Well, you know it's a common notion in statistics that "All models are wrong, but some are useful". This means no model will ever capture reality as is, but we can make sure the model is good enough to be useful for the particular application. This is possible because we can actually quantify uncertainty about prior information, estimates and predictions (e.g. through credible or confidence intervals) and make sure models are as exact and as complex as needed.

Funnily, we can predict things quite well, especially when it comes to large numbers of people (individuals are the hard stuff). Like how social background influences educational levels for a population, how lifestyle will influence average health, how climate change may affect frequency of extreme weather, even what people may want to write on their smartphones is predicted with these kind of models.

We can predict the future, with a degree of confidence. The statistical models used in this un peer reviewed paper have been peer reviewed. The math they used is sound because it's been peer reviewed and deemed appropriate and accurate enough. I'm sure it will be reviewed in the future, Vice or whoever just wants to get a jump on a good article.