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Quantifying the effects of social influence.

Mavrodiev P, Tessone CJ, Schweitzer F - Sci Rep (2013)

Bottom Line: It holds across all questions analysed, even though the correct answers differ by several orders of magnitude.We argue that the nature of the response crucially changes with the level of information aggregation.This insight contributes to the empirical foundation of models for collective decisions under social influence.

View Article: PubMed Central - PubMed

Affiliation: Chair of Systems Design, ETH Zurich, Weinbergstrasse 58, 8092 Zurich, Switzerland.

ABSTRACT
How do humans respond to indirect social influence when making decisions? We analysed an experiment where subjects had to guess the answer to factual questions, having only aggregated information about the answers of others. While the response of humans to aggregated information is a widely observed phenomenon, it has not been investigated quantitatively, in a controlled setting. We found that the adjustment of individual guesses depends linearly on the distance to the mean of all guesses. This is a remarkable, and yet surprisingly simple regularity. It holds across all questions analysed, even though the correct answers differ by several orders of magnitude. Our finding supports the assumption that individual diversity does not affect the response to indirect social influence. We argue that the nature of the response crucially changes with the level of information aggregation. This insight contributes to the empirical foundation of models for collective decisions under social influence.

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Related in: MedlinePlus

QQ Plots.Theoretical quantiles of a normal distribution versus sample quantiles for all six questions. There are outliers in the data resulting in non-normal residuals. Question numbers (Q) are indicated on the top left corner of each plot.
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f3: QQ Plots.Theoretical quantiles of a normal distribution versus sample quantiles for all six questions. There are outliers in the data resulting in non-normal residuals. Question numbers (Q) are indicated on the top left corner of each plot.

Mentions: Second, in Figure 3 we check normality of errors by plotting the quantiles of the residual distribution against the quantiles of a normal distribution. The off-diagonal points in all questions clearly indicate the presence of a few large outliers, as expected for skewed data. Nonnormality of residuals plays no role for the BLUE (best linear unbiased estimator) properties of OLS estimators, provided (a) and (c) hold (the homoscedasticity assumption is evaluated below). However, exact t and F statistics will be incorrect. Therefore, we make use of the relatively large sample size in all questions to justify the asymptotic normality property of the OLS estimators23. It can be shown that by employing the central limit theorem and conditional on (a) and (c), OLS produces estimators that are approximately normal24, hence t-test can be carried out in the same way.


Quantifying the effects of social influence.

Mavrodiev P, Tessone CJ, Schweitzer F - Sci Rep (2013)

QQ Plots.Theoretical quantiles of a normal distribution versus sample quantiles for all six questions. There are outliers in the data resulting in non-normal residuals. Question numbers (Q) are indicated on the top left corner of each plot.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3584930&req=5

f3: QQ Plots.Theoretical quantiles of a normal distribution versus sample quantiles for all six questions. There are outliers in the data resulting in non-normal residuals. Question numbers (Q) are indicated on the top left corner of each plot.
Mentions: Second, in Figure 3 we check normality of errors by plotting the quantiles of the residual distribution against the quantiles of a normal distribution. The off-diagonal points in all questions clearly indicate the presence of a few large outliers, as expected for skewed data. Nonnormality of residuals plays no role for the BLUE (best linear unbiased estimator) properties of OLS estimators, provided (a) and (c) hold (the homoscedasticity assumption is evaluated below). However, exact t and F statistics will be incorrect. Therefore, we make use of the relatively large sample size in all questions to justify the asymptotic normality property of the OLS estimators23. It can be shown that by employing the central limit theorem and conditional on (a) and (c), OLS produces estimators that are approximately normal24, hence t-test can be carried out in the same way.

Bottom Line: It holds across all questions analysed, even though the correct answers differ by several orders of magnitude.We argue that the nature of the response crucially changes with the level of information aggregation.This insight contributes to the empirical foundation of models for collective decisions under social influence.

View Article: PubMed Central - PubMed

Affiliation: Chair of Systems Design, ETH Zurich, Weinbergstrasse 58, 8092 Zurich, Switzerland.

ABSTRACT
How do humans respond to indirect social influence when making decisions? We analysed an experiment where subjects had to guess the answer to factual questions, having only aggregated information about the answers of others. While the response of humans to aggregated information is a widely observed phenomenon, it has not been investigated quantitatively, in a controlled setting. We found that the adjustment of individual guesses depends linearly on the distance to the mean of all guesses. This is a remarkable, and yet surprisingly simple regularity. It holds across all questions analysed, even though the correct answers differ by several orders of magnitude. Our finding supports the assumption that individual diversity does not affect the response to indirect social influence. We argue that the nature of the response crucially changes with the level of information aggregation. This insight contributes to the empirical foundation of models for collective decisions under social influence.

Show MeSH
Related in: MedlinePlus