System state change and regulation

by circular causal loops

System state change

Classical cybernetics is about system state change; about state variable values that change in response to input or events

System Dynamics is also about system state change; about stock populations that change in response to inter-stock flows.

In both, the state changes are an inexorable result of the system’s laws; they do not change those laws.

Setting a System Dynamics model in motion reveals the trajectory of state changes over time.

It may reveal that the quantitative values of state variable or stock population:

· change in a linear/orderly or non-linear/chaotic, manner.

· change continually in one direction, or oscillate back and forth.

· settle into a steady cyclical pattern or state (as in a homeostatic system)

· periodically move from one steady state to another (as a weather system or solar system may do).

Steady and periodic states may be “attractive” - meaning the system, when in a nearby state, likely moves towards them.

Some say such a system appears is “self-organising”, but it might better be called “self-regulating”.

System state change and regulation

System Dynamics features causal or feedback loops.

A causal loop is formed when two stocks are connected by flows in both directions.

A causal loop may have a reinforcing/amplifying or balancing/dampening effect on the populations of the stocks.

Reinforcing (amplifying) loops

The two flows in a causal loop may reinforce each other.

Two stocks can continually expand or grow.

sales income

salesmen

sales income

Sometimes the long-term result of a causal loop can be chaotic.

Or a stock may exhaust a resource it needs.

But our main interest here is in causal loops of the kind found in homeostatic systems.

Balancing (dampening, self regulating) loops

In a homeostatic system, two subsystems or stocks may interact so as to regulate system state change.

Each keeps the state of the other within certain bounds.

This kind of adaptation or self-regulation has been recognised in biology for c200 years.

It can happen also, for example, in a solar system or a weather system.

A predator-prey system

A flock of sheep and a pack of wolves can be represented by two variables: the quantity of sheep and the quantity of wolves.

A System Dynamics model can help to explain how these stocks interact.

It employs the idea of causal loop between what Marx and Engels might call opposites.

sheep

wolves

fewer

sheep

In "Thinking in Systems – A Primer" Donella H. Meadows wrote that a system generally "goes on being itself... even with complete substitutions of its elements."

In our example, this means that individual sheep and wolves come and go.
What remains stable is only the roles that sheep and wolves play in the described system’s behaviors.

Of course, a System Dynamics model does not contain any wolves or sheep.

It models entity stocks (wolf pack, sheep flock), not individual entities (a wolf, a sheep), unless you count each “1” in a stock total as a model.

It models events batches (sheep killed per time unit), not individual events (a single birth or death), unless you count each “1” in a number of events as a model.

Real wolves and sheep realises the abstract system only in so far they demonstrably play their roles.

Almost everything real sheep and wolves do lies outside the described system.
The wolves can act contrary to the system; say find another flock on which to predate.
The sheep may play unrelated roles in other systems, both natural system (eating grass) and designed (sheep shearing).

The biomass system

We all depend on the fact that plants and animals balance the stock of oxygen.

System Dynamics helps to explain how these stocks interact so as to reach a balance.

A model might include these flows (and others).

More	plant material	more	atmospheric oxygen	more	animal material
More	animal material	less	atmospheric oxygen	less	plant material

Every model of the world is a selective abstraction.

E.g. this one excludes carbon dioxide, others consumers of oxygen, water, and global warming.

So the real world is likely to behave differently from a model, to some extent.

System mutation and self-organisation

Nothing said above implies changing the system’s behaviour or state variables.

Whatever the state change trajectory turns out to be, it is an inexorable result of behaving according to given rules.

While the state of a weather or solar system may change in linear/orderly or non-linear/chaotic way, the laws of physics do not change.

By contrast, second-order cybernetics (discussed elsewhere) is about system mutation.

Here, the term adaptation is used to mean changing the state variable types or how events change them.

It changes the very nature of the system; it changes its laws.

Further reading

Read “System Dynamics” and “System mutation and self-organisation” on the system theory page.