IDEAS home Printed from https://ideas.repec.org/a/eee/ecolet/v202y2021ics0165176521001166.html
   My bibliography  Save this article

On a diversity of perspectives and world views: Learning under Bayesian vis-á-vis DeGroot updating

Author

Listed:
  • Ghosh, Aniruddha
  • Khan, M. Ali

Abstract

In an influential recent paper, Mailath–Samuelson formalize learning and reasoning through “model-based inference” and Bayesian updating. In this announcement, we substitute DeGroot’s heuristic for Bayesian updating by (i) furnishing a plausible interaction matrix that agents use to weigh each other’s beliefs, and by (ii) using this matrix to derive properties of the process for the DeGroot updating of beliefs by agents and oracles. The alternative argumentation that we provide facilitates bridging the literature on networks and that on model-based learning and inference; and it identifies productive and ongoing directions for further investigation.

Suggested Citation

  • Ghosh, Aniruddha & Khan, M. Ali, 2021. "On a diversity of perspectives and world views: Learning under Bayesian vis-á-vis DeGroot updating," Economics Letters, Elsevier, vol. 202(C).
    • Handle: RePEc:eee:ecolet:v:202:y:2021:i:c:s0165176521001166
    DOI: 10.1016/j.econlet.2021.109839
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0165176521001166
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.econlet.2021.109839?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. George J. Mailath & Larry Samuelson, 2020. "Learning under Diverse World Views: Model-Based Inference," American Economic Review, American Economic Association, vol. 110(5), pages 1464-1501, May.
    2. Jan-Willem Romeijn & Olivier Roy, 2018. "All agreed: Aumann meets DeGroot," Theory and Decision, Springer, vol. 85(1), pages 41-60, July.
    3. Pooya Molavi & Alireza Tahbaz‐Salehi & Ali Jadbabaie, 2018. "A Theory of Non‐Bayesian Social Learning," Econometrica, Econometric Society, vol. 86(2), pages 445-490, March.
    4. Benjamin Golub & Matthew O. Jackson, 2012. "How Homophily Affects the Speed of Learning and Best-Response Dynamics," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 127(3), pages 1287-1338.
    5. Raffaella Giacomini & Vasiliki Skreta & Javier Turen, 2020. "Heterogeneity, Inattention, and Bayesian Updates," American Economic Journal: Macroeconomics, American Economic Association, vol. 12(1), pages 282-309, January.
    6. Arun G. Chandrasekhar & Horacio Larreguy & Juan Pablo Xandri, 2020. "Testing Models of Social Learning on Networks: Evidence From Two Experiments," Econometrica, Econometric Society, vol. 88(1), pages 1-32, January.
    7. Richard Bradley, 2018. "Learning from others: conditioning versus averaging," Theory and Decision, Springer, vol. 85(1), pages 5-20, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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).
    2. Simone Cerreia-Vioglio & Roberto Corrao & Giacomo Lanzani, 2020. "Robust Opinion Aggregation and its Dynamics," Working Papers 662, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    3. Crès, Hervé & Tvede, Mich, 2022. "Aggregation of opinions in networks of individuals and collectives," Journal of Economic Theory, Elsevier, vol. 199(C).
    4. Dasaratha, Krishna & He, Kevin, 2020. "Network structure and naive sequential learning," Theoretical Economics, Econometric Society, vol. 15(2), May.
    5. Anufriev, Mikhail & Borissov, Kirill & Pakhnin, Mikhail, 2023. "Dissonance minimization and conversation in social networks," Journal of Economic Behavior & Organization, Elsevier, vol. 215(C), pages 167-191.
    6. Isabel Melguizo, 2019. "Homophily and the Persistence of Disagreement," The Economic Journal, Royal Economic Society, vol. 129(619), pages 1400-1424.
    7. Li, Wei & Tan, Xu, 2021. "Cognitively-constrained learning from neighbors," Games and Economic Behavior, Elsevier, vol. 129(C), pages 32-54.
    8. Bowen, T. Renee & Galperti, Simone & Dmitriev, Danil, 2021. "Learning from Shared News: When Abundant Information Leads to Belief Polarization," CEPR Discussion Papers 15789, C.E.P.R. Discussion Papers.
    9. Gallo, E. & Langtry, A., 2020. "Social Networks, Confirmation Bias and Shock Elections," Cambridge Working Papers in Economics 2099, Faculty of Economics, University of Cambridge.
    10. Polanski, Arnold & Vega-Redondo, Fernando, 2023. "Homophily and influence," Journal of Economic Theory, Elsevier, vol. 207(C).
    11. Rapanos, Theodoros, 2023. "What makes an opinion leader: Expertise vs popularity," Games and Economic Behavior, Elsevier, vol. 138(C), pages 355-372.
    12. Edoardo Gallo & Alastair Langtry, 2020. "Social networks, confirmation bias and shock elections," Papers 2011.00520, arXiv.org.
    13. Dasaratha, Krishna & He, Kevin, 2021. "An experiment on network density and sequential learning," Games and Economic Behavior, Elsevier, vol. 128(C), pages 182-192.
    14. Sebastiano Della Lena, 2019. "Non-Bayesian Social Learning and the Spread of Misinformation in Networks," Working Papers 2019:09, Department of Economics, University of Venice "Ca' Foscari".
    15. Boucher, Vincent & Dedewanou, F. Antoine & Dufays, Arnaud, 2022. "Peer-induced beliefs regarding college participation," Economics of Education Review, Elsevier, vol. 90(C).
    16. Nicole Tabasso, 2014. "Diffusion of Multiple Information," School of Economics Discussion Papers 0914, School of Economics, University of Surrey.
    17. Allouch, Nizar, 2017. "The cost of segregation in (social) networks," Games and Economic Behavior, Elsevier, vol. 106(C), pages 329-342.
    18. Gabriel Martinez & Nicholas H. Tenev, 2020. "Optimal Echo Chambers," Papers 2010.01249, arXiv.org, revised Feb 2024.
    19. Kai Barron & Heike Harmgart & Steffen Huck & Sebastian O. Schneider & Matthias Sutter, 2023. "Discrimination, Narratives, and Family History: An Experiment with Jordanian Host and Syrian Refugee Children," The Review of Economics and Statistics, MIT Press, vol. 105(4), pages 1008-1016, July.
    20. Sam Tavassoli & Viroj Jienwatcharamongkhol & Pia Arenius, 2023. "Colocation of Entrepreneurs and New Firm Survival: Role of New Firm Founder’s Experiential Relatedness to Local Entrepreneurs," Entrepreneurship Theory and Practice, , vol. 47(4), pages 1421-1459, July.

    More about this item

    Keywords

    Bayesian updating; DeGroot’s consensus; Learning; Models; Oracles;
    All these keywords.

    JEL classification:

    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecolet:v:202:y:2021:i:c:s0165176521001166. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ecolet .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.