#systems theory

I know people on this site love "The purpose of a system is what it does" so here's where that comes from.

Cybernetics is the transdisciplinary study of control and feedback in systems. In other words, how systems stay in a stable state based on the inputs and feedback they receive. It is relevant to every branch of science and is the origin of the word Cyber. It was first defined by Norbert Weiner in 1943, and adapted for a business and organisational context by his student Stafford Beer in Brain of the Firm (1972).

Important concepts in cybernetics include: -“The purpose of a system is what it does” meaning that if a system is failing to meet its stated goals then that is a result of the current state of the system and it must be changed. -The idea of the “Black Box” meaning many systems are black boxes, too complicated to fully model, and instead should be evaluated by looking at their inputs and outputs, and how external and internal feedback influences them. -The Law of Requisite Variety: The idea that to control a system, the controller must have at least as much complexity of the system it is controlling. A car changes speed and steers, so it needs pedals and a wheel. A plane moves in three dimensions, so it needs a yoke and pedals to give it three dimensions of control. Management must have information complex enough to make informed decisions but not so complex it overwhelms them, and information can be amplified or attenuated to get to the level of variety needed in given situations. Management needs feedback from both the internal and external environment in a form they can quickly understand and react to, and the ability to give feedback to internal systems in an effective manner.

In Brain of the Firm, Beer defines the Viable Systems Model. This is a sort of a “Theory of everything” of management, and nearly every other management theory I have studied neatly fits inside it. It is based on how humans process information and learn. Put simply, an an organisational system will have five elements:

Operations: The people performing the actual functions of the system.

Co-Ordination: Managing resources and dealing with crisis

Tactics: Setting targets and pushing towards them.

Intelligence: Examining external environments and and possible futures to plan ahead

Ethos: Philosophy, Norms, and Beliefs.

Each role can be held by many or multiple people, based on what they actually do and not always job role. They must have communication systems between them that amplify or attenuate information based on the needs of the people. If any of these stop functioning the system will fail. Most interestingly, the system is considered a “Fractal” meaning it is the same at every scale. It can be true for a team, a department, a company, and an industry, all at once, with each Operations element containing an entire viable system inside it. I find nearly every theory I have looked at including Resource Based View, Dynamic Capabilities Theory, Stakeholder Theory, Sociotechnical Systems Theory and most others I studied at undergrad fit nearly inside this framework, and I think it is will be a useful tool to have in my toolbox in terms of setting measurements and examining feedbacks from different stakeholders, as well as scaling down as low as teams and scaling up to the level of trade associations. I have attached a diagram of the model from Wikipedia as it helped me understand the concept.

The EU AI Act is often perceived primarily as a regulatory challenge, as another layer of compliance requirements, documentation obligations, and control mechanisms that organisations must integrate into their existing processes. This perspective, however, remains incomplete. The deeper significance of the EU AI Act lies not only in the requirements it imposes on AI systems, but in the structural…

For example.

A Crawler HAS Agency BUT NO Authority

A Monsters/NPCs HAS NO Agency AND NO Authority

The System AI HAS NO Agency BUT Authority

SO

The Corporation HAS Agency AND Authority

And this is correct

At first

Until it isn’t.

Because none of it is prescriptive and says how it has to be.

Just how it is at the moment.

Systems resist change with all their might. Ecosystems may lose their balance, but they eventually heal their wounds and continue in a new configuration. Economic systems simply put their blinders on as carnage takes place in the stock market, but come back from the dead once a collapse has killed everything. Our nervous system is perhaps the most stubborn of all, because it doesn’t even register change: it goes either straight into denial, or into traumatic shock. What all these very different systems have in common, despite their different strategies, is that they consider change the enemy — so much so, that they would rather self-annihilate than take steps to reform themselves. So, next time you discuss system change with someone, explain to them the fundamental raison d'être of every system: to stay the same, or die.

This is why it is never an oxymoron to state that a successful system is also a self-destructive system. Humanity is incredibly successful, but also incredibly self-destructive. The success comes from building resilient systems that robustly resist all reform and external pressure. The demise comes from when these systems eventually come across new parameters that they were never built for. Most times, they fail. A system is only successful as long as the parameters it evolved in stay the same. On a rapidly changing planet the knee-jerk reaction of all of our systems is to commit suicide than have to go through the thought process of conscious, voluntary change: a quality that is foreign to most systems.

Humanity’s problem is therefore not that we lack solutions. We have thousands of them. The problem is that every solution asks a system to do the one thing systems are least inclined to do: change. We keep writing love letters to imaginary humans who are willing to change their minds, sacrifice their interests, abandon their habits and save their descendants. Meanwhile, the real humans remain embedded in systems that reward precisely the opposite behaviour. Perhaps this is why the question 'How do we change the system?' has always bothered me. The more important question is:

What happens when the system discovers that we are trying to change it?"

What It Means To Persist Through Time

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