IDEAS home Printed from https://ideas.repec.org/p/cwl/cwldpp/2161r.html
   My bibliography  Save this paper

Learning under Diverse World Views: Model-Based Inference

Author

Listed:

Abstract

People reason about uncertainty with deliberately incomplete models, including only the most relevant variables. How do people hampered by different, incomplete views of the world learn from each other? We introduce a model of “model-based inference.” Model-based reasoners partition an otherwise hopelessly complex state space into a manageable model. We nd that unless the differences in agents’ models are trivial, interactions will often not lead agents to have common beliefs, and indeed the correct-model belief will typically lie outside the convex hull of the agents’ beliefs. However, if the agents’ models have enough in common, then interacting will lead agents to similar beliefs, even if their models also exhibit some bizarre idiosyncrasies and their information is widely dispersed.

Suggested Citation

  • George J. Mailath & Larry Samuelson, 2019. "Learning under Diverse World Views: Model-Based Inference," Cowles Foundation Discussion Papers 2161R, Cowles Foundation for Research in Economics, Yale University, revised Sep 2019.
  • Handle: RePEc:cwl:cwldpp:2161r
    as

    Download full text from publisher

    File URL: https://cowles.yale.edu/sites/default/files/files/pub/d21/d2161-r.pdf
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. Jehiel, Philippe & Koessler, Frédéric, 2008. "Revisiting games of incomplete information with analogy-based expectations," Games and Economic Behavior, Elsevier, vol. 62(2), pages 533-557, March.
    2. Brandenburger, Adam & Dekel, Eddie & Geanakoplos, John, 1992. "Correlated equilibrium with generalized information structures," Games and Economic Behavior, Elsevier, vol. 4(2), pages 182-201, April.
    3. Gilboa, Itzhak & Samuelson, Larry, 2012. "Subjectivity in inductive inference," Theoretical Economics, Econometric Society, vol. 7(2), May.
    4. Jehiel, Philippe, 2005. "Analogy-based expectation equilibrium," Journal of Economic Theory, Elsevier, vol. 123(2), pages 81-104, August.
    5. Harrison Hong & Jeremy C. Stein & Jialin Yu, 2007. "Simple Forecasts and Paradigm Shifts," Journal of Finance, American Finance Association, vol. 62(3), pages 1207-1242, June.
    6. Ran Spiegler, 2016. "Bayesian Networks and Boundedly Rational Expectations," The Quarterly Journal of Economics, Oxford University Press, vol. 131(3), pages 1243-1290.
    7. George J. Mailath & Larry Samuelson, 2019. "The Wisdom of a Confused Crowd:Model-Based Inference," PIER Working Paper Archive 19-001, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    8. Erik Eyster & Michele Piccione, 2013. "An Approach to Asset Pricing Under Incomplete and Diverse Perceptions," Econometrica, Econometric Society, vol. 81(4), pages 1483-1506, July.
    9. Geanakoplos, John D. & Polemarchakis, Heraklis M., 1982. "We can't disagree forever," Journal of Economic Theory, Elsevier, vol. 28(1), pages 192-200, October.
    10. Acemoglu, Daron & Chernozhukov, Victor & Yildiz, Muhamet, 2016. "Fragility of asymptotic agreement under Bayesian learning," Theoretical Economics, Econometric Society, vol. 11(1), January.
    11. Nabil I. Al-Najjar, 2009. "Decision Makers as Statisticians: Diversity, Ambiguity, and Learning," Econometrica, Econometric Society, vol. 77(5), pages 1371-1401, September.
    12. Michael Ostrovsky, 2012. "Information Aggregation in Dynamic Markets With Strategic Traders," Econometrica, Econometric Society, vol. 80(6), pages 2595-2647, November.
    13. Morris, Stephen, 1994. "Trade with Heterogeneous Prior Beliefs and Asymmetric Information," Econometrica, Econometric Society, vol. 62(6), pages 1327-1347, November.
    14. Brandenburger, Adam & Dekel, Eddie, 1987. "Common knowledge with probability 1," Journal of Mathematical Economics, Elsevier, vol. 16(3), pages 237-245, June.
    15. Kyle, Albert S, 1985. "Continuous Auctions and Insider Trading," Econometrica, Econometric Society, vol. 53(6), pages 1315-1335, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Gabriel Martinez & Nicholas H. Tenev, 2020. "Optimal Echo Chambers," Papers 2010.01249, arXiv.org, revised Oct 2020.

    More about this item

    Keywords

    Wisdom of the Crowd; Information aggregation; Common prior; NonBayesian updating;

    JEL classification:

    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:cwl:cwldpp:2161r. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Matthew Regan). General contact details of provider: http://edirc.repec.org/data/cowleus.html .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.