First, I do think of systems biology as one of the main fields where the tools we’re developing should apply.
But I would not say that fields like artificial life or computational biology have done much to cross the theory-practice gap, mainly because they have little theory at all. Artificial life, at least what I’ve seen of it, is mostly just running various cool simulations without any generalizable theory at all. When there is “theory”, it’s usually e.g. someone vibing about the free energy principle, but it’s just vibing without any gears behind it. Levin’s work is very cool, but doesn’t seem to involve any theory beyond kinda vibing about communication and coordination. (Though of course all this could just be my own ignorance talking, please do let me know if there’s some substantive theory I haven’t heard of.)
Uri Alon’s book is probably the best example I know of actual theory in biology, but most of the specifics are too narrow for our purposes. Some of it is a useful example to keep in mind.
Let me drop some examples of “theory” or at least useful bits of information that I find interesting beyond the morphogenesis and free energy principle vibing. I agree with you that basic form of FEP is just another formalization of bayesian network passing formalised through KL-divergence and whilst interesting it doesn’t say that much about foundations. For Artificial Life, it is more a vibe check from having talked to people in the space, it seems to me they’ve got a bunch of thoughts about it but it seems like they’ve got some academic capture so it might be useful to at least talk to the researchers there about your work?
Like a randomly insultingly simple suggestion: Do a quick literature review through elicit in ActInf and Computational Biology for your open questions and see if there’s links, if there are send those people a quick message. I think a bunch of the theory is in people’s heads and if you nerdsnipe them they’re usually happy to give you the time of day.
Here’s some stuff that I think is theoretically cool as a quick sampler:
For Levin’s work:
In the link I posted above he talks about morphogenesis, the thing I find the most interesting there from an agent foundations and information processing perspective is the anti-fragility of systems with respect to information loss (similar to some of the stuff in Uri’s work if I’ve understood that correctly.) There are lots of variations of underlying genetics yet similar structures can be decoded through similar algorithms and it just shows a huge resillience there. It seems you probably know this from Uri’s work already
The reason why I find this very interesting is that it seems to me to be saying something fundamental about information processing systems from a limited observer perspective.
I haven’t gotten through the entire series yet but it is like a derivation of hierarchical agency or at least why a controller is needed from first principles.
I think this ACS post explains it better than I do below but here’s my attempt at it:
I’m trying to find the stuff I’ve seen on <<Boundaries>> within Active Inference yet it is spread out and not really centered. There’s this very interesting perspective of there only being model and modelled and that talking about agent foundations is a bit like taking the modeller as the foundational perspective whilst that is a model in itself. Some kind of computational intractability claims together with the above video series gets you to this place where we have a system of hierarchical agents and controllers in a system with each other. I have a hard time explaining it but it is like it points towards a fundamental symmetry perspective between an agent and it’s environment.
Other videos from Levin’s channel:
Agency at the very bottom—some category theory mathy stuff on agents and their fundamental properties: https://youtu.be/1tT0pFAE36c
The Collective Intelligence of Morphogenesis—if I remember correctly it goes through some theories around cognition of cells, there’s stuff about memory, cognitive lightcones etc. I at least found it interesting: https://youtu.be/JAQFO4g7UY8
Great question.
First, I do think of systems biology as one of the main fields where the tools we’re developing should apply.
But I would not say that fields like artificial life or computational biology have done much to cross the theory-practice gap, mainly because they have little theory at all. Artificial life, at least what I’ve seen of it, is mostly just running various cool simulations without any generalizable theory at all. When there is “theory”, it’s usually e.g. someone vibing about the free energy principle, but it’s just vibing without any gears behind it. Levin’s work is very cool, but doesn’t seem to involve any theory beyond kinda vibing about communication and coordination. (Though of course all this could just be my own ignorance talking, please do let me know if there’s some substantive theory I haven’t heard of.)
Uri Alon’s book is probably the best example I know of actual theory in biology, but most of the specifics are too narrow for our purposes. Some of it is a useful example to keep in mind.
Let me drop some examples of “theory” or at least useful bits of information that I find interesting beyond the morphogenesis and free energy principle vibing. I agree with you that basic form of FEP is just another formalization of bayesian network passing formalised through KL-divergence and whilst interesting it doesn’t say that much about foundations. For Artificial Life, it is more a vibe check from having talked to people in the space, it seems to me they’ve got a bunch of thoughts about it but it seems like they’ve got some academic capture so it might be useful to at least talk to the researchers there about your work?
Like a randomly insultingly simple suggestion: Do a quick literature review through elicit in ActInf and Computational Biology for your open questions and see if there’s links, if there are send those people a quick message. I think a bunch of the theory is in people’s heads and if you nerdsnipe them they’re usually happy to give you the time of day.
Here’s some stuff that I think is theoretically cool as a quick sampler:
For Levin’s work:
In the link I posted above he talks about morphogenesis, the thing I find the most interesting there from an agent foundations and information processing perspective is the anti-fragility of systems with respect to information loss (similar to some of the stuff in Uri’s work if I’ve understood that correctly.) There are lots of variations of underlying genetics yet similar structures can be decoded through similar algorithms and it just shows a huge resillience there. It seems you probably know this from Uri’s work already
Active Inference stuff:
Physics as information processing: https://youtu.be/RpOrRw4EhTo
The reason why I find this very interesting is that it seems to me to be saying something fundamental about information processing systems from a limited observer perspective.
I haven’t gotten through the entire series yet but it is like a derivation of hierarchical agency or at least why a controller is needed from first principles.
I think this ACS post explains it better than I do below but here’s my attempt at it:
I’m trying to find the stuff I’ve seen on <<Boundaries>> within Active Inference yet it is spread out and not really centered. There’s this very interesting perspective of there only being model and modelled and that talking about agent foundations is a bit like taking the modeller as the foundational perspective whilst that is a model in itself. Some kind of computational intractability claims together with the above video series gets you to this place where we have a system of hierarchical agents and controllers in a system with each other. I have a hard time explaining it but it is like it points towards a fundamental symmetry perspective between an agent and it’s environment.
Other videos from Levin’s channel:
Agency at the very bottom—some category theory mathy stuff on agents and their fundamental properties: https://youtu.be/1tT0pFAE36c
The Collective Intelligence of Morphogenesis—if I remember correctly it goes through some theories around cognition of cells, there’s stuff about memory, cognitive lightcones etc. I at least found it interesting: https://youtu.be/JAQFO4g7UY8
(I’ve got that book from URI on my reading list btw, reminded me of this book on Categorical systems theory, might be interesting: http://davidjaz.com/Papers/DynamicalBook.pdf)