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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

Residuals vs. fitted values for both information conditions and all questions.The first two rows show the no-information condition, while the last two – the aggregate information condition. Questions are numbered from left to right and top to bottom. The mutual information (MI) is shown on top of each plot (see Methods for definition of MI).
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f2: Residuals vs. fitted values for both information conditions and all questions.The first two rows show the no-information condition, while the last two – the aggregate information condition. Questions are numbered from left to right and top to bottom. The mutual information (MI) is shown on top of each plot (see Methods for definition of MI).

Mentions: The model is estimated by the method of Ordinary Least Squares (OLS), which is based to the following assumptions: (a) (linear model is correct), (b) (normality of the error distribution), (c) (homoscedasticity), and (d) (independence of errors). First, to assess the overall feasibility of the linear model, we plot the residuals from the OLS estimation of Eq. 1 versus the fitted values, commonly known as a Tukey-Anscombe plot (Figure 2). A strong trend in the plot is evidence that the linear model is not suitable, consequently (a) is violated.


Quantifying the effects of social influence.

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

Residuals vs. fitted values for both information conditions and all questions.The first two rows show the no-information condition, while the last two – the aggregate information condition. Questions are numbered from left to right and top to bottom. The mutual information (MI) is shown on top of each plot (see Methods for definition of MI).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f2: Residuals vs. fitted values for both information conditions and all questions.The first two rows show the no-information condition, while the last two – the aggregate information condition. Questions are numbered from left to right and top to bottom. The mutual information (MI) is shown on top of each plot (see Methods for definition of MI).
Mentions: The model is estimated by the method of Ordinary Least Squares (OLS), which is based to the following assumptions: (a) (linear model is correct), (b) (normality of the error distribution), (c) (homoscedasticity), and (d) (independence of errors). First, to assess the overall feasibility of the linear model, we plot the residuals from the OLS estimation of Eq. 1 versus the fitted values, commonly known as a Tukey-Anscombe plot (Figure 2). A strong trend in the plot is evidence that the linear model is not suitable, consequently (a) is violated.

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