I mentioned in my review of Anatoly Levenchuk’s “Systems Thinking 2020” having some subsequent dialogue about common ground in other areas of the Psybertron agenda. A significant overlap is the work of Karl Friston (Free Energy Principle / Markov Blankets / Emergent Organism / Active Inference) in my reading of Mark Solms, and in Levenchuk’s case, where he and Friston are both members of the advisory board of “The Active Inference Lab”.
[Small world in itself – and yet in the days since, the concept of “systems thinking” is everywhere, from politics and biology, to consciousness and metaphysics. This is not going away. It was xxxx noticed back in January? I’d slipped into systems language quite naturally into ongoing dialogues. And, I made quite a thing of “systems architecture” considerations when interpreting both Solms and McGilchrist (independent) work in terms of (say) anatomical and functional brain architecture.]
In the dialogue above, Levenchuk shared a paper appearing to cast doubt on Friston’s use of Markov Blankets – “The Emperor’s New Markov Blankets” – ENMB (2021) for short here. Full refs:
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- Bruineberg, Jelle and Dolega, Krzysztof and Dewhurst, Joe and Baltieri, Manuel (2021) The Emperor’s New Markov Blankets. Behavioral and Brain Sciences 1-63. [Preprint] doi:10.1017/S0140525X21002351
or
- Bruineberg, Jelle and Dolega, Krzysztof and Dewhurst, Joe and Baltieri, Manuel (2020) The Emperor’s New Markov Blankets. PhilSciArchive [Preprint]
(More dialogue below in the Post Notes.)
START
It’s a substantial paper, 48 pages with some pretty heavy maths as well as arguments of principle. In fact when I read the parts of Solms where, amongst other things, he used (Freudian) mathematical notation additionally developed with Friston, I noted that it was perfectly possible I wasn’t properly understanding Friston’s arguments. Whilst it chimed intuitively with my own understandings, I wasn’t well placed to say whether it was formally right, one way or another – an occupational hazard in this kind of multi-disciplinary research.
“I’ve also kept in [my reviews] lots of technical specifics which I probably don’t understand as Solms intended, primarily to allow me later checking against other resources” (Myself, earlier.)
Well here is an opportunity 🙂 to respond to the “ENMB Paper” quoted directly below:
“This web of formalisms (Free Energy Principle, Markov Blankets, Active Inference) is developing at an impressively fast pace and the constructs it describes are often assigned a slightly unconventional meaning whose full implications are not always obvious. While this might ironically explain some of its appeal, as it can seem to the layperson to be steeped in unassailable mathematical justification …”
“We will argue that although this approach might have interesting philosophical consequences, it is dependent upon additional metaphysical assumptions that are not themselves contained within the Markov blanket construct.”
“In our view the FEP literature consistently fails to clearly distinguish between the ‘map’ (a representation of reality) and the ‘territory’ (reality itself). This slippage becomes most apparent in their treatment of the concept of a Markov blanket.”
“… a broader tendency within the FEP literature, in which mathematical abstractions are treated as worldly entities with causal powers.”
“[Friston’s is] a new and largely independent theoretical construct that is more closely aligned with notions of sensorimotor loops and agent-environment boundaries.”
“Inference within a model, as opposed to inference with a model, seeks to understand inference as it is physically implemented in a system, and places literal Markov blankets at the boundary between the system and its environment. The ‘model’ within which these Markov blankets are used is usually understood ontologically: here the map is the territory – the system performing inference is itself a model of its environment, and its boundary is demarcated by Markov blankets.”
“This procedure of attributing to the territory (the dynamical system) what is a property of the map (the Bayesian network) is a clear example of the reification fallacy: treating something abstract as something concrete (without any further justification) … we propose to distinguish between ‘Pearl blankets’ to refer to the standard ‘epistemic’ use of Markov blankets and ‘Friston Blankets’ to refer to this new ‘metaphysical’ construct. While Pearl blankets are unambiguously part of the map (i.e., the graphical model), Friston blankets are best understood as parts of the territory (i.e., the system being studied).”
“As a general rule, one should not mistake the map described by a model for the territory it is describing: a model of the sun is not itself hot, a model of an organism is not itself alive, and so on.”
[ENMB Paper]
OK, so again, without going through any of the mathematical rigour – itself unassailable by me – an important issue is indeed covered by the extract above, that there are metaphysical (ontological) premises possibly unstated in the work of Friston (and Solms), that might “appear to break this general rule without any further justification”. However these are quite explicit here.
Solms’ own response is categorical without any further metaphysical justification.
I have read [the paper]. I don’t think the Markov blanket formalism is a map of a territory but a description of the causal dynamics that actually exist in a territory. The territory in question is the (monist) functional organization of both brain and mind.
The ‘territories’ are the observable mental and neural phenomena. What they are calling the ‘map’ is, for me, the underlying functional system that explains those phenomena. This explanatory level (the functional organization of the system) cannot be observed; it must be inferred.
It is a dualist position. The formalism describes the actually causal ontology. As Galileo said: the book of Nature is written in the language of mathematics.
(Solms in Twitter exchange.)
I don’t buy the Galilean / Platonic argument as definitive, but it reinforces that this is not an accidental error in this school of work, but a deliberate act that needs to be understood as such. Sure the “book” of nature may be written in maths, but maybe not “nature” itself?
Good question, from any small boy not seeing the emperor’s clothes.
But it’s not necessary to analyse exactly what Galileo, or Plato before him, was asserting in any specific detail. That general rule of not confusing the map with the territory – not falling for the reification fallacy – is good advice and indeed is ancient advice. A Buddhist might point out that “the finger pointing at the moon is not the moon”. In science generally, our models in mathematical constructs used to represent, analyse and predict data about the real world are contingent approximations to the behaviour of that real world, but they are not it. Physics isn’t the real world, it’s our best current model of it. In any number of more mundane engineering applications, especially those that get implemented in analytical and operational computing applications, we constantly have to remind ourselves that the model is only a model, not the real thing, however seductive the virtual reality might be.
Dennett (much cited here) is among those acknowledged as providing advice to the ENMB paper, without any specific reference and, given his views on disembodied information and computation (**) – independent of any physical layer – in his own “evolved consciousness” story, I’d be interested to know his actual views on this argument. I’m pretty certain he uses very similar arguments to Friston and Solms, as I do too.
What we are saying is that in this model, the computation (**), the sensing of information and algorithmic processes of the systems and subsystem components, with and without Markov-blankets, is quite literally happening. These information entities and processes are more fundamental than the physical models which self-organise and emerge from them. This is indeed a metaphysical claim, whether or not explicitly stated as such by every user.
In these theories, in my own metaphysics as well as Solms says above, the information processing (**) is the territory, the foundation of the territory itself not just a map of it. Though obviously like any model we have also plenty of other information representations used to describe and present (map) the model and its processes to human audiences.
Friston and Solms (and myself) are not unique here, as the ENMB paper acknowledges, there are many philosophers and cognitive scientists with information-and-computation-based ontologies of reality. Integrated Information Theory (IIT after G Tononi) is one well developed example, but these are part of a wider movement. One corollary of these foundational (metaphysical) information-based ontologies is that both the physical (body) and mental (mind) worlds and their causal relations are explained by the same underlying metaphysics. A credible monism where dualism has stubbornly continued to exist. (Also a lot of new interest in various versions of pan-psychism in the 21st C, and again these theories provide an information-based “pan-proto-psychism” that may better support these.)
In many ways it’s good that the ENMB paper exists, because it is ringing an important alarm bell that more people in both science and philosophy should wake-up to how radically important these not-so-new theories are.
Thanks for the warning ENMB, but what you are describing is exactly what we’re doing.
END
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In a similar critical vein to the ENMB Paper, this newer one on FEP
(**) And Yogi Yaeger’s paper. “Natural Information Processing” as opposed to formal “Computation”. Not be confused – the formal term “computation” associated with the “computability” of Church, Turing, Shannon et al and the natural language term for “processing information”.
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Post Notes:
One source here on Psybertron that I’ve not really developed yet is John C. Doyle, a control systems guru I’ve mentioned being impressed with before – he’s written the text-books and is much cited in papers – but he hasn’t written for a generalist public so he’s quite low profile if it’s not your field. He’s very much a systems thinker looking for architectural abstractions yet using very real-life examples to illustrate. In this 30 minute talk – very dense / terse / rushed, packed with content easy to miss if you’re not concentrating – the last 15 minutes is very interesting. Very clearly joining up issues of multi-layer systems optimisation and evolution (Levenchuk) with human situational awareness and responses based around the visual field and the speed of saccade eye-movements (Solms)?
Anatoly Levenchuk Comments:
2. There are not only “map-territory” distinction and representation relation that may be confusing. There are functional object — physical object distinction with implementation/realization relation. And you should decide: what type of relation each of authors mentions.
IG: Ah, yes. Not always explicit in every discussion, but pretty fundamental that the systems / architecture view is functional – you maybe saw my comments on trying to get a Brain Atlas that held to this schematic view. See later comments on process-based relations.
3. A day ago I did post about phys-math-modeling and compactification/universalization of knowledge. It suggest more long chain of ontology modeling: physical object from domain that is classified/annotated by type of physical object (ToPO) from physics textbook and then this ToPO is represented (or classified/annotated, if you prefer it) by mathematics/abstract object. I am not mention about functional object option here, it is enough complicated with this. Mathematics is foundation and upper ontology, physics is middle ontology, domain objects is working ontology.
Thus you can parse phases like “As the locus of molecular, thermodynamic, and bioelectric exchange with the environment, the cell membrane implements a Markov Blanket (MB) that renders its interior сonditionally independent of its exterior (Pearl 1988; Clark 2017); this allows the cell to be described as a Bayesian active inference system (Friston 2010, 2013; see also Cooke 2020 for a variation on this approach)” — this “implements” means classification relation (but easily you can go along 3D extentionalism and try functional-physical object “implementation/realisation”).
Phrase I took from https://chrisfieldsresearch.com/min-phys-NC-2021.pdf
My text (sorry, in Russian) here: https://ailev.livejournal.com/1621997.html
IG: Excellent. The chain of causality, with emergent layers separated by Markov blankets is the model I’ve had in mind all the way through – even before I’d consciously heard of Markov blankets 😉 As you know my interest is going back to metaphysical foundations, but yes, even with a functional bias / preference we must get to the functional-physical realisation in the real world. Think I’ve come across Fields and Glazebrook before but yes … at root in my model, “(All) physical interaction is information exchange”. (That’s precisely why information is more fundamental metaphysically 🙂 )
4. “, the information processing is the territory, its foundation not the map of it, though obviously we have plenty of other information representations used to describe and present (map) the model and its processes to human audiences” — you refer here to “information processing” and I can point you to:
— “Integrating information in the brain’s EM field: the cemi field theory of consciousness”, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7507405/ — “I describe the conscious electromagnetic information (cemi) field theory which has proposed that consciousness is physically integrated, and causally active, information encoded in the brain’s global electromagnetic (EM) field. I here extend the theory to argue that consciousness implements algorithms in space, rather than time, within the brain’s EM field”.
— “Types as Processes, via Chu spaces”, We match up types and processes by putting values in correspondence with events, coproduct with (noninteracting) parallel composition, and tensor product with orthocurrence. We then bring types and processes into closer correspondence by broadening and unifying the semantics of both using Chu spaces and their transformational logic. Beyond this point the connection appears to break down; we pose the question of whether the failures of the correspondence are intrinsic or cultural. — https://www.sciencedirect.com/science/article/pii/S157106610580475X?via%3Dihub (и дальше по этой линии Information flow in context-dependent hierarchical Bayesian inference, https://chrisfieldsresearch.com/contextual-pre.pdf)
IG: Thanks for these. I’m sceptical of “CEMI” and I don’t consider it necessary – it’s an information field, whatever the physical substrate, EM or otherwise – but I may follow-up, since Chris Fields is someone I have time for. Thanks.
Hope I paid my debt of commenting your posts )))
Sorry, but I am not sure that tell you something substantional about Markov blanket. Your post show that you already understand difference between abstract MB и physical one, I can only add complexity of ontology choices with functional object variant )))
IG: Actually – I was hoping you’d comment on the two prior posts on Solms – but YES – we have effectively covered the same ground. I appreciate your confidence I’m understanding this – or that at least we’re both misunderstanding it the same way 😉 Many thanks for the dialogue.
In my view “process” (something that flow, functional diagrams always with some flow/current in it) is good heuristic that we deal with functional (run-time) objects, not physical/product/module (construction time) objects.
I often have talks with “only process” people (e.g. category theory or other “transformations first”). Common thinking is about objects (of attention!) and relationships (processes) and IMHO this is supported by wetware in a brain. To the thinking you need both, but with only objects it became metaphysical (especially if you have no at least 3 timescales — evolution, learning/adapting and run-time) but with processes only you have absence of attention anchors for wetware. Therefore both but things first, process second.
4D dimentionalism is good for integration of object vs. process false dichotomy. Process is related not only time and function, but also space and physical objects!
IG: Yes understand that 4D model and that in the real day-to-day world we interact with space and “things” – but I am (after Whitehead) being quite radical here – metaphysical again 😊. (These “things” only emerge from networks of “events” at the information level … longer story).
Yes, works of John Doyle is relevant here — https://scholar.google.com/citations?hl=en&user=C6DtGmMAAAAJ&view_op=list_works&sortby=pubdate
IG: In my more general public dialogues – as opposed to researching technical papers – I find very few who have heard or or understand Doyle’s multi-layer architectural-optimisation. Once we accept layers (Markov blankets) as REAL, then I think his view is VERY IMPORTANT to so much evolution / self-organisation of ALL systems. This is amazing convergence from the practical engineering level right back to fundamental physics – as information and computation 🙂 Many thanks again.
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