A MODEL OF NONBELIEF IN THE LAW OF LARGE NUMBERS

• 成果类型: Article
• 署名作者: Benjamin, Daniel J.; Rabin, Matthew; Raymond, Collin
• 署名单位: Cornell University; University of Southern California; Harvard University; University of Oxford
• 刊物名称: JOURNAL OF THE EUROPEAN ECONOMIC ASSOCIATION
• ISSN/ISSBN: 1542-4766
• DOI: 10.1111/jeea.12139
• 发表日期: 2016
• 页码: 515-544
• 关键词: repeated gambles; risk-aversion; choices; probability; calibration; preferences; uncertainty; decisions; BEHAVIOR

摘要

People believe that, even in very large samples, proportions of binary signals might depart significantly from the population mean. We model this nonbelief 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 nonbeliever expects the distribution of signals will have fat tails. In inference, a nonbeliever 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.