Belief revision in a micro-social network: Modeling sensitivity to statistical dependencies in social learning

Jan-Philipp Fränken, Nikolaos Theodoropoulos, Adam Moore, Neil R Bramley

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Both in professional domains and everyday life, people frequently rely on their 'social network neighbors' to form and update their beliefs. Here, it is important to understand how people deal with statistical dependencies underlying correlated beliefs in their social environment. Using an interface allowing us to elicit full probabilistic beliefs from people, we investigated people's ability to distinguish between the evidential value of social information across three conditions: integrating independent beliefs, dependent beliefs formed on the basis of the shared evidence, and dependent beliefs that result from sequential communication between sources. Comparing participants' judgments to a normative Bayesian model, we found that they distinguished dependent from independent sources but treated social sources as much weaker sources of evidence than direct experience. The value of eliciting and visualising beliefs as full probability distributions and potential implications for modeling belief revision in social networks (e.g., using agent-based models of echo chambers) are discussed.
Original languageEnglish
Title of host publicationProceedings of the 42nd Annual Meeting of the Cognitive Science Society
Place of PublicationAustin, TX
PublisherCognitive Science Society
ISBN (Print)9781713818977
Publication statusPublished - 1 Aug 2020

Keywords / Materials (for Non-textual outputs)

  • social networks
  • probabilistic beliefs
  • sequential belief updating
  • information cascades
  • Bayesian modeling


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