Flattening seems to have suddenly become a fashionable word in “complex adaptive systems” discourse. This was the most recent LinkedIn post that raised the topic.
That is, the error we make in seeing causal relations in a single layer of interacting entities, ignoring an important aspect of complexity that different processes and different entity-abstractions, happen in different architectural axes and layers for good evolutionary reasons. And, since evolution – of ourselves and our ecosystems – never ceases, what were once good reasons may become problematic and/or exploitable by later evolved entities – viruses in our broadest conception of that word.
Even if real world change happens self-evidently in physical implementation layers, it is so important that we think, and think about thinking and planning and project-managing, in other layers of abstraction.
I responded to a couple of mentions on LinkedIn that “flattening” was a word I have been using, so I was prompted to check the history of the word here on Psybertron. In 2010 I quoted Mary Parker-Follett, the original guru of management gurus back in the 1930’s.
‘Flattening out “important differences” … finding difference is easy, synthesising common value is harder’ – Mary Parker-Follett
“Significant Difference” has become a core concept for me, recognising all significant differences and where they fit in our integrated approaches to real-world human decision-making. Necessary distinction or discrimination, as valuable input to our integration efforts, without implying or treating them as competitive or divisive dichotomies. [One obvious 21st C source of flattening – “cancelling” differences from discourse – arises in “woke” or Mental Parity of “neurodivergent” contexts – (Lionel Shriver – “Mania” (2024). “Requisite Variety” anyone?]
Parker-Follett – like Dennett and Hofstadter and McGilchrist and Solms and Levenchuk and a few more – is a hero of mine, and relatively recently (2019-2022) I added John C Doyle to that list. Flattening – the accidental overlooking of important architectural distinctions – is central to his work as it is to mine.
His background is electronic control-systems / control-theory, where he has well-respected text-books, so it’s very easy to be prejudiced against him – like all Cybernetic / Computer “machine” language – in our complex human contexts. [Same reason I coined “Psybernetics” of course.] But he has turned his insights to our human predicament in the 2020’s.
Previous posts of mine refer to two different John C Doyle presentations. (They are incredibly dense and rapid-content-filled, with little quarter given to any background understanding, but the effort is highly recommended.)
“Scientists will Hate This“
– Psybertron post April 2022
“Zience and John C Doyle“
– Psybertron post April 2022
“Who’s in Charge?“
– Psybertron post August 2019 (My first reference to Doyle in Gazzaniga, in which Gazzaniga also makes the connection between many-layered architecture in mind<>brain functioning, and the understanding lost if we flatten the architecture in our minds.)
Those recommended John C Doyle presentations are:
“Universal Laws and Architectures in Complex Networks” (Doyle, 2018)
“Universal Laws and Architectures and Their Fragilities” (Doyle, 2021)
It’s Architectural – about HW / OS / SW layers and levels
It’s about SEAFT’s – speed-efficiency-accuracy-flexibility trade-offs
It’s about DeSS’s – diversity-enabled-sweet-spots
If we flatten out all the differences within and between the different architectural layers the losses are enormous. We don’t embrace difference and divergence by treating everything the same, or ignoring the ones with the most “baggage”, we do it by dynamic integration.
Enjoy!
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Post Notes:
One LinkedIn post not so obviously related to the flattening topic, but note that layers come on multiple axes. It’s architectural not spatial. Primary focus here is intuitive insights about the Data / Information / Knowledge / Wisdom “framework” – the weakness of one-dimensional 4-layer pyramids. But it’s exactly on point that knowledge layers exists on at least two axes, one of which is our conceptual model of knowledge as distinct from our perception of the known & knowable. (Savoir/Connaitre & Kennen/Wissen again.)
This was the LinkedIn post that prompted my riff on “Flattening” above.
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