Without doing the math to check, nothing that you said seems wrong. However, I take a very different lesson from the idea of the Lindy Effect than you do. Specifically, the Lindy Effect tells us that when making predictions about future lifetimes of non-perishable things, we should assume a power law distribution. If you’ve never dealt with predictions under thick-tail assumptions, you might be surprised how little intuition you will have for it. (The 80-20 rule is another example of assuming thick-tails.)
For example, a Pareto distribution (the easiest of the power law distributions) will typically have a mean larger than the median, and the mode (most common single outcome) is essentially instant failure. If the constant of proportionality in the Lindy Effect is greater than one, this implies an infinite variance even with a finite mean. Also, the force of mortality (instantaneous rate of death, aka hazard function) is a decreasing function of time.
The reason that this is typically a good rule of thumb for making future estimates is a consequence of making predictive distributions (i.e. integrating out uncertainty in a fit parameter). As the most familiar example, remember that even if the maker of the Matrix came down and told that a random variable was normally distributed, you will still need to estimate the parameters from observation. This will lead to your mean estimates being distributed as a Student-t, which is a power law.
If I’m reading you right (low confidence), then I think our lessons are compatible. The longer something’s been around, the longer we should expect it to continue, in absolute terms. At the same time, our best outside view guess is always that the thing is getting toward the end of its life, in relative terms.
I notice the Lindy Effect getting tossed out often as a counterargument to an inside view. So for example, if John says “the Catholic church is on its last legs,” Alice might say “it’s been around for almost two millennia, so the Lindy Effect suggests it’ll probably be around for a long time to come.”
I think the way to synthesize their approaches is to start with Alice’s point of view, then modify it with John’s. And this makes perfect sense. If you told me that X has been around for 2,000 years, then . without knowing what X is, I’d feel pretty confident that it’s not going to disappear tomorrow. But I’d also want to know what X is, so I can modify my expectations accordingly.
The Lindy Effect makes a little less intuitive sense when X is only a few seconds old. But that’s because I can’t stop my imagination from filling in what X might be by imagining the social circumstances. Anything you can tell me is 2 seconds old is probably something you made, and it’s probably an object. Most objects don’t self-destruct seconds after they were manufactured.
More generally, anything that requires work to make requires an input of energy. That means evolution’s fighting entropy for it, and probably wouldn’t invest in it if it was likely to be fragile. Anything the living make has a life expectancy in proportion to the energy it took to build it.
But likewise, the more energy it takes to make a thing, the smaller a fraction of the total output of things per unit time. If the Lindy Effect doesn’t seem intuitive, that’s because we’re so used to paying attention to big, old things that we don’t think to use the countless small and temporary things as examples.
Without doing the math to check, nothing that you said seems wrong. However, I take a very different lesson from the idea of the Lindy Effect than you do. Specifically, the Lindy Effect tells us that when making predictions about future lifetimes of non-perishable things, we should assume a power law distribution. If you’ve never dealt with predictions under thick-tail assumptions, you might be surprised how little intuition you will have for it. (The 80-20 rule is another example of assuming thick-tails.)
For example, a Pareto distribution (the easiest of the power law distributions) will typically have a mean larger than the median, and the mode (most common single outcome) is essentially instant failure. If the constant of proportionality in the Lindy Effect is greater than one, this implies an infinite variance even with a finite mean. Also, the force of mortality (instantaneous rate of death, aka hazard function) is a decreasing function of time.
The reason that this is typically a good rule of thumb for making future estimates is a consequence of making predictive distributions (i.e. integrating out uncertainty in a fit parameter). As the most familiar example, remember that even if the maker of the Matrix came down and told that a random variable was normally distributed, you will still need to estimate the parameters from observation. This will lead to your mean estimates being distributed as a Student-t, which is a power law.
If I’m reading you right (low confidence), then I think our lessons are compatible. The longer something’s been around, the longer we should expect it to continue, in absolute terms. At the same time, our best outside view guess is always that the thing is getting toward the end of its life, in relative terms.
I notice the Lindy Effect getting tossed out often as a counterargument to an inside view. So for example, if John says “the Catholic church is on its last legs,” Alice might say “it’s been around for almost two millennia, so the Lindy Effect suggests it’ll probably be around for a long time to come.”
I think the way to synthesize their approaches is to start with Alice’s point of view, then modify it with John’s. And this makes perfect sense. If you told me that X has been around for 2,000 years, then . without knowing what X is, I’d feel pretty confident that it’s not going to disappear tomorrow. But I’d also want to know what X is, so I can modify my expectations accordingly.
The Lindy Effect makes a little less intuitive sense when X is only a few seconds old. But that’s because I can’t stop my imagination from filling in what X might be by imagining the social circumstances. Anything you can tell me is 2 seconds old is probably something you made, and it’s probably an object. Most objects don’t self-destruct seconds after they were manufactured.
More generally, anything that requires work to make requires an input of energy. That means evolution’s fighting entropy for it, and probably wouldn’t invest in it if it was likely to be fragile. Anything the living make has a life expectancy in proportion to the energy it took to build it.
But likewise, the more energy it takes to make a thing, the smaller a fraction of the total output of things per unit time. If the Lindy Effect doesn’t seem intuitive, that’s because we’re so used to paying attention to big, old things that we don’t think to use the countless small and temporary things as examples.