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A Model of Non-Belief in the Law of Large Numbers
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A Model of Non-Belief in the Law of Large Numbers
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Paper
A Model of Non-Belief in the Law of Large Numbers
2013
Overview
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.
Publisher
Federal Reserve Bank of St. Louis
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