A Model of Non-Belief in the Law of Large Numbers

Collin Raymond, Daniel Benjamin and Matthew Rabin

No 672, Economics Series Working Papers from University of Oxford, Department of Economics

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.

Keywords: learning; non-Bayesian updating; behavioral economics; information economics (search for similar items in EconPapers)
JEL-codes: B49 D03 D14 D83 G11 (search for similar items in EconPapers)
Date: 2013-09-17
New Economics Papers: this item is included in nep-cbe, nep-evo and nep-mic
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Citations: View citations in EconPapers (2)

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Related works:
Journal Article: A MODEL OF NONBELIEF IN THE LAW OF LARGE NUMBERS (2016)
Journal Article: A Model of Nonbelief in the Law of Large Numbers (2016)
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