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Quantifying the effects of social influence

Author

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  • Pavlin Mavrodiev
  • Claudio J. Tessone
  • Frank Schweitzer

Abstract

How do humans respond to indirect social influence when making decisions? We analysed an experiment where subjects had to repeatedly guess the correct answer to factual questions, while 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 \emph{linearly} on the distance to the mean of all guesses. This is a remarkable, and yet surprisingly simple, statistical regularity. It holds across all questions analysed, even though the correct answers differ in several orders of magnitude. Our finding supports the assumption that individual diversity does not affect the response to indirect social influence. It also complements previous results on the nonlinear response in information-rich scenarios. We argue that the nature of the response to social influence crucially changes with the level of information aggregation. This insight contributes to the empirical foundation of models for collective decisions under social influence.

Suggested Citation

  • Pavlin Mavrodiev & Claudio J. Tessone & Frank Schweitzer, "undated". "Quantifying the effects of social influence," Working Papers ETH-RC-13-001, ETH Zurich, Chair of Systems Design.
    • Handle: RePEc:stz:wpaper:eth-rc-13-001
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Gabriel Madirolas & Gonzalo G de Polavieja, 2015. "Improving Collective Estimations Using Resistance to Social Influence," PLOS Computational Biology, Public Library of Science, vol. 11(11), pages 1-16, November.
    2. Corentin Vande Kerckhove & Samuel Martin & Pascal Gend & Peter J Rentfrow & Julien M Hendrickx & Vincent D Blondel, 2016. "Modelling Influence and Opinion Evolution in Online Collective Behaviour," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-25, June.
    3. Jayles, Bertrand & Escobedo, Ramon & Cezera, Stéphane & Blanchet, Adrien & Kameda, Tatsuya & Sire, Clément & Théraulaz, Guy, 2020. "The impact of incorrect social information on collective wisdom in human groups," IAST Working Papers 20-106, Institute for Advanced Study in Toulouse (IAST).
    4. Bertrand Jayles & Ramon Escobedo & Stéphane Cezera & Adrien Blanchet & Tatsuya Kameda & Clément Sire & Guy Théraulaz, 2020. "The impact of incorrect social information on collective wisdom in human groups," Post-Print hal-03019820, HAL.
    5. Mavrodiev, Pavlin & Schweitzer, Frank, 2021. "The ambigous role of social influence on the wisdom of crowds: An analytic approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 567(C).
    6. Bertrand Jayles & Clément Sire & Ralf H J M Kurvers, 2021. "Crowd control: Reducing individual estimation bias by sharing biased social information," PLOS Computational Biology, Public Library of Science, vol. 17(11), pages 1-28, November.
    7. Jean M Carlson & David L Alderson & Sean P Stromberg & Danielle S Bassett & Emily M Craparo & Francisco Guiterrez-Villarreal & Thomas Otani, 2014. "Measuring and Modeling Behavioral Decision Dynamics in Collective Evacuation," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-17, February.
    8. Evangelos Ioannidis & Nikos Varsakelis & Ioannis Antoniou, 2020. "Promoters versus Adversaries of Change: Agent-Based Modeling of Organizational Conflict in Co-Evolving Networks," Mathematics, MDPI, vol. 8(12), pages 1-25, December.

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