This is called modelling error. Both Bayesian and frequentist approaches suffer from modelling error. That's what TFA talks about when mentioning the normality assumptions behind the paper's GLM. Moreover, if errors are additive, certain distributions combine together easily algebraically meaning it's easy to "marginalize" over them as a single error term. In most GLMs, there's a normally distributed error term meant to marginalize over multiple i.i.d normally distributed error terms.

> Plenty of downvotes and comments, but nothing addressing the point of the argument might suggest something.

I don't understand the point of your argument. Please clarify it.

> Here’s the experiment and here’s the data is concrete it may be bogus but it’s information. Updating probabilistic based on recursive estimates of probabilities is largely restating your assumptions.

What does this mean, concretely? Run me through an example of the problem you're bringing up. Are you saying that posterior-predictive distributions are "bogus" because they're based on prior distributions? Why? They're just based on the application of Bayes Law.

> Black swans can really throw a wrench into things

A "black swan" as Taleb states is a tail event, and this sort of analysis is definitely performed (see: https://en.wikipedia.org/wiki/Extreme_value_theory). In the case of Bayesian stats, you're specifically calculating the entire posterior distribution of the data. Tail events are visible in the tails of the posterior predictive distribution (and thus calculable) and should be able to tell you what the consequences are for a misprediction.