J. Richard Gott III (1993) has used the “Copernican principle” to derive a probability density function for the total longevity of any phenomenon, based solely on the phenomenon’s past longevity. John Leslie (1996) and others have used an apparently similar probabilistic argument, the “Doomsday Argument,” to claim that conventional predictions of longevity must be adjusted, based on Bayes’ Theorem, in favor of shorter longevities. Here I show that Gott’s arguments are flawed and contradictory, but that one of his conclusions—his delta t formula—is mathematically equivalent to Laplace’s famous (and notorious) ‘rule of succession’; moreover, Gott’s delta t formula is a plausible worst-case (if one favors greater longevity) bound in some contexts. On the other hand, the Doomsday Argument is fallacious: the argument’s Bayesian formalism is stated in terms of total duration, but all attempted real-life applications of the argument—with one exception, an application by Gott 1994—actually plug in prior probabilities for future duration; moreover, the Self-Sampling Assumption, an essential premise of the Doomsday Argument, is contradicted by the prior information in all known real-life cases. But rejecting the Doomsday Argument does not entail rejecting the possibility of learning about the future from the past. Applying the work of Bruce M. Hill (1968, 1988, 1993) and Frank P.A. Coolen (1998, 2006) in the field of non-parametric predictive inference, I propose and defend an alternative methodology for quantifying how past longevity of any phenomenon does provide evidence for future longevity. In so doing, I identify an objective standard by which to choose among counting time intervals, counting population, or counting any other measure of past longevity in predicting future longevity. This methodology forms the basis of a calculus of induction.
|Keywords||Induction non-parametric predictive inference Doomsday Argument Self-Sampling Assumption Copernican principle Anthropic principle|
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