A Model of Nonbelief in the Law of Large Numbers

Daniel Benjamin, Matthew Rabin and Collin Raymond

Journal of the European Economic Association, 2016, vol. 14, issue 2, 515-544

Abstract: People believe that, even in very large samples, proportions of binary signals might depart significantly from the population mean. We model this “nonbelief 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 nonbeliever expects the distribution of signals will have fat tails. In inference, a nonbeliever 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.

JEL-codes: B49 D03 D14 D83 G11 (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (22)

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Related works:
Journal Article: A MODEL OF NONBELIEF IN THE LAW OF LARGE NUMBERS (2016)
Working Paper: A Model of Non-Belief in the Law of Large Numbers (2013)
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Persistent link: https://EconPapers.repec.org/RePEc:oup:jeurec:v:14:y:2016:i:2:p:515-544.

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