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Modelling uncertainty is definitely a useful tool to have, but I'm not sure why the author expects there to be a "scientific" (a.k.a. mechanistic) way of doing it.

In normal programming, there's no fool-proof formula for picking the best data structure or the best algorithm. If there were, we could just write one program to write all other programs and be done with it!

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I’m definitely not advocating for an automated way of building these models, for the reasons you point out. Instead, I’m saying more that probabilistic models are not taught in a way that we can easily map our knowledge structures onto them, so I’m advocating for more explicit guidance in that process.

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