Doomsday Argument
Summary | The Doomsday argument is a family of arguments about humanity’s likely survival. There are mainly two versions of the argument discussed in the literature, both of which appeal to a form of Copernican principle (or principle of typicality or mediocrity). A first version of the argument (endorsed by, e.g., John Leslie) dictates a probability shift in favor of theories that predict earlier end dates for our species assuming that we are a typical—rather than atypical—member of that group. The other main version of the argument is sometimes referred to as the ‘delta-t argument,’ and it has provoked both outrage and genuine scientific interest. It claims to allow one to make a prediction about the total duration of any process of indefinite duration based only on the assumption that the moment of observation is randomly selected. A variant of this argument, which gives equivalent predictions, reasons in terms of one’s rank in a sequential process. |
Key works | An early version of the Doomsday argument, referred to as the 'Carter catastrophe', appeared in Carter & McCrea 1983. The Doomsday argument was then popularized by John Leslie 1990. The 'delta-t argument' was put forth by Richard Gott (1993, 1994). Several attempts to block the conclusions of this argument have been offered. In order to counter the consequence of what he called the 'self-sampling assumption,' Nick Bostrom 2002 suggested to adopt the 'self-indicating assumption.' In order to avoid conclusions that are entirely dependent on our reference class, Radford Neal 2006 argued that we ought to appeal to fully non-indexical conditioning to block the conclusions of the Doomsday argument. Benétreau-Dupin argued that only imprecise probabilities can avoid the conclusions of all versions of the Doomsday argument. |
Introductions | See Bostrom 2002 (§6-7) and Richmond 2006 for reviews. See Monton & Roush ms for Gott's version of the Doomsday argument. |
- Monty Hall Problem (30)
- New Riddle of Induction (231)
- Paradox of Confirmation (104)
- Sleeping Beauty (135)
- Probabilistic Puzzles, Misc (71)
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