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Learning under Diverse World Views: Model-Based Inference

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  • George J. Mailath
  • Larry Samuelson

Abstract

People reason about uncertainty with deliberately incomplete models. 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. Unless the differences in agents' models are trivial, interactions will often not lead agents to have common beliefs or beliefs near the correct-model belief. 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.

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  • 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.
    • Handle: RePEc:aea:aecrev:v:110:y:2020:i:5:p:1464-1501
    DOI: 10.1257/aer.20190080
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    Cited by:

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    2. Zohar, Osnat, 2024. "Cyclicality of uncertainty and disagreement," Journal of Monetary Economics, Elsevier, vol. 143(C).
    3. 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.
    4. Cuimin Ba, 2021. "Robust Misspecified Models and Paradigm Shifts," Papers 2106.12727, arXiv.org, revised Aug 2023.
    5. Philippe Jehiel, 2022. "Analogy-Based Expectation Equilibrium and Related Concepts:Theory, Applications, and Beyond," Working Papers halshs-03735680, HAL.
    6. 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.
    7. 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).
    8. Drew Fudenberg & Giacomo Lanzani & Philipp Strack, 2021. "Limit Points of Endogenous Misspecified Learning," Econometrica, Econometric Society, vol. 89(3), pages 1065-1098, May.
    9. Gabriel Martinez & Nicholas H. Tenev, 2020. "Optimal Echo Chambers," Papers 2010.01249, arXiv.org, revised Feb 2024.
    10. Kai Barron & Tilman Fries, 2023. "Narrative Persuasion," Rationality and Competition Discussion Paper Series 469, CRC TRR 190 Rationality and Competition.
    11. Enrique Urbano Arellano & Xinyang Wang, 2023. "Social Learning of General Rules," Papers 2310.15861, arXiv.org.
    12. Maarten Meeuwis & Jonathan A. Parker & Antoinette Schoar & Duncan Simester, 2022. "Belief Disagreement and Portfolio Choice," Journal of Finance, American Finance Association, vol. 77(6), pages 3191-3247, December.
    13. Leung, B. T. K., 2020. "Learning in a Small/Big World," Cambridge Working Papers in Economics 2085, Faculty of Economics, University of Cambridge.
    14. Yair Antler ad Ran Spiegler, 2024. "Competitive Markets with Imperfectly Discerning Consumers," Papers 2409.14885, arXiv.org.
    15. Benson Tsz Kin Leung, 2020. "Learning in a Small/Big World," Papers 2009.11917, arXiv.org, revised Mar 2023.
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    17. 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.
    18. Andrew Ellis & Heidi Christina Thysen, 2021. "Subjective Causality in Choice," Papers 2106.05957, arXiv.org, revised Dec 2022.

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    JEL classification:

    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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