IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v203y2023ics0167715223001451.html
   My bibliography  Save this article

Doob’s consistency of a non-Bayesian updating process

Author

Listed:
  • Kawakami, Hajime

Abstract

We consider an asymptotic property of the posterior distribution (belief) of a non-Bayesian updating process. For the Bayesian updating process, the corresponding property is known as Doob’s consistency. The result of our study provides a sufficient condition for the non-Bayesian updating process to satisfy this property.

Suggested Citation

  • Kawakami, Hajime, 2023. "Doob’s consistency of a non-Bayesian updating process," Statistics & Probability Letters, Elsevier, vol. 203(C).
    • Handle: RePEc:eee:stapro:v:203:y:2023:i:c:s0167715223001451
    DOI: 10.1016/j.spl.2023.109921
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167715223001451
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.spl.2023.109921?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. De Filippis, Roberta & Guarino, Antonio & Jehiel, Philippe & Kitagawa, Toru, 2022. "Non-Bayesian updating in a social learning experiment," Journal of Economic Theory, Elsevier, vol. 199(C).
    2. Jadbabaie, Ali & Molavi, Pooya & Sandroni, Alvaro & Tahbaz-Salehi, Alireza, 2012. "Non-Bayesian social learning," Games and Economic Behavior, Elsevier, vol. 76(1), pages 210-225.
    3. , G. & , & ,, 2008. "Non-Bayesian updating: A theoretical framework," Theoretical Economics, Econometric Society, vol. 3(2), June.
    4. Lijoi, Antonio & Prünster, Igor & Walker, Stephen G., 2007. "Bayesian Consistency For Stationary Models," Econometric Theory, Cambridge University Press, vol. 23(4), pages 749-759, August.
    5. Pati, Debdeep & Dunson, David B. & Tokdar, Surya T., 2013. "Posterior consistency in conditional distribution estimation," Journal of Multivariate Analysis, Elsevier, vol. 116(C), pages 456-472.
    6. Ghosal,Subhashis & van der Vaart,Aad, 2017. "Fundamentals of Nonparametric Bayesian Inference," Cambridge Books, Cambridge University Press, number 9780521878265.
    7. Epstein Larry G & Noor Jawwad & Sandroni Alvaro, 2010. "Non-Bayesian Learning," The B.E. Journal of Theoretical Economics, De Gruyter, vol. 10(1), pages 1-20, January.
    8. Pietro Ortoleva, 2012. "Modeling the Change of Paradigm: Non-Bayesian Reactions to Unexpected News," American Economic Review, American Economic Association, vol. 102(6), pages 2410-2436, October.
    9. Timo Henckel & Gordon D. Menzies & Peter G. Moffatt & Daniel J. Zizzo, 2022. "Belief adjustment: a double hurdle model and experimental evidence," Experimental Economics, Springer;Economic Science Association, vol. 25(1), pages 26-67, February.
    10. Gagnon-Bartsch, Tristan & Bushong, Benjamin, 2022. "Learning with misattribution of reference dependence," Journal of Economic Theory, Elsevier, vol. 203(C).
    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. Aislinn Bohren & Daniel Hauser, 2018. "Social Learning with Model Misspeciification: A Framework and a Robustness Result," PIER Working Paper Archive 18-017, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 01 Jul 2018.
    2. Yves Breitmoser & Justin Valasek & Justin Mattias Valasek, 2023. "Why Do Committees Work?," CESifo Working Paper Series 10800, CESifo.
    3. He, Xue Dong & Xiao, Di, 2017. "Processing consistency in non-Bayesian inference," Journal of Mathematical Economics, Elsevier, vol. 70(C), pages 90-104.
    4. Breitmoser, Yves & Valasek, Justin, 2023. "Why do committees work?," Discussion Paper Series in Economics 18/2023, Norwegian School of Economics, Department of Economics.
    5. Kwon, Seokbeom & Motohashi, Kazuyuki, 2017. "How institutional arrangements in the National Innovation System affect industrial competitiveness: A study of Japan and the U.S. with multiagent simulation," Technological Forecasting and Social Change, Elsevier, vol. 115(C), pages 221-235.
    6. Sun, Lan, 2016. "Hypothesis testing equilibrium in signaling games," Center for Mathematical Economics Working Papers 557, Center for Mathematical Economics, Bielefeld University.
    7. Manzan, Sebastiano, 2021. "Are professional forecasters Bayesian?," Journal of Economic Dynamics and Control, Elsevier, vol. 123(C).
    8. Cheng, Ing-Haw & Hsiaw, Alice, 2022. "Distrust in experts and the origins of disagreement," Journal of Economic Theory, Elsevier, vol. 200(C).
    9. Ali Jadbabaie & Pooya Molavi & Alvaro Sandroni & Alireza Tahbaz-Salehi, 2009. "Non-Bayesian Social Learning, Third Version," PIER Working Paper Archive 11-025, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 05 Aug 2011.
    10. I. Votsi & G. Gayraud & V. S. Barbu & N. Limnios, 2021. "Hypotheses testing and posterior concentration rates for semi-Markov processes," Statistical Inference for Stochastic Processes, Springer, vol. 24(3), pages 707-732, October.
    11. Zimper, Alexander, 2023. "Unrealized arbitrage opportunities in naive equilibria with non-Bayesian belief processes," Mathematical Social Sciences, Elsevier, vol. 125(C), pages 27-41.
    12. J. Aislinn Bohren & Daniel N. Hauser, 2023. "Behavioral Foundations of Model Misspecification," PIER Working Paper Archive 23-007, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    13. Li, Wenhui & Wilde, Christian, 2020. "Belief formation and belief updating under ambiguity: Evidence from experiments," SAFE Working Paper Series 251, Leibniz Institute for Financial Research SAFE, revised 2020.
    14. Bogaçhan Çelen & Sen Geng & Huihui Li, 2018. "Belief Error and Non-Bayesian Social Learning: An Experimental Evidence," GRU Working Paper Series GRU_2018_022, City University of Hong Kong, Department of Economics and Finance, Global Research Unit.
    15. Bauer, Kevin & von Zahn, Moritz & Hinz, Oliver, 2022. "Expl(AI)ned: The impact of explainable Artificial Intelligence on cognitive processes," SAFE Working Paper Series 315, Leibniz Institute for Financial Research SAFE, revised 2022.
    16. Riella, Gil, 2013. "Preference for Flexibility and Dynamic Consistency," Journal of Economic Theory, Elsevier, vol. 148(6), pages 2467-2482.
    17. Bohren, Aislinn & Hauser, Daniel, 2017. "Learning with Heterogeneous Misspecified Models: Characterization and Robustness," CEPR Discussion Papers 12036, C.E.P.R. Discussion Papers.
    18. Zhang, Hanzhe, 2013. "Evolutionary justifications for non-Bayesian beliefs," Economics Letters, Elsevier, vol. 121(2), pages 198-201.
    19. Azzimonti, Marina & Fernandes, Marcos, 2023. "Social media networks, fake news, and polarization," European Journal of Political Economy, Elsevier, vol. 76(C).
    20. Aislinn Bohren & Daniel Hauser, 2017. "Bounded Rationality And Learning: A Framwork and A Robustness Result," PIER Working Paper Archive 17-007, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 01 May 2017.

    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:stapro:v:203:y:2023:i:c:s0167715223001451. 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/wps/find/journaldescription.cws_home/622892/description#description .

    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.