ᛕᎥᕼᗷᗴᖇᑎᗴ丅Ꭵᑕᔕ @Kihbernetics@qoto.org

There is a lot of talking against "#linear thinking" and planning in #complexity and how the only "good" hierarchy is a "flat" one.
The above diagram shows how a #hierarchy is a natural effect of folding the linear "information #flow" to match the physical structure of the system.

For nearly 4 decades in organizational change management, I've been using this idea of #hierarchy layers emerging from the #folding of a sequential "information processing" #flow and just found that a whole area in biology deals with this exciting topic.👇

https://www.sciencedirect.com/science/article/pii/S1877050922017811

Many people think that "history doesn't repeat itself" so they dislike #models because "they are based on the #past and thus not useful for identifying all the #complexity associated with the #changes that will happen in the #future."

This is most likely because they think models are for producing #forecasts, while the best use of models is, instead, to plan future #experiments.

#Predictions are often made as statements about what **will** occur in the future, while they should be only statements about #expectations of what **may** most probably happen in the (most immediate) future.

Predictions based on historical data define the boundary of the narrow conical "#possibility_space", the "volume" of which rapidly increases with longer prediction times.

#H_v_Foerster defines #complexity as
>"the number of #cycles it takes to compute the arrangement from the description".

When the description is much shorter than the arrangement it describes, we have #order, and when the length of the description is the same as that of the arrangement we have #disorder.

>"If the length of the description approximates the length of the arrangement, it is clear that *we do not understand this arrangement*."

😀

Reading Wolfram's "A New Kind of Science" to see what's all the fuss about and I think he's got the basics wrong.
For starters, his "cellular automata" do not produce #behavior but #structure, and all of the #complexity originates from the fact that every new row has two more pixels.

https://www.wolframscience.com/nks/p27--how-do-simple-programs-behave/

Conway's "Game of Life" cellular automata can be said to produce #behavior as they maintain their initial dynamic #structure while moving through the "dead environment" of empty cells and will change their behavior when interacting with the static or dynamic structures of other "live" automatons.

https://playgameoflife.com/

I've drawn the picture below as a reaction to the current inclination of prominent #ChangeManagement the(r)orists frowning at "military" style #CommandAndControl or #C2 organizational methods while promoting #FlatOrganizations without #MiddleManagement and with no need for #Planning (everything will change anyway), as a far better, more #Agile way of dealing with #Complexity.

This may be true if the organization is operating and struggling to survive on the left side of this "complexity plane", but if it wants to mature and "get somewhere in life" it better starts aiming for the other side by collecting, documenting and using "lessons learned", planning for and working towards wherever it wants to be in the future.

Complexity is in the eye of the beholder (observer)

Ashby: "In this book I use the words “very large” to imply that some definite #observer en, with definite resources and techniques, and that the system (is in) some practical way, too large for him; so that he cannot observe completely, or control it completely, or carry out the calculations for prediction completely. In other words, he says the system (is) “very large” if in some way it beats *him* by its richness and #complexity."

p.62