The Lindy effect is a theory that the future life expectancy of some non-perishable things like a technology or an idea is proportional to their current age, so that every additional period of survival implies a longer remaining life expectancy.
There are a some shallow criticisms you could make of it as a predictive tool. How do you define the thing? What's the proportion of age to life expectancy? How often do we update it?
But these are all problems of refining a loose claim into an unambiguous prediction. How would we evaluate whether this heuristic is a useful predictive tool?
Let's imagine that we're updating the Lindy effect predictions every year.
Bathing machines were roofed and walled wooden carts rolled to the beach to allow swimmers to wash and change in privacy. The earliest evidence of them is an engraving from 1735, and they were almost entirely out of use by the early 1920s. Their technological lifespan is around 185 years.
Let's be generous to the Lindy effect, and say that it needs to give a lifespan estimate that's within 25% of the true value to count as correct. In this case, it needed to predict that bathing machines would become obsolete sometime between 1874-1966.
People often use the Lindy Effect to predict that at any given time, we're halfway through the lifespan of anything that currently exists. Using that value, it generated correct predictions between 1805 and 1851, about 25% of the lifespan of the bathing machine. So across the lifetime of the bathing machine, the Lindy Effect was usually wrong.
To take this a step further, I wrote a Python script that finds the most successful Lindy Effect proportion. The code is in the comments. Here are my results.
- The most successful historical Lindy Effect predictions, using the 25% confidence interval, estimate that a thing will be around for 125% of its age in any given year.
- It will be right only 40% of the time throughout the lifespan of the thing.
- It will fail the year that the thing becomes obsolete.
Estimating that the bathing machine would be around for 125% of its present age would therefore have worked from 1846-1920.
What are the takeaways? Even interpreted very generously, the Lindy Effect is usually wrong. It's most successful when it estimates a thing has "one more phase of life" left; analogous to entering retirement. It's only accurate during the last 40% of the thing's actual lifespan.