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

106 Pages Posted: 19 Oct 2011 Last revised: 30 Oct 2012

See all articles by Daniel J. Benjamin

Daniel J. Benjamin

USC, Center for Economic and Social Research (CESR); National Bureau of Economic Research (NBER)

Matthew Rabin

University of California, Berkeley - Department of Economics

Collin Raymond

University of Michigan at Ann Arbor - Department of Economics

Date Written: October 27, 2012

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 di fferent 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: under-inference, non-Bayesian updating, conservatism bias, representativeness heuristic

JEL Classification: B49, D03, D14, D83, G11

Suggested Citation

Benjamin, Daniel J. and Rabin, Matthew and Raymond, Collin, A Model of Non-Belief in the Law of Large Numbers (October 27, 2012). Available at SSRN: https://ssrn.com/abstract=1945916 or http://dx.doi.org/10.2139/ssrn.1945916

Daniel J. Benjamin (Contact Author)

USC, Center for Economic and Social Research (CESR) ( email )

635 Downey Way
Los Angeles, CA 90089-3332
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National Bureau of Economic Research (NBER) ( email )

1050 Massachusetts Avenue
Cambridge, MA 02138
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Matthew Rabin

University of California, Berkeley - Department of Economics ( email )

549 Evans Hall #3880
Berkeley, CA 94720-3880
United States

Collin Raymond

University of Michigan at Ann Arbor - Department of Economics ( email )

611 Tappan Street
Ann Arbor, MI 48109-1220
United States

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