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A Model Of Nonbelief In The Law Of Large Numbers

Author

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  • Daniel J. Benjamin
  • Matthew Rabin
  • Collin Raymond

Abstract

People believe that, even in very large samples, proportions of binary signals might depart significantly from the population mean. We model this "non-belief in the Law of Large Numbers" by assuming that a person believes that proportions in any given sample might be determined by a rate different than the true rate. In prediction, a non-believer expects the distribution of signals will have fat tails, more so for larger samples. In inference, a non-believer remains uncertain and influenced by priors even after observing an arbitrarily large sample. We explore implications for beliefs and behavior in a variety of economic settings.
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Suggested Citation

  • Daniel J. Benjamin & Matthew Rabin & Collin Raymond, 2016. "A Model Of Nonbelief In The Law Of Large Numbers," Journal of the European Economic Association, European Economic Association, vol. 14(2), pages 515-544, April.
  • Handle: RePEc:bla:jeurec:v:14:y:2016:i:2:p:515-544
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    File URL: http://hdl.handle.net/10.1111/jeea.2016.14.issue-2
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    References listed on IDEAS

    as
    1. David M. Grether, 1980. "Bayes Rule as a Descriptive Model: The Representativeness Heuristic," The Quarterly Journal of Economics, Oxford University Press, vol. 95(3), pages 537-557.
    2. Matthew Rabin, 2002. "Inference by Believers in the Law of Small Numbers," The Quarterly Journal of Economics, Oxford University Press, vol. 117(3), pages 775-816.
    3. Shunichiro Sasaki & Toshiji Kawagoe, 2007. "Belief Updating in Individual and Social Learning: A Field Experiment on the Internet," ISER Discussion Paper 0690, Institute of Social and Economic Research, Osaka University.
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    Cited by:

    1. repec:eee:mateco:v:70:y:2017:i:c:p:90-104 is not listed on IDEAS
    2. Jesse Aaron Zinn, 2015. "Expanding the Weighted Updating Model," Economics Bulletin, AccessEcon, vol. 35(1), pages 182-186.
    3. repec:eme:afrpps:afr-05-2016-0045 is not listed on IDEAS
    4. Farouq Abdulaziz Masoudy, 2018. "Accurate Evaluation of Asset Pricing Under Uncertainty and Ambiguity of Information," Papers 1801.06966, arXiv.org, revised Mar 2018.
    5. Jonathan Zinman, 2014. "Consumer Credit: Too Much or Too Little (or Just Right)?," The Journal of Legal Studies, University of Chicago Press, vol. 43(S2), pages 209-237.

    More about this item

    JEL classification:

    • B49 - Schools of Economic Thought and Methodology - - Economic Methodology - - - Other
    • D03 - Microeconomics - - General - - - Behavioral Microeconomics: Underlying Principles
    • D14 - Microeconomics - - Household Behavior - - - Household Saving; Personal Finance
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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