For the perishable, every additional day in its life translates into a shorter additional life expectancy. For the nonperishable, every additional day implies a longer life expectancy.
So the longer a technology lives, the longer it is expected to live. Let me illustrate the point. Say I have for sole information about a gentleman that he is 40 years old and I want to predict how long he will live. I can look at actuarial tables and find his age-adjusted life expectancy as used by insurance companies. The table will predict that he has an extra 44 to go. Next year, when he turns 41 (or, equivalently, if apply the reasoning today to another person currently 41), he will have a little more than 43 years to go. So every year that lapses reduces his life expectancy by about a year (actually, a little less than a year, so if his life expectancy at birth is 80, his life expectancy at 80 will not be zero, but another decade or so).
The opposite applies to nonperishable items. I am simplifying numbers here for clarity. If a book has been in print for forty years, I can expect it to be in print for another forty years. But, and that is the main difference, if it survives another decade, then it will be expected to be in print another fifty years. This, simply, as a rule, tells you why things that have been around for a long time are not “aging” like persons, but “aging” in reverse. Every year that passes without extinction doubles the additional life expectancy. This is an indicator of some robustness. The robustness of an item is proportional to its life!
This is the “winner-take-all” effect in longevity.
The main argument against this idea is the counterexample — newspapers and traditional telephone lines come to mind. These technologies, widely considered inefficient and dying, have been around for a long time. Yet the Copernican Principle would suggest they will continue to live on for a long time.
These arguments miss the point of probability. The argument is not about a specific example, but rather about the life expectancy, which is, Taleb writes “simply a probabilistically derived average.”
Perhaps an example, from Taleb, will help illustrate. If I were to ask you to guess the life expectancy of the average 40 year old man, you would probably guess around 80 (at least that’s what the actuarial tables likely reveal). However, if I now add that the man is suffering from cancer, we would revisit our decision and most likely revise our estimate downward. “It would,” Taleb writes, “be a mistake to think that he has forty four more years to live, like others in his age group who are cancer-free.”
“In general, the older the technology, not only the longer it is expected to last, but the more certainty I can attach to such statement.”
If you liked this, you’ll love these three other Farnam Street articles:
The Copernican Principle: How To Predict Everything — Based on one of the most famous and successful prediction methods in the history of science.
Ten Commandments for Aspiring Superforecasters — The ten key themes that have been “experimentally demonstrated to boost accuracy” in the real-world.
Philip Tetlock on The Art and Science of Prediction — How we can get better at the art and science of prediction, including diving into makes some people better at making predictions and how we can learn to improve our ability to guess the future.