A model of non-belief 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...
| Main Authors: | , , |
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| Format: | Working paper |
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University of Oxford
2013
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| _version_ | 1826274177069350912 |
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| author | Raymond, C Benjamin, D Rabin, M |
| author_facet | Raymond, C Benjamin, D Rabin, M |
| author_sort | Raymond, C |
| collection | OXFORD |
| description | 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. |
| first_indexed | 2024-03-06T22:39:28Z |
| format | Working paper |
| id | oxford-uuid:5b0ae027-21de-47d9-893d-2f89510c9fd8 |
| institution | University of Oxford |
| last_indexed | 2024-03-06T22:39:28Z |
| publishDate | 2013 |
| publisher | University of Oxford |
| record_format | dspace |
| spelling | oxford-uuid:5b0ae027-21de-47d9-893d-2f89510c9fd82022-03-26T17:19:39ZA model of non-belief in the law of large numbersWorking paperhttp://purl.org/coar/resource_type/c_8042uuid:5b0ae027-21de-47d9-893d-2f89510c9fd8Bulk import via SwordSymplectic ElementsUniversity of Oxford2013Raymond, CBenjamin, DRabin, MPeople 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. |
| spellingShingle | Raymond, C Benjamin, D Rabin, M A model of non-belief in the law of large numbers |
| title | A model of non-belief in the law of large numbers |
| title_full | A model of non-belief in the law of large numbers |
| title_fullStr | A model of non-belief in the law of large numbers |
| title_full_unstemmed | A model of non-belief in the law of large numbers |
| title_short | A model of non-belief in the law of large numbers |
| title_sort | model of non belief in the law of large numbers |
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