IDEAS home Printed from https://ideas.repec.org/a/nas/journl/v118y2021pe2106292118.html
   My bibliography  Save this article

Dynamical system model predicts when social learners impair collective performance

Author

Listed:
  • Vicky Chuqiao Yang

    (Santa Fe Institute, Santa Fe, NM 87501)

  • Mirta Galesic

    (Santa Fe Institute, Santa Fe, NM 87501; Complexity Science Hub Vienna, A-1080 Vienna, Austria; Vermont Complex Systems Center, University of Vermont, Burlington, VT 05405)

  • Harvey McGuinness

    (Zanvyl Krieger School of Arts and Sciences, Johns Hopkins University, Baltimore, MD 21218)

  • Ani Harutyunyan

    (Sunwater Institute, North Bethesda, MD 20852)

Abstract

A key question concerning collective decisions is whether a social system can settle on the best available option when some members learn from others instead of evaluating the options on their own. This question is challenging to study, and previous research has reached mixed conclusions, because collective decision outcomes depend on the insufficiently understood complex system of cognitive strategies, task properties, and social influence processes. This study integrates these complex interactions together in one general yet partially analytically tractable mathematical framework using a dynamical system model. In particular, it investigates how the interplay of the proportion of social learners, the relative merit of options, and the type of conformity response affect collective decision outcomes in a binary choice. The model predicts that, when the proportion of social learners exceeds a critical threshold, a bistable state appears in which the majority can end up favoring either the higher- or lower-merit option, depending on fluctuations and initial conditions. Below this threshold, the high-merit option is chosen by the majority. The critical threshold is determined by the conformity response function and the relative merits of the two options. The study helps reconcile disagreements about the effect of social learners on collective performance and proposes a mathematical framework that can be readily adapted to extensions investigating a wider variety of dynamics.

Suggested Citation

  • Vicky Chuqiao Yang & Mirta Galesic & Harvey McGuinness & Ani Harutyunyan, 2021. "Dynamical system model predicts when social learners impair collective performance," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 118(35), pages 2106292118-, August.
  • Handle: RePEc:nas:journl:v:118:y:2021:p:e2106292118
    as

    Download full text from publisher

    File URL: http://www.pnas.org/content/118/35/e2106292118.full
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhang, Wei & Brandes, Ulrik, 2023. "Conformity versus credibility: A coupled rumor-belief model," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    2. Li Zhenpeng & Tang Xijin, 2021. "Stimuli strategy and learning dynamics promote the wisdom of crowds," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 94(12), pages 1-8, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nas:journl:v:118:y:2021:p:e2106292118. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Eric Cain (email available below). General contact details of provider: http://www.pnas.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.