Truth and Conformity on Networks.

Typically, public discussions of questions of social import exhibit two important properties: (1) they are influenced by conformity bias, and (2) the influence of conformity is expressed via social networks. We examine how social learning on networks proceeds under the influence of conformity bias....

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Publicado en:Erkenntnis Vol. 86; no. 6; pp. 1509 - 1531
Autores principales: Mohseni, Aydin, Williams, Cole Randall
Formato: Artículo
Publicado: Springer Nature Dec2021
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Acceso en línea:Ver este registro en EBSCOhost
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      pub: Springer Nature
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        10.1007/s10670-019-00167-6
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        atl: Truth and Conformity on Networks.
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          Mohseni, Aydin
          Williams, Cole Randall
        affil: Department of Logic and Philosophy of Science, University of California, Irvine, 3151 Social Science Plaza A, 92697-5100, Irvine, CA, USA
      su:
        Conformity
        Social learning
        Public opinion
        Social networks
      sug:
        subj:
          Conformity
          Social learning
          Public opinion
          Social networks
      ab: Typically, public discussions of questions of social import exhibit two important properties: (1) they are influenced by conformity bias, and (2) the influence of conformity is expressed via social networks. We examine how social learning on networks proceeds under the influence of conformity bias. In our model, heterogeneous agents express public opinions where those expressions are driven by the competing priorities of accuracy and of conformity to one's peers. Agents learn, by Bayesian conditionalization, from private evidence from nature, and from the public declarations of other agents. Our key findings are that networks that produce configurations of social relationships that sustain a diversity of opinions empower honest communication and reliable acquisition of true beliefs, and that the networks that do this best turn out to be those which are both less centralized and less connected.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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      custom: Erkenntnis is a copyright of Springer, 2021. All Rights Reserved.
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