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"Advanced technologies and biology have extremely different physical implementations, but they are far more alike in systems-level organization than is widely appreciated. Convergent evolution in both domains produces modular architectures that are composed of elaborate hierarchies of protocols and layers of feedback regulation, are driven by demand for robustness to uncertain environments, and use often imprecise components. This complexity may be largely hidden . . ."

Yes. Which means that even if the origins of designlike structure had been the aim of the article at issue here, the researchers would not have needed to show 'how complexity emerged - and a type of complexification (Kolmogorov, self-organizing, physical, functional, etc.)' and, in turn, 'how semiosis emerged (language, encoding, decoding) which in turn requires establishing how autonomy arose and information itself'. It's already well established that the sort of control/feedback behavior in question here arises in a quite broad range of underlying 'substrate' systems; they don't need to prove anything about the origin of the underlying system in order to justify the use of the modelling techniques.

RE: Your #114 above.

Excellent in terms of addressing the focus of the research in question, which is attuned to developing a model to interpret biological complexity, but I would like to make one additional comment.

If the researchers argue that "convergent evolution" leads to biological complexity, they do present a theoretical model to explain the origins of complexity. And I submit that this approach is fundamentally sound since it interprets whatever evidence is presented within a framework that is scientifically-justifiable since it does not ask that we retreat to mathematical probability, but rather to actually use the observed dynamics of biological systems as a guide when interpreting the evidence of observed phenomena.

I see that y'all had posted additional replies while I was composing the previous one. Strangely, my response to all of them is the same. Again I assert:

Correlation is not causation.

That is a huge error on the part of the researchers. By making a presumption of causation they opened the door for exactly what happened here.

A classic example used by PatrickHenry as I recall is the presence of storks when babies are born. That is correlation, not causation.

If all they intended was to illustrate the usefulness of reverse engineering, they should have stopped right there and declared cause unknown leaving the remainder of the research (complexity, semiosis, autonomy, information) to other investigators.