I think the author does a pretty good job of highlighting that there are imperfections and shortcomings while eliding over those details. They may have slipped up in some places such as your quote but I'm definitely not concerned of anyone taking any of this too seriously and extrapolating on it dangerously.
> I'm definitely not concerned of anyone taking any of this too seriously and extrapolating on it dangerously.
Not this particular article, no. But there's a whole genre of excited mathematical modelling literature where the author demonstrates a gee-whizz concept that looks like it could be really useful. The trouble is that once you start digging down into the specific details, they turn out to be really hard to get right, and at best you end up with a model that's brittle, for want of a better word.
An example that I have in mind is the literature on power law distributions. A little bit of theory showed how power law distrbutions could arise via a process known as preferential attachment, and everyone got excited and suddenly people were spotting them everywhere. The literature on this topic is massive.
The thing is, it turns out that it's quite hard to check that a given dataset follows a power law. This paper [0] showed that many of the claims were sloppy, and the researchers hadn't been careful with their statistics.
The crux of what I'm saying is that establishing that a model fits well is hard, whether it's a diffusion model, a power law distribution, or anything else. If someone wants to claim that some mathematical widget can be used to model X, they'd better be able to back that claim up with a real demonstration and carefully laid-out details. Otherwise they're just waving their hands in the air.
