and information sharing between individuals. Here we measure individual biases and social influence rules in multiple experiments involving hundreds of individuals performing a classic numerosity estimation task. We first investigate how existing aggregation methods, such as calculating the arithmetic mean or the median, are influenced by these sources of error. We show that the mean tends to overestimate, and the median underestimate, the true value for a wide range of numerosities. Quantifying estimation bias, and mapping individual bias to collective bias, allows us to develop and validate three new aggregation measures that effectively counter sources of collective estimation error. In addition, we present results from a further experiment that quantifies the social influence rules that individuals employ when incorporating personal estimates with social information. We show that the corrected mean is remarkably robust to social influence, retaining high accuracy in the presence or absence of social influence, across numerosities, and across different methods for averaging social information. Utilizing knowledge of estimation biases and social influence rules may therefore be an inexpensive and general strategy to improve the wisdom of crowds.

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Counteracting estimation bias and social influence to improve the wisdom of crowds

Journal of The Royal Society Interface ◽

10.1098/rsif.2018.0130 ◽

2018 ◽

Vol 15 (141) ◽

pp. 20180130 ◽

Cited By ~ 9

Author(s):

Albert B. Kao ◽

Andrew M. Berdahl ◽

Andrew T. Hartnett ◽

Matthew J. Lutz ◽

Joseph B. Bak-Coleman ◽

...

Keyword(s):

Social Influence ◽

Estimation Error ◽

Social Information ◽

Estimation Bias ◽

General Strategy ◽

Wisdom Of Crowds ◽

Sources Of Error ◽

True Value ◽

Wide Range ◽

The Wisdom Of Crowds

Aggregating multiple non-expert opinions into a collective estimate can improve accuracy across many contexts. However, two sources of error can diminish collective wisdom: individual estimation biases and information sharing between individuals. Here, we measure individual biases and social influence rules in multiple experiments involving hundreds of individuals performing a classic numerosity estimation task. We first investigate how existing aggregation methods, such as calculating the arithmetic mean or the median, are influenced by these sources of error. We show that the mean tends to overestimate, and the median underestimate, the true value for a wide range of numerosities. Quantifying estimation bias, and mapping individual bias to collective bias, allows us to develop and validate three new aggregation measures that effectively counter sources of collective estimation error. In addition, we present results from a further experiment that quantifies the social influence rules that individuals employ when incorporating personal estimates with social information. We show that the corrected mean is remarkably robust to social influence, retaining high accuracy in the presence or absence of social influence, across numerosities and across different methods for averaging social information. Using knowledge of estimation biases and social influence rules may therefore be an inexpensive and general strategy to improve the wisdom of crowds.

Download Full-text