I was under the impression that his conclusions come from the same sort of papers we're discussing in the OP, and that the animation is just a demonstration of the effect. Some real-life issues should be addressed in future studies, for sure; for airplanes, it's probably important to model "families" that sit, board, and deplane together. But just listing the variables not accounted for in the study or video isn't scientific criticism, you have to do your own work to actually demonstrate that it makes a difference.

You are correct. But his videos aren't presented as "this is possibly the best way, and it should be further investigated." They're presented as "This is the best way because I say so, even though I haven't done any testing but I'm a college professor and on YouTube so you should believe me."

They're presented more like "This is the best way", the rest of your characterization is unfair.

This doesn't invalidate your core criticism, but there's no need to accuse him of trying to impose authority.

I've watched a lot of CGP grey and I just learned now, from you, that he's a college professor. I may have missed or forgotten it being mentioned a couple times, but I'm sure it's not front and center as you imply.

Being on YouTube is, tautologically, obvious from being on YouTube, but it's also necessarily obvious from being on YouTube, and a similar accusation of appeal to authority could be constructed for any other medium, e.g."it's best because I say so and I have a blog".

> Similarly, his plane boarding simulation falls down as soon as someone shows up late, or in a wheelchair, or with a bum leg, drunk, a baby, luggage that the wheel has fallen off of, etc...

All of these things occur in non-ideal boarding scenarios as well. I don't see how they affect his solution more so than what we currently have.

It’s sort of like comparing two algorithms and using the one that has the simplest best case time complexity without considering the average case or that your particular real-world use might actually be a worst case.

But ultimately the same problems affect both algorithms in the same way. So the best case algorithm will be slower in the real world, but should still be faster than the other cases.

Only if all algorithms are affected the same way by the same external factors, which simply doesn't happen.

It's like when a Toyota and a Freightliner crash into the same wall. They both crashed at the same speed, why is one damaged more than the other?

You can come up with endless scenarios to justify it, but the fact is you can't design something that will deal with all of them. So you design for the typical scenario.

Maybe there is an algorithm that boards a plane faster when there are 2 handicapped people, 3 drunks, and 1 person being late. But how typical is that? And is it realistic to design the whole algorithm around that? I would argue it isn't.

Real flights have those things in some numbers and a model probably ought to attempt to account for them, since, as you noted in a separate post I can’t seem to find now, they’ll affect the model negatively. The goal isn’t to find the best number of late people and wheelchairs. It’s to be able to board real flights as quickly as possible and every real flight I’ve ever seen, except the flight I took shortly after 9/11, has had some number of strollers, wheelchairs, or what have you.

I don't think we can make that claim. There is no reason to believe the problems will affect both algorithms the same way. Some may be more fragile or failure resistant to different failure modes.

Except this is not true. Quicksort is typically faster than mergesort, but worst case it can be very much slower. The worst case does not affect both algorithms the same way.

But we are not talking about quicksort or mergesort, either way it helps prove my point. Typically faster is an interesting way of describing something. What is typical? 80% of the time?

Your point is that all algorithms respond the same way to inputs, and a counterexample helps prove it?

That wasn't my point - we are talking about a specific scenario here, not "all"

An algorithm sees its best time complexity over some percentage of the input space. Over some other percentage it sees its worst time complexity. And, on average over the entire input space, it sees its average time complexity. The algorithms are necessarily different in what those best, average and worst are, and importantly, over what parts of the input space those categories apply. If they weren't, we'd call the algorithms mathematically equivalent and it wouldn't matter which one you chose. And if they all handled 'problem' data equally, we'd see that they all have the same performance penalty between best and average and average and worst. They don't. Sometimes best and average are equal. Sometimes worst and average are equal. Sometimes all three are equal.

If your data is truly random, you should pick the algorithm with the best average time complexity. Is your data random? Almost certainly not. It might be 'random enough', or it might not be. A real world data set may fall completely within the best case of algorithm A and the worst case of algorithm B, even if the average case of B is better than the average case of A.

In this case, the data points that are most likely to negatively affect an algorithm are specifically being excluded from the model. Far from comparing even the average case, we're comparing best cases. We aren't using a 'real world' data set. So it's an interesting simulation, but it isn't enough to reach any conclusions at all.