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I made a single reference to Pirsig and “quality” in the previous post about Kastrup and “consciousness”. It prompted me to re-read a few earlier references I’d written – a couple of which I realise I’m quite proud of, and thought I’d “re-up” on these pages:

“After the Fireworks” (July 2018)
– a retrospective on the Pirsigian journey shortly after his death.

“If Something’s Missing, Just Write it” (August 2018)
– subsequent reflection on shared / parallel life trajectories.

“Hoops of Fire” (September 2008)
– an earlier signing-off at a Minnesotan cross-roads in my own life.

And for completeness simply bookended by:

The most recent “On Quality” (2022)
and earlier
my conference paper “It’s Evolutionary Psychology, Stupid.” (2005)
and (pre-Pirsig) my MBA Dissertation on Change & Culture (1991)
where some of my issues were first documented.

All prompted by getting my head around “just write something!“

In this dialogue Bernardo Kastrup gives us his working definition of “consciousness:

That whose excitations
are the experiences we have.
(Independent of our experiencing of them.)

The sine qua non of experience.

Raw subjectivity
Raw basic experience.

This is Pirsigian quality.

He doesn’t use the formulation above in his book “The Idea of the World”, but I concluded there that his perspective there was already Northrop, Pirsig, Whitehead, take your pick. (That link to the book review in fact includes as comprehensive a statement of my position as any.)

Since most people using the word consciousness mean something – stuff & things – much richer, many layered, experience in many dimensions, than this raw essence I see this qualitative essence of experience as proto-consciousness – so rather than seeing pan-psychism or literal idealism, I say pan-proto-psychism. Framed like this it’s not remotely contentious?

[His model of Alters and TWEs – in his book above – just seems to pile confusion on top of what is quite a straightforward monism. Why do people go for controversy and disagreement when there is synthesis and agreement there to be had? Yeah, I know, clicks and sales.]

Anyway just more grist to my definitions mill:

“The trouble with definitions is that although they can be practically useful, the one thing which they cannot do, is definitively define a thing”

[Broad v Narrow. Systems Thinking – architectural levels of detail ignorable. Onward and upward.]

I watched a talk by Eva Jablonka on the Extended Evolutionary Synthesis (EES) which I’ve mentioned previously in connection with Kevin Laland et al. (In fact she provides one of the cover blurbs for his “Darwin’s Unfinished Symphony“.)

Apart from the general plea for the phenotypic focus (*) on evolutionary change – of which (DNA) genetics are simply one (important) aspect along with several other epigenetic processes – with which I’m already on-board, I was struck by one particular section captured in this screen shot:

The more general underlying problem with mainstream / orthodox science – that the style of thinking itself excludes proper consideration of alternatives, even alternatives that might solve long-standing problems.

I often talk in terms of many stalemates boiling down to being too narrowly and objectively reductive in “what counts as evidence”. Yes, we all demand evidence – empirical evidence – in support of any thesis, but we might need to be more open-minded in what evidence is available for consideration.

Those quotes above from Fleck are excellent – very reminiscent of Kuhnian revolutionary paradigm shifts – in terms of denial preceding tipping points. In terms of what counts as evidence this stood out:

“judgement which
the thought-collective
considers evident”

“The Thought-Collective” – ie a group-think problem.

The emphasis on style of thinking captured by the orthodox paradigm – where I am very much pushing “Systems Thinking” alternative to objectivity for want of a better definition of the orthodoxy. Even the textbook definitions of genotype and phenotype are trapped inside objectivity. Fascinating possibilities here.

(*) focus on the whole organism (typical individual and the species) properties and behaviour, not just its DNA and gene-sequencing as if the former is simply / wholly the expression of the latter. It’s not.

In all questions of inheritance it’s about what kinds of property or process information can be captured (remembered) and communicated (transmitted) directly or symbolically to future generations. I’ve never been too precious about even genes being all that clearly defined objectivity, so have never found objections to memes on such grounds to be problematic. (Jablonka has herself been sceptical about memes previously). But the lines we draw round stuff to consider them as “distinct things” we give names to have always been essentially pragmatic / useful – and organisms are no different in defining their own boundaries. (Consider the Python’s black knight?)

Anyway, boxes & arrows / nodes & edges / trees or networks – are the natural language of Systems Thinking. Loved this too:

A musical interlude:

A couple of years ago I posted some pictures I took of John McGeoch, back in 1978 when he was playing in Howard Devoto’s Magazine, in a Facebook thread reminiscing on Devoto and Magazine.

(Photos taken at The Nashville Rooms, a regular London pub-rock bar in the days of punk, that later became The Three Kings. I seem to recall that Lemmy and Phil were in there that night, playing the gaming machine.)

Anyway, John died young – aged 48 in 2004 – but in his time played in and impressed several important bands including Siouxsie and Visage (both of whom I also saw, the latter with John) and more significantly here, Public Image Limited (PiL) who I follow to this day. He was the link between Keith Levine (original) and Lou Edmonds (to this day) in the PiL sound.

This post is really an excuse to link to (a) this 6 part audio documentary, and (b) below some footage from a festival gig in Estonia in 1988. Despite the intrusive Estonian talking heads / interviews, and the tinny sound, great to see both John and Lou (with Johnny, Bruce and that must be Alan Dias on the bass?) performing The Public Image, Home and Rise.

And the reason for posting? There is a new biography of John McGeoch.
“The Light Pours Out of Me” (2022) by Rory Sullivan-Burke.

[Work in Progress – Draft will be edited without notification.]

[Feedback appreciated on the “See / Refs” – where more are needed? Meantime all those indicated will be elaborated and worked into the text. And obviously on the intelligibility of the text so far. Drafting arose out of the “Three Essays” post, particularly “Algorithms for Humans” and the comment thread on “Balancing the Future”. It represents one (of many) draft of my “thesis”.]

[UPDATE(S) : As requested, I will soon be moving this content to a “page” and will continue the updating there, and simply notify “significant” updates in “posts”. This post will become a stub, linking to the page.]

[At this stage I don’t intend any revision-control within the page / document, but if anyone is up for it we could turn it into a collaborative document? As in fact is being done with “coda” in the Active Inference Lab.]

Information & Systems Thinking

I’m going to talk and think in terms of systems, information and processes and some of that will use language often associated with machines and computers. The fact that until recently computers were actual humans doesn’t seem to lighten the mechanistic baggage that we tend to see computers and computing systems as electro-mechanical machines – and indeed much of their research and development uses (eg Turing) machine language.

Using the terms above, almost anyone with a humanist / humanities bent will baulk if I start using them to talk about how humans actually work biologically, psychologically or socially. Any use of them being at best metaphorical in underlying biological processes. Crude talk of living things as machines and brains or minds as computers generates a pretty negative knee jerk response. Part of mechanistic scientism over-reaching into the humanities, and missing – devaluing – its true richness.

Believe me, I share that reaction. My main quest here has for 22 years been precisely to point out the shortcomings of logical, objective, scientific thinking when it comes to human enterprises of all kinds. So, I’m not going to use the language of physical machines or computers if I don’t mean to. What I am going to use is systems thinking, and I will be talking about how it, they, we process information.

So, firstly I need to reassure you that when I say the words – systems, information and processing – and the shorthand of computation – (and god forbid, algorithms) – for what systems that process information do, I’m not, and you mustn’t therefore, be thinking of these as machines or computers, mechanical or electronic.

System is simply the word for any collection of stuff, thought of in terms of the arrangement / architecture of its component systems. Systems made of systems in any arrangement, any level of nesting within each other. Notice I could have said “parts” for the sub-systems, provided you didn’t assume I meant material-physical things. We really are talking any stuff as the subject of any thought or dialogue.

• • • Two (or more) dots on this screen. Two (or more) points separated in any frame of reference in fact.
    • A pair of quarks, an electron-proton pair, a DNA molecule …
    • Two hemispheres in a brain, a fore-brain and a mid-brain, a whole brain, a whole brains-trust of many brains.
    • Me writing and you reading this.
    • Two sides of a debate, the set of concepts in a dialogue.
    • The idea of logical positivism, the entire works of Dostoevsky.
    • A trolley problem, a termite mound, the Sagrada Familia.
    • The thought experiments of philosophers everywhere.

The reason to think of them as systems – to use systems thinking – is to focus on their architecture: how they’re arranged, how they relate and communicate with each other and with their parts as more sub-systems.

A system is literally anything conceivable, but thought of in terms of its architecture. Systems Thinking. Literally.

[Communication is a fundamental information exchange process. In fact information, communication and computation are as fundamental as – if not more than – physics itself. See xxx / later.]

[Cybernetics – the root topic in this Psybertron blog – has itself suffered the same skewing of perceptions as the systems and computation talk above. Very much from Plato to the 1946 Macy conference it was about kybernetes, the self-governance of human affairs, but it was taken over in implementation by computer geeks for the past 80 years, to the point that the original human cybernetics became known ironically after 1963 as “the second cybernetics”. In further irony, the term governor had already been  borrowed metaphorically from human affairs to name the mechanical devices regulating the speed of early machines like 19th C steam-engines, long before we reinvented Roman thermostats. See yyy / later.]

[For more on Systems Thinking itself and positively practical application to everyday use by real humans in business and in government, see Anatoly Levenchuk (Ref). Unfortunately, in this piece I’m going to climb & dive into layers further removed from the everyday – using the abstractions of systems thinking to address some fundamental issues with science and humanity.]

Systems & Sub-Systems

When we talk of wholes and parts in everyday life – or of systems and sub-systems in systems thinking – there are common throwaway opinions:

“Some things – significant real-world things – are more than the sum of their parts and anyway, context is everything.”

To which orthodox scientific “rational” responses would be:

“Well no, once you’ve taken proper causal account of “all” component contributions, there can be no more. And, no, context is just more stuff to be taken into account.”

As ever the initial problem is at least partly linguistic – “taking into account” is more than a “sum”. We have apples and pears involved. 2 apples “plus” 2 pears/nuts/shells/seals gives us neither 4 apples nor 4 pears/nuts/shells/seals – but 4 fruit at best, 2 pairs of different kinds. And the process itself introduces or creates kinds we might not have started with. Pairs are not the only fruit. The best solution depends on what was the question.

[Classification / Naming / Definitions / Taxonomies / Good Fences / Tabletop / Rules & Exceptions / Identity Politics / Levels of Abstraction / “Small & Large” Facts]

The combination of component “parts” is more like systems architecture (above) and more like integration than addition. What’s more, an integration / assembly over multiple axes and levels of “kinds” of stuff and “types” of things, not to mention integration of processes over multiple timescales and histories in most real world contexts.

At human, individual and social scales, and at both micro and macro physical levels, and indeed at any number of biological, geological and cosmological scales, the outcomes of so many real world processes are statistical, dependant on probability distributions. Different kinds of distribution over different axes and timescales on top of the distributions on the kinds of stuff and things. Accounting as simple addition doesn’t get us very far. Being charitable, every discipline knows which particular set of mathematical / computational operations helps with their day job.

[History matters – Ergodicity / Uncertainty / Bayes / Taleb]

Complicated, or complex, or both. I don’t intend to make a big thing of the difference here, but different they are. Which is another complication.

It’s complicated.

Leaving aside whether in any real world case it could ever be practical, tractable in a computational sense, to take literally everything into account in a useful finite time, is moot.

In reality we draw control volumes – boundaries – around our problems. Boundaries within which and across which we are either in control or have workable levels of predictability and uncertainty. In a scientific, logical or mathematical context those control boundaries may simply be credible working assumptions, which can themselves be varied and validated in due course. That validation may often be the point of the scientific exercise. In most human endeavours it’s about holding the alligators at bay whilst we drain the swamp, or whatever it was we were trying to achieve.

This process / strategy looks like good practice learned from shared human experience, but it turns out to be much more fundamentally natural than that.

Faced with complexity, systems thinking helps. Remember, that means thinking in terms of stuff being systems as described above, not simply systematic / systemic / disciplined / formalised ways of thinking / planning / acting. Systems Thinking says treat the thing in front of you as a system whether or not it’s called a system in an everyday usage. A computer application or a collection of work processes or protocols or any electro-mechanical network assembly – we readily see as a system. Systems Thinking says treat every collection of stuff whose existence or behaviour depends on interactions with other stuff as a system. Systems Thinking addresses complexity.

[Park the argument for now between the strict orthodox scientist that says everything is ultimately physical, including our thoughts – the kind of thing material / energy / physicalists say – and the humanist who would reject that. Remember we already hinted earlier that information and computation are more fundamental than either physics or thought. More later.]

Thinking of the complex stuff / complicated thing in front of us as a system we will identify component sub-systems – the parts – and we will also identify the context – the super-system outside our control boundary – the world. We can further subdivide our thinking as necessary. Our system its internal sub-systems and that external world super-system can be further divided into as many more sub-sub-systems as suits our task.

Thing, with boundary, with internal view and with external view.

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Now the clever – and surprisingly fundamental bit – that describes and makes an ontological commitment to the reality of both physics and thought evolving from this basic view of systems and information processes within and between them.

At this point you need to believe the whole of the world and everything in it, can at least be thought in these “systems thinking” terms above, without any losses? No point elaborating if there’s already any objection up to this point.

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So given seemingly universal applicability of Systems Thinking, without any other presumptions about what exists in the world, the entire world, micro and macro, past and future, how it all works and fits together – no other presumptions – the world is my oyster. No? Just a way of thinking about stuff, including thinking itself.

I’ve already noted asides in the text so far, and below we have dozens more angles and corollaries to come. And this is the history of the entire world, so far and forever – a very long story – so clearly I’m going to need to be choosy about which bits I try to articulate and why, what for? I need to apply systems thinking to my systems thinking – which details matter where?

Remember I may not mention every detail, or give examples of everything, that has happened or may happen in the entire (previous and future) history of the world, but this story really is about all of that. Seen through systems thinking.

In many ways, apart from my aim to synthesise the story around systems thinking, there is nothing here I’m creating that’s actually new. All my sources and ideas are already published – some of them long ago, some of the syntheses more recently. ‘Twas ever thus. Nothing new under the sun.

And obviously lots has been written scientifically and philosophically, and mythologically or creatively metaphorically in the humanities – in literature generally. And a lot of that reflects prehistorical oral traditions before there was even literature. In some ways, from the humanities side, all I’m doing is clearing a path through ancient wisdom and intuitions that have become overgrown with scientific progress. From the ever-contingent science side, I’m mostly pointing out missed opportunities in choices made to best fit the data at the time, according to the methods of science. Stuff taken to be right – best current model – because it worked for some recognisable STEM purposes. And which touch-points / differences / controversies are invisible in the undergrowth may be obscure archaeology unless they’re part of your specialism.

This story is necessarily polymathic and necessarily of unequal depth and rigour in any given specialism – an occupational hazard of multi-discipline working. Indeed as we shall see – having the courage (*) to know which details do and don’t matter in any given context are a fundamental part of systems thinking. So this is mostly a story of rhetoric and persuasion, bringing together science and humanities where each may have missed a trick in reconciling with the other. Creativity and metaphor are not confined to either. No truth without reconciliation.

[Sun Tzu “Art of War” – Even if he could know them, the general does not need the position of every blade of grass on the battlefield, not even every individual warrior. (*) And “courage” – see risk aversion in “science” generally.]

And, I need to start somewhere. One thing I skipped so far, was that in claiming universality for “systems thinking” – I also mentioned information, processes and computation, and I subsumed them into that systems thinking umbrella without saying much more about them.

[Placeholder – I could come back and branch differently from this point? Why don’t you start with … ]

I can’t quite believe myself, but in order to start talking about information and processes in a systems thinking way, I’m going to start with one of the most abstruse technical areas of philosophy, metaphysics itself as a basis for all existence (ontology) and meaning (epistemology) in the world.

[No prizes for spotting I will be relying on Whitehead when it comes to process metaphysics.]

Information however …

[Counterfactuals and conceivability. Deutsch & Marletto.]

Systems thinking about thinking? Let’s do a thought experiment. Not one of those that asks you to suspend disbelief about whether it could possibly ever be arranged to happen – and please god, not a trolley or a zombie – just literally a thought experiment.

Imagine nothing and imagine the least thing that could conceivably exist in or in addition to that nothing, to make it something other than nothing.

Firstly, whatever you managed to conceive of as nothing (you’ll have done better than Larry Krauss anyway) – whatever you conceived of as that smallest addition would, in some pretty absolute sense, be indivisible – atomic in Democritus original conception, a thing with no parts, a Euclidian point. If it were in any way divisible, you could have conceived of one of those parts (sub-systems) existing, no?

[Avoid the confusion of the pretty complex systems that science first labelled “atoms” in order to make distinctions between chemical elements. Elemental elements in a chemical sense only. These are a long way from the Democritan / Euclidian conception. That’s just naming and language – already noted above.]

It’s not possible to conceive of anything less than the least conceivable thing. No cheating now. (And this is true whatever you had to settle on for that absolute void of no-thingness before you added that thing to it – not surprising that many may have to posit some ill-defined cosmic unity thing for that nothingness, just to be able to have the thought, but no matter.)

We have in mind that no-thing and a thing. If you can think of any things more primitive than that thing and that thing<>no-thing relation, I’d like to hear it. (We’ve not said anything about things like space or time, where or when yet, nor anything else we might call physical or material? This least thing is conceivably the dimensionless thing without any properties a Democritan / Euclidian point.)

If we chose binary notation, this no-thing and point-thing looks very like most primitive 0 & 1 but we don’t have to jump to “bits” quite yet, and anyway as I said above we don’t need this line of thought to be dependent on what we thought of as that 0 (no-thing) in the first place. So patience.

Now imagine a second thing. Hard to imagine it being much different to the first thing – after all we have placed some severe constraints on our thinking so far – no time or space, no materials or physics. Whatever kinds of things these things are they are simply both the same kind of indivisible atomic point-things.

So what makes them different things? What is their difference? What could conceivably be the difference between two point-things? Their difference is their separation, what separates them?

[We might think all that separates them so far is that they arose from two thoughts – but have no fear, we’re not jumping to pan-psychism – we can keep distinct the thought we’re having (here and now in the real world) and the conceivable thing thought about – the map and the terrain, the finger and the moon.]

 

 

 

[Insert magic – aka – Sense and Experience, Free Energy Principle / Markov Blankets / Active Inference / McGilchrist, Solms & Friston / Emperors’s New Clothes / Finger / Moon – Map / Terrain – Model / Reality. Information & Quality / The Subjective Perspective.]

[All models are wrong, or are they? Active Inference makes the ontological commitment as well as providing the model.]

[Corollaries / Negative-Corollaries / un-Adages: It’s the thought that counts. Things that can be counted are not the things that count.
Nothing new under the sun / ‘Twas ever thus / This s all already “out there”. Free Won’t, Vive la Differance, Devil in the Details / Examples / Pennies & Pounds / Butterflies & Cow-farts / One subject & one audience at a time?]

Meantime, any Psybertron content searches on Markov, Solms, Active Inference, and the Information Metaphysics (2019 version) will all be recast in this Work-in-Progress essay. It’s all there 🙂

Pass me the machete?

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Another, the first, of Richard Emerson’s interlocutors in his “Rebalancing the Future” podcast series was ex-GP writer James Willis. His “Paradox of Progress” and “Friends in Low Places” were books I reviewed very early in my own journey, and reviewed again when PoP was re-released earlier this year. We’ve been “fellow travellers” riding pillion on Robert Pirsig’s “Zen and the Art of Motorcycle Maintenance” and culminating recently in us both finding great value in Iain McGilchrist’s “The Matter With Things”.
(I talked with Richard on these and more last week.)

Early in this conversation on the “paradox” of James’ title, so much reminded me of what had originally resonated with me. You’ll find rules and definitions are regular – and deep – topics of mine here on Psybertron.

Obviously science-led technological process is wonderful, but the paradox is that the way it gets implemented can so easily miss or destroy important human value, and even our humanity itself. (This is a very old sentiment, as old as any romantic resistance to classical science – “we murder to dissect” etc.)

If we implement rules and definitions and best-practices of “experts” in technology in such a way that it constrains what humans can do, ie by physically enforcing rules definitively, we’ve taken away their / our humanity. Accountant managers rarely value this cost-disbenefit.

From me:

Most recently “Definition as a Coffin”
Back in 2013 “Hold Your Definition – Dennett”
And, the hazards of writing rules around definitions – definitive rules – has formed the basis of my “Rules of Engagement” for even longer.

Rules are for
guidance of the wise
and the obedience of fools.

A long-standing agenda item, that still deserves a considered essay from me is “Good Fences” mentioned earlier in both the Dennett Definition and Definition Coffin links above. There are good reasons to have definitions (fences around things) but even better (identity politics) reasons to understand why these should be respected, but never treated as cast in stone – or “cast in silicon” to use James’ computing version.

There can be no doubt James’ words have been an important inspiration to my own work for two decades.

Richard Emerson invited me onto his podcast earlier in the week.

We’ve met Richard here before on Psybertron, his personal “Renaissance” book and his “Ancient Worlds” project discovering – rediscovering – the value in ancient wisdom, with Dante’s “cosmology” being the poster boy.

Apart from having a shortlist of topics before we started – and my specialist subject heading of “Cybernetics” – we didn’t have much agenda or preparation other than Richard’s working title for his current podcast series: “Great Conversations about Balance and Rebirth”

Whether it’s a great conversation, you can judge, but Richard holds the thread together as I trample over our starting topics: Robert Pirsig’s “On Quality” published this week; and Iain McGilchrist’s “The Matter with Things” from late last year, in my rush to future synthesis.

Hopefully you’ll find it interesting, I certainly found it a useful exercise having to think on my feet and obviously now have half-a-dozen things to write-up based on things I now know I missed in the conversation. How many times for example, did Richard use the word unify, and I failed to make the direct connection between McGilchrist’s hemispherical dualism and Pirsig’s quality monism now reflected in the computational monism of Friston / Solms / Doyle et al under the Active Inference umbrella. You can probably tell, I’m quite excited about these latest practical scientific developments, in the talk as well as the pages of this blog. Onward and upward.

No more spoilers, have a listen. Thanks for the opportunity Richard.

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Just place-holders for pieces – or indeed whole comms campaigns (?) – that need to be created: Most generally because there are enormous persistent misunderstandings within science & technology and with public understanding of it, and specifically (topically given Musk!) with socially degenerate aspects of ubiquitous social media.

In no particular order, they’re all connected multiple ways:

AI or AI?

So called “Artificial Intelligence” has so far come nowhere near real (human) intelligence, basically “automation”, although in the right hands, its study / research / experience to date has undoubtedly contributed enormously to understanding real brains and minds. Part of that understanding is essentially Systems Thinking:- from the Free-Energy-Principle / Markov-Blankets / Ergodicity / Strong-Emergence under an “Active Inference” umbrella, also conveniently still AI.

Algorithms for Humans?

Those with a human (humanist / humanities / dare-I-say spiritual) cultural perspective will react negatively to machine (algorithmic / electro-mechanical computer) models of “how real human (hard/intellectual & soft/emotional) intelligence works”. It’s a common sense of science over-reaching into the “human” culture war, the politics, as old as “The Third Culture”. Our mechanistic, imperfect, negative experience of algorithms so far (eg in social media, and marketing) and automatons (eg in robotics and thought experiments) can only reinforce this sense. However there is a perfectly credible story that the softer side of the human condition is explainable by categorical / qualitative “algorithms” in a living, biological “soft machine”.

Subjectivity for Scientists?

A lot of this reasoning is currently hampered by the limitations of orthodox (objective) scientific rationality. Partly general reductionism in causal chains that cannot handle the strong-emergence of causal agents acting independently of their component parts and partly that subjective agents with “minds of their own” are modelled objectively if not explicitly excluded by design. Catch-22. Scientific rationality needs to embrace – empathise with – the subjective perspective – cross Solms’ “Rubicon”.

Subir Sarkar was interviewed by Sabine Hossenfelder last month, but I didn’t capture the link then:

Interesting content in the “Einstein was right when he said he was wrong” domain when it comes to the cosmological constant. Pointing to to some radically “new” ideas being needed to fix anomalies in physics. (That’s new as in old, but ignored.) But as I tweeted at the time, it is a fine interview anyway – proper respect between scientists with different disciplines of expertise and levels of experience.

Was prompted also to read this “Heart of Darkness” by Subir Sarkar on the same topic in a magazine called Inference. More spooky convergences, as “Active Inference” is this month’s topic in Cybernetics & Systems Thinking generally.

A quickie to capture this link:

Very impressed watching this recorded Web3 Foundation talk by Michael Zargham. He’s a name I came across from making contact with the “Active Inference Lab”. I already know Anatoly Levenchuck and Karl Friston on the AIL Advisory Board and discovered that Zargham is another board member.
(I’m intending to participate in the .edu domain of the AIL.)

The Age of Networks
and the
Rebirth of Cybernetics

Highlights:

• • • Very positive non-apology for focussing on many layers of abstraction above the bits & bytes. The essence of systems thinking is knowing what details to ignore in various levels of complex systems.
    • Very familiar recap of the history of Cybernetics starting from Plato (Kybernetes) via the Macy conferences. With “systems thinking” and network architectures front and centre of response to complexity.
    • Being comfortable with circular reasoning (Hofstadter for me). “Second Order” Cybernetics, positive as well as negative feedback loops. Future consequences are causal now. (There is active predictive inference involved – hence AI-Lab.)
    • Attention cost of participation (eg in social government). The more the “infrastructure” can handle invisible processes we don’t have to worry about, the better for us. Transparency is a distraction from what really matters. Noise means we always fall back to lowest common denominators. [See Mental Switching Costs]. What we need to trust is that the design of the decentralised system knows its own limitations.

(And, great to hear someone use that quote “All models are wrong, they simply have a valuable domain of intended use.” 3 decades (!) since I heard Julian Fowler use it.)

Anyway, a new “hero” (with no mention of John Doyle).
Connected on Twitter.

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Contrast with this pm’s talk with Iain McGilchrist’s elaboration of his “Sense of the Sacred”. Tremendous audience (and Iain) prejudice against “engineering” and machine language. These two domains just don’t get how close they really are. Same as deep thinking physicists being very close to the same sense of (something) sacred. In the Solms / Friston (bio-psycho) story, the turnaround of Damasio is telling, from the same prejudice against mechanistic algorithms to understanding the human subject involvement.

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