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A MODEL OF NONBELIEF IN THE LAW OF LARGE NUMBERS.

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. 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.

SUBMITTER: Benjamin DJ

PROVIDER: S-EPMC4833119 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature

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A MODEL OF NONBELIEF IN THE LAW OF LARGE NUMBERS.

Benjamin Daniel J DJ Rabin Matthew M Raymond Collin C

Journal of the European Economic Association 20150604 2

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. In inference, a non-believer remains uncertain and influenced by priors even after observing a ...[more]

PMID: 27087795

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