ᛕᎥᕼᗷᗴᖇᑎᗴ丅Ꭵᑕᔕ @Kihbernetics@qoto.org

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.

A #hierarchy is an #emergent property of an otherwise "flat" #network composed of randomly connected elements.

I see more and more individuals doing "organizational change in complex systems" frowning on the mention of "best practices", documentation, and planning, because:

> "in an increasingly interconnected world where technology, information, and customer expectations evolve at an accelerating rate, insights from past performance quickly become irrelevant in many scenarios"

https://medium.com/topology-insight/best-practices-are-useless-in-complex-systems-f7797a12071

All such "modern approaches" to dealing with *complex systems* forget that the *insight from past performances* is the **only** thing we can actually rely on while preparing for the uncertain future.

They also forget that organizations normally work, not in any one of the *clear, complicated, complex, and chaotic domains* at any point in time, but they are rather *in and out of all of those situations all the time*, and different parts of the same organization can also be in different situations at the same time.

Best practices are also not "silver bullets" as they would like you to believe. Best practices are the default (only possible) response the system can produce to par the current situation because it depends primarily on the #knowledge_state the system is currently in.

Having *diverse perspectives*, allowing time for *experimentation*, and maintaining short and direct *learning loops* are not some new and "improved" methods the organization should start adopting when things get complicated or complex, but should be rather part of the (documented and planned) very ***best practices*** an organization adopts as the *normal way of doing business*.

>#Life must have emerged from the physical world. This emergence must be understood if our knowledge is not to degenerate (more than it has already) into a collection of disjoint specialized disciplines.

>... #physics and #biology require different levels of #models ... physical theory is described by rate-dependent dynamical #laws that have no #memory, while #evolution depends, at least to some degree, on #control of dynamics by rate-independent memory #structures."

https://www.researchgate.net/publication/12009802_The_physics_of_symbols_Bridging_the_epistemic_cut

>A #sign is something, A, which brings something, B, its #interpretant sign determined or created by it, into the same sort of correspondence with something, C, its #object, as that in which itself stands to C.

#CS_Peirce (1902)

In #Kihbernetics a sign is the #model describing (documenting) a #system ("mental model") abstracted from a real #phenomenon (object) by an #observer (the interpretant).

The self-#constructor:

#HH_Pattee
"Evolving Self-reference: Mater, Symbols, and Semantic Closure"
https://www.academia.edu/2947945/Evolving_Self_reference_Matter_Symbols_and_Semantic_Closure

#DNA is not a #blueprint describing the #structure or the #function of an #organism.
It is rather a #recipe (an algorithm) that describes (#control) the #process of #construction, i.e. specifies what #action the #constructor has to perform at any moment depending on the constructor's own #state and that of its #environment.

The #function of the #organism is defined by its #structure as constructed by following the #DNA algorithm. Besides the functions involved in this process of self-construction and maintenance, a mature organism is also involved in the #production of #artifacts (messages, seeds) for dissemination (#communication) in the environment.

A rare short and clearheaded analysis of the **real** risks associated with the use of #AI tools in contrast and response to the general overwhelming "doomsday" hype such as that presented in the recent *6 months moratorium* letter.

https://aisnakeoil.substack.com/p/a-misleading-open-letter-about-sci

If #AI by some chance gets in a position with the power to "wipe out" humanity, it will be not because of its superior intelligence but because of humanity itself.

The truth is that intelligence neither craves power nor it is a precondition to raise into a position of power. Quite the opposite.

Some wise words from John Dewey about the difference between #Intelligence and #Power written back in 1934.

https://newrepublic.com/article/100340/intelligence-and-power

Humberto #Maturana Romesin on ***structural determinism***
*Our Genome Does Not Determine Us*
Presentation made at the Remaining Human Forum
Vancouver, B.C., May 22, 2001
https://asc-cybernetics.org/2001/RH-Maturana.htm

1943 - The year when it all started:
#Cybernetics, #Computationalism, #ANN
From: *Brains, Machines, and Mathematics*
by: *Michael A. Arbib*

Control theories such as #PCT (Perceptual Control Theory), which are based on #Cybernetics, are primarily focused on the #negative_feedback control loop closed through the system's #environment and have little or no concern for the more important, internal #positive_feedback motor loop controlling the system's #growth and #learning cycles.

#Meaning is usually described with #VectorSpace #Semantics as in the article below comparing the works from #CAShannon and #AMTuring:

https://www.journals.uchicago.edu/doi/full/10.1093/bjps/axx029

Basically, what vector space semantics says is that the meaning of a message depends on the #Context provided by the sender's and the receiver's #DynamicalSystem #Knowledge #State.

As they are two different physical entities they will obviously be in different states, so the two meaning can never be *exactly* the same.

People often interpret *Ashby’s Law* (after W. Ross Ashby) as if the *system*'s internal states must have the ***same level of variety*** as its *environment* in order to survive, which implies that the system should be able to *respond* (react) to every little disturbance from the environment.

This is not completely true because, on the lowest, #regulation, level, the system blocks #variety from an (environmental) #disturbance reaching the (internal, system protected) #EssentialVariables in two ways:

1️⃣ #Passive isolation (sheltering) from most environmental disturbances, and

2️⃣ #Active reaction to (parring with) the remaining disturbance that managed to *break through* this passive protection.

from "Intro to #Cybernetics"
http://pespmc1.vub.ac.be/ASHBBOOK.html

Stages of #Learning according to #Kihbernetics:

4️⃣ #Unconscious Incompetence: I think that I know what I'm doing, but I don't;
3️⃣ Conscious #Incompetence: I know that I don’t know;
2️⃣ #Conscious Competence: I know that I know;
1️⃣ Unconscious #Competence: I am doing it without thinking.

A truly remarkable thinker
**Anthony Wilden** - ***#System and #Structure***
*Essays in Communication and Exchange* - Second edition (1980)

A classic on #AL and #AI from #HHPattee written back in 1995 when polymer folding was still a *computationally intractable problem*😀.
Still, his thoughts are as powerful as ever.

"Artificial Life Needs a Real Epistemology"

https://www.academia.edu/3075569/Artificial_Life_Needs_a_Real_Epistemology

Hunter-gatherer, #DemandSharing societies, in which there is no reason to hoard more than one currently needs, are highly #individualistic as well as #egalitarian.

From:
*Work: A Deep History, from the Stone Age to the Age of Robots* by **James Suzman**

A compelling approach to #Intelligence back from the 80's that does not rely upon #ProblemSolving or #GoalOrientedBehavior as do most of the contemporary doctrines of #AI and #AGI.

#HRMaturana #Autopoiesis #DynamicalSystems #Communication