Without knowing the lady’s age, the DA reasoning produces a "rule" to convert the birthday ("n") into a maximum lifespan with 50% confidence ("N"). Gott's Copernicus method rule is simply: Prob ("N" < 2"n") = 50%. How accurate would this estimate turn out to be? Western demographics are now fairly uniform across ages, so a random birthday ("n") could be (very roughly) approximated by a U(0,"M"] draw where "M" is the maximum lifespan in the census. In this 'flat' model, everyone shares the same lifespan so "N" = "M". If "n" happens to be less than ("M")/2 then Gott's 2"n" estimate of "N" will be under "M", its true figure. The other half of the time 2"n" underestimates "M", and in this case (the one Caves highlights in his example) the subject will die before the 2"n" estimate is reached. In this 'flat demographics' model Gott's 50% confidence figure is proven right 50% of the time.