A Model of Non-Belief in the Law of Large Numbers
Daniel J. Benjamin
Cornell University - Department of Economics; National Bureau of Economic Research (NBER)
University of California, Berkeley - Department of Economics
University of Michigan at Ann Arbor - Department of Economics
October 27, 2012
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.
Number of Pages in PDF File: 106
Keywords: under-inference, non-Bayesian updating, conservatism bias, representativeness heuristic
JEL Classification: B49, D03, D14, D83, G11working papers series
Date posted: October 19, 2011 ; Last revised: October 30, 2012
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