To use your own example: You have to take a long time to walk a long distance. That doesn't mean you have to take a short time to walk a short distance.
You basically only know one thing about something, which is how long it has been around. The things that come to life and die that you observe will be, on average, in the middle of their life.
So if something has been around for five years, given no more information, your best bet is that it'll be around for another five years.
One year? Another one year.
Hopefully this explains well why the Lindy effect also says that something that's been around for a shorter period of time is more likely to disappear sooner.
You can see Notion come out, and go "wow, I find this good and I think it will keep being around and good for a while". Like the GP said, the fact that it is young doesn't mean it is doomed, or even that it is not good. The Lindy effect says nothing in that direction.
Sure, you can have a much richer model of the world than the Lindy model provides.
>fact that it is young doesn't mean it is doomed, or even that it is not good. The Lindy effect says nothing in that direction.
No; it says that young things are less likely to last than old things. If the average expectancy is small, sure it doesn't guarantee any one thing is doomed (some aren't) but it absolutely does tell you that most of them aren't going to last long.
Imagine a friend tells you that they know someone who is planning to become a professional rock star when they leave school.
They are probably not going to make it, because most people who try don't. You can't be sure, because some people do become rockstars. But it's not an error to be sceptical of their chances.
If you hear they are still gigging after 3 years, even if they haven't made it yet, you are probably a tiny bit less sceptical.
That all makes sense, right?
If you tell your rock star friend to go back to school, you are helping him minimize risk. The chances are that he won't make it, but that's not a consequence of the Lindy effect. It's because surviving in the rocking world is difficult as it requires that people pick your music repeatedly over other available bands, which is the requirement for the Lindy effect to apply, not its consequence.
More importantly, if you tell every rocker to stop and go back to school because their chances of succeeding are low, you end up with no rock bands. Because all rock bands have to start out being new.
Believing that most new rockers won't become big is fine. Believing that any rocker that is new won't make it is wrong, in fact you can be certain that some will.
We're not making absolute statements about either new or old things.
We're not saying any one new thing definitely won't last another 10 years, the same way we're not saying any one old thing will definitely last another 10 years.
The whole discussion is about what things are likely to do.
There's a place for formal logic in discussions of probabilistic models, but it can also confuse people.
> Believing that any rocker that is new won't make it is wrong, in fact you can be certain that some will.
If you were made to bet money repeatedly on whether a new rocker or an old rocker would still be a rocker in 5 years, you would end up with more money if you always picked the old rocker. This means that older rockers last relatively longer and new rockers last relatively shorter ("on average" / "by expectation" / "probabilistically"); that's literally the same statement.
That the underlying mechanism might be gradual removal doesn't change this, and doesn't matter.
I got the sense that the author is trying to say the latter, however what they literally said is more like the former: Visual Studio Code, specifically, is likely not to be around in 30 years. I can't argue with that.
Yes it is, in this context.
When you take into account more factors, then sure you can get a better prediction. But then it's not the Lindy effect anymore.
I'm talking about the Lindy effect only. Nothing else.
First sentence on the Wikipedia page:
> The Lindy effect (also known as Lindy's Law[1]) is a theorized phenomenon by which the future life expectancy of some non-perishable things, like a technology or an idea, is proportional to their current age.
Proportional.
So if we assume that's implicit in my example, how does the Wikipedia page on the contrapositive help now? (It doesn't, as my argument isn't relying on any property of it.)
I would read up on the topic if I were you. Statistics can be unintuitive sometimes, and basing your argument on supposition can be unreliable. I'm not sure about the correct answer here, but most folks get e.g. the Monty Hall problem incorrect if they rely on their intuition.
For a product at T = 0, why would it be optimal to assume its lifespan would be T = 0 as well, rather than the average age of all products?
No; not sure where you got that from.
>For a product at T = 0, why would it be optimal to assume its lifespan would be T = 0 as well, rather than the average age of all products?
I'm not saying it would be. (The original blog might, but that's irrelevant to my posts here.)
If you knew the average lifespan of all products, that would be your best estimate of the lifespan of a new product. (You can't use average age naively without thinking about right censoring.)
If the average lifespan of a product was short, then the average lifespan of a new product would be short. As the product aged, it's expected lifespan would increase.
I.e. the longer something has been around the longer it will be around, or, equivalently, the shorter it has been around the shorter it will be around (both obviously taking about expected times.) Make sense now?
But this is a stupid-obvious problem. The Lindy effect is easy to write out in formalisms:
age(x) > age(y) ⟶ life_expectancy(x) > life_expectancy(y)
age(x) < age(y) ⟶ life_expectancy(x) < life_expectancy(y)
age(y) < age(x) ⟶ life_expectancy(y) < life_expectancy(x)