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Experiments on Belief Formation in Networks

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  • Veronika Grimm
  • Friederike Mengel

Abstract

We study belief formation in social networks using a laboratory experiment. Participants in our experiment observe an imperfect private signal on the state of the world and then simultaneously and repeatedly guess the state, observing the guesses of their network neighbors in each period. Across treatments we vary the network structure and the amount of information participants have about the network. Our first result shows that information about the network structure matters and in particular affects the share of correct guesses in the network. This is inconsistent with the widely used naive (deGroot) model. The naive model is, however, consistent with a larger share of individual decisions than the competing Bayesian model, whereas both models correctly predict only about 25%–30% of consensus beliefs. We then estimate a larger class of models and find that participants do indeed take network structure into account when updating beliefs. In particular they discount information from neighbors if it is correlated, but in a more rudimentary way than a Bayesian learner would.

Suggested Citation

  • Veronika Grimm & Friederike Mengel, 2020. "Experiments on Belief Formation in Networks," Journal of the European Economic Association, European Economic Association, vol. 18(1), pages 49-82.
    • Handle: RePEc:oup:jeurec:v:18:y:2020:i:1:p:49-82.
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    File URL: http://hdl.handle.net/10.1093/jeea/jvy038
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    Cited by:

    1. Michel Grabisch & Agnieszka Rusinowska, 2020. "A Survey on Nonstrategic Models of Opinion Dynamics," Games, MDPI, vol. 11(4), pages 1-29, December.
    2. Stephan Leitner, 2021. "On the dynamics emerging from pandemics and infodemics," Mind & Society: Cognitive Studies in Economics and Social Sciences, Springer;Fondazione Rosselli, vol. 20(1), pages 135-141, June.
    3. Buechel, Berno & Klößner, Stefan & Meng, Fanyuan & Nassar, Anis, 2023. "Misinformation due to asymmetric information sharing," Journal of Economic Dynamics and Control, Elsevier, vol. 150(C).
    4. Zakharov, Alexei & Bondarenko, Oxana, 2021. "Social status and social learning," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 90(C).
    5. Kara Layne Johnson & Jennifer L. Walsh & Yuri A. Amirkhanian & Nicole Bohme Carnegie, 2021. "Performance of a Genetic Algorithm for Estimating DeGroot Opinion Diffusion Model Parameters for Health Behavior Interventions," IJERPH, MDPI, vol. 18(24), pages 1-22, December.
    6. Syngjoo Choi & Sanjeev Goyal & Frederic Moisan & Yu Yang Tony To, 2023. "Learning in Networks: An Experiment on Large Networks with Real-World Features," Management Science, INFORMS, vol. 69(5), pages 2778-2787, May.
    7. Li, Wei & Tan, Xu, 2021. "Cognitively-constrained learning from neighbors," Games and Economic Behavior, Elsevier, vol. 129(C), pages 32-54.
    8. Andreas Bjerre-Nielsen & Martin Benedikt Busch, 2022. "Statistical inference in social networks: how sampling bias and uncertainty shape decisions," Papers 2205.13046, arXiv.org.
    9. Friederike Mengel, 2021. "Gender Bias In Opinion Aggregation," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 62(3), pages 1055-1080, August.
    10. Phillip J. Monin & Richard Bookstaber, 2021. "Information flows and crashes in dynamic social networks," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 16(3), pages 471-495, July.

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