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Free information disrupts even Bayesian crowds.

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  • 1Department of Sociology, Faculty of Behavioral and Social Sciences, University of Groningen, Grote Rozenstraat 31, 9712 TG, Groningen, The Netherlands.

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Summary
This summary is machine-generated.

Unconstrained information exchange on social media platforms can harm group beliefs, even with ideal agents. Carefully consider information flow constraints in network design.

Keywords:
Bayesian agent based modelepistemic inequalityhomophilysocial learningwisdom of crowds

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Area of Science:

  • Computational Social Science
  • Information Science
  • Network Theory

Background:

  • Contemporary information networks, like social media, assume unconstrained user information exchange.
  • This assumption is central to their design and societal impact.

Purpose of the Study:

  • To investigate the effects of unconstrained information exchange on group belief correctness.
  • To determine if idealized agents are susceptible to negative outcomes from open information flow.

Main Methods:

  • Utilized a computational agent-based model.
  • Simulated groups of truth-seeking, cooperative agents with perfect information-processing abilities.

Main Results:

  • Even with idealized agents, unconstrained information exchange led to detrimental effects on group belief correctness.
  • The findings suggest potential negative consequences in real-world scenarios.

Conclusions:

  • Unconstrained information flow can be harmful to group belief accuracy.
  • Constraints on information flow warrant careful consideration in designing impactful communication networks, including social media platforms.