The Case For Cybernetic Governance (Part 1)

Governance, as generally understood, is a system of organization that manages interactions of entities & everything that sits in-between (rules, conditions, etc). It concerns with how entities structure responses pertaining to its environment & how they're run.

Entities in this context are defined as units that have a sense of "self"; this "self" has characteristics that sets itself apart from its environment & can be tracked on its own merit. How they exist is as much as a philosophical question as an empirical one, but in our case we will stick to the latter & identify the 4 main definitions as primitives, which are listed down here:

1. The Feedback-loop Definition: Units that respond to external stimuli (inputs from environment) and adjust its state accordingly. The information gained passes through configurations that are called a "system". The system outputs a response towards the end.

2. The Boundary Definition: Units that have characteristics which differ from a set of members. Whatever the criteria for entity status depends on the reference frame of the set & nothing else.

3. The Agent Definition: Units that are capable of action in the praxieological sense: capable of preferring a future state over the current one. This is the narrowest definition of the four.

4. The Continuation Definition: Units that maintain its existence against the entropic force of the environment. Entities get reduced in whatever feature of continuation they depend on thus incentivizing them to increase it however they can, which includes maximizing its consumption of energy (Similar to definition #3).

Entities seek ways of reaching an endgoal & will have a set of means of achieving so. These means range massively in scope & have multiple types depending on the endgoal, requiring entities to utilize energy from their environment which cannot be utilized by more than one entity; a concept known as "rivalrous goods". The scopes of how & why rivalrous goods are sought after differs; the underlying contradiction of it all is what's defined as a "conflict".

Scopes vary and however you scale this concept will create many ways entities mitigate conflicts, averaging out as "aggregates".

Setting Up Components

Since aggregates (regardless of scale size) are abstractions of entity dynamics, we can use deduction to find patterns that arise from said dynamics because of the shared denominator of entity conflicts. Because they don't occur in a vacuum, they're subjected to the high-variance of the given environment; of which sends all types of input for the entity (invoking definition #1). Actions that result are manifested by its output (invoking definition #3) are only possible due to its concrete set of features (invoking definition #2), which we can therefore deduce. These recurrences are then abstracted as "models".

Definition #2 is most useful when examining models, which relates variables. Models get made from examining aggregates which explain the entities involved and what configuration of inputs + features triggered the conflict. However, this isn't the full scope of what models can do. If we go back to how governance is defined, we see that definition #4 is what's implied; entities in a conflict invoke definition #4 to ensure their existence which depends on rivalrous goods.

And since models are the pattern-matcher of these conflicts, it logically follows that governance can comprise itself of mechanisms to store models to predict conflicts (invoking definition #4). Rule-sets are thus made according to the environment to ensure definition #4 stays active.

Below are the components needed to ensure definition #4:

Component No.1: Anticipation

Environments

Component No.2: Diffusion

Component No.3:

Applications of the Governance Concept

For example: in economics, GDP records scarcity allocation in sectors through transactions. Because prices reflect scarcity, GDP acts as a proxy of how it’s being allocated in the economy.

AI will remove scarcity in the lower sectors of the economy (tertiary and secondary sectors) since AI output will drive down price signals and their effectiveness in showing productive capacity. Humans will be consumers at best and the gap between welfare and economic activity will widen hard.

It isn’t like you could reroute and/or expand what GDP captures, because then you’d run into the valuation problem. A lot of non-market/zero-price goods (websites, software, etc etc) won’t register price signals GDP relies on.

Patchworks made to solve this (software capitalization, hedonistic adjustments, etc) are blind to this; they sit in the awkward phase where non-physical goods create their own epistemology & scale away from 20th-century measurements.