In technical terms, there is no need for a prior on theory space:
https://arxiv.org/abs/1801.02176
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5.2 Occam’s Razor
A probability distribution from which to calculate the most likely choice of parameter adds unnecessary structure to the theory and is thus in conflict with the dictum of simplicity. We could have chosen a parameter and be done with it. The probability distribution and all the not-observed values of the parameters are unnecessary for the derivation of any observable and they should therefore be stripped by Occam’s razor.
However, I don't think disliking parsimony is that radical. Andrew Gelman is not a fan of parsimony.
http://andrewgelman.com/2004/12/10/against_parsimo/
My current view is that parsimony is a heuristic you can use, but weak evidence at best. Plus, parsimony is not unambiguously defined, so comparing hypotheses in terms of it can still be subjective even with objective criteria as there is no agreement on which approach is best. It's most justified to say that extremely complex models are unlikely if the complexity is not necessary.
I read the article, and I don't see any insightful answers, perhaps we're supposed to buy the book? Or perhaps people should devise stricter criteria for theories to be experimentally demonstrable...