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False Information from Near and Far

Author

Listed:
  • Christophe Bravard

    (GAEL - Laboratoire d'Economie Appliquée de Grenoble - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - UGA - Université Grenoble Alpes - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - UGA - Université Grenoble Alpes)

  • Jacques Durieu

    (CREG - Centre de recherche en économie de Grenoble - UGA - Université Grenoble Alpes, GAEL - Laboratoire d'Economie Appliquée de Grenoble - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - UGA - Université Grenoble Alpes - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - UGA - Université Grenoble Alpes)

  • Sudipta Sarangi

    (DIW Berlin - Deutsches Institut für Wirtschaftsforschung, Virginia Tech [Blacksburg])

  • Stéphan Sémirat

    (GAEL - Laboratoire d'Economie Appliquée de Grenoble - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - UGA - Université Grenoble Alpes - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - UGA - Université Grenoble Alpes)

Abstract

We study message credibility in social networks with biased and unbiased agents. Biased agents prefer a specific outcome while unbiased agents prefer the true state of the world. Each agent who receives a message knows the identity (but not type) of the message creator and only the identity and types of their immediate neighbors. We characterize the perfect Bayesian equilibria of this game and demonstrate filtering by the network: the posterior beliefs of agents depend on the distance a message travels. Unbiased agents, who receive a message from a biased agent, are more likely to assign a higher credibility and transmit it further when they are further away from the source. For a given network, we compute the probability that it will always support the communication of messages by unbiased agents. Finally, we establish that under certain parameters, this probability increases when agents are uncertain about their network location.

Suggested Citation

  • Christophe Bravard & Jacques Durieu & Sudipta Sarangi & Stéphan Sémirat, 2023. "False Information from Near and Far," Post-Print hal-03850289, HAL.
    • Handle: RePEc:hal:journl:hal-03850289
    DOI: 10.1016/j.geb.2022.11.002
    Note: View the original document on HAL open archive server: https://hal.science/hal-03850289v1
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    Cited by:

    1. Christophe Bravard & Jacques Durieu & Sudipta Sarangi & Corinne Touati, 2024. "Influence and Counter-Influence in Networks [Mécanismes d'influence et de contre-influence dans les réseaux]," Post-Print hal-04733885, HAL.

    More about this item

    Keywords

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    JEL classification:

    • D74 - Microeconomics - - Analysis of Collective Decision-Making - - - Conflict; Conflict Resolution; Alliances; Revolutions
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation

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