A Model Of Nonbelief In The Law Of Large Numbers
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.
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Volume (Year): 14 (2016)
Issue (Month): 2 (April)
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- David M. Grether, 1980. "Bayes Rule as a Descriptive Model: The Representativeness Heuristic," The Quarterly Journal of Economics, Oxford University Press, vol. 95(3), pages 537-557.
- Matthew Rabin, 2002. "Inference by Believers in the Law of Small Numbers," The Quarterly Journal of Economics, Oxford University Press, vol. 117(3), pages 775-816.
- Shunichiro Sasaki & Toshiji Kawagoe, 2007. "Belief Updating in Individual and Social Learning: A Field Experiment on the Internet," ISER Discussion Paper 0690, Institute of Social and Economic Research, Osaka University.
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