System stability and change

Copyright 2017 Graham Berrisford. One of about 300 papers at Last updated 02/03/2018 20:53


Heraclitus of Ephesus was a Greek philosopher known for his doctrine of change being central to the universe.

Plato quoted him as saying “Everything changes and nothing stands still.

Yet central to human existence is that some things are stable, or stable enough, for a while.

What makes a system stable? And in what ways can it change?

This paper discusses system stability and change with reference to Ashby’s ideas.


Entities and systems. 1

System stability. 2

System change types. 3

Discrete system mutation. 5

Continuous system mutation. 6

Conclusions and remarks. 8

Footnote: more about biological organisms. 9


Entities and systems

“At this point we must be clear about how a "system" is to be defined.

Our first impulse is to point at [a concrete entity] and to say "the system is that thing there".

This method, however, has a fundamental disadvantage: every [concrete entity has] no less than an infinity of variables and therefore of possible systems.

Any suggestion that we should study "all" the facts is unrealistic, and actually the attempt is never made.

What is necessary is that we should pick out and study the facts that are relevant to some main interest that is already given.” Ashby 1956


Simply pointing to an entity and calling it a system has no useful meaning.

Since an entity is as many systems as we can describe and test it as being.


Different entities can realise the same system over time

E.g. One bus company may replace another, operating to the same timetable.

The actors change, the roles and rules remain the same.


Different entities can realise the same system at once.

E.g. Different orchestras can play the same symphony at the same time.


One entity can realise different systems over time.

E.g. A caterpillar mutates into a butterfly.

The identity of the entity (its DNA) remains the same

The nature of the system changes, meaning its structures and behaviors change.


One entity can realise different systems at once

E.g. You may be an employee, a singer, a computational engine and a heat generator.

All the while being also a biological system of systems: circulatory, respiratory, digestive, etc.


An entity has continuity of identity - meaning its DNA, name or other identifier remains the same.

E.g. Any named social entity in which actors choose behaviors to reach goals, their own or shared.

And as a legal entity, IBM is continuous.


A system has continuity of roles and rules - meaning its behaviour is regular or repeatable.

E.g. Any social system in which actors realise roles and rules.

As a socio-technical system, or a container of systems, IBM changes.


IBM is an ever changing entity in which stuff happens.

If there is no description of the entity as a system, there is no testable system, just stuff happening.

To be called a system, a concrete entity must exhibit (manifest, instantiate, realise) the properties of an abstract system.

There must be a system description (a type or theory) that the entity conforms to – near enough.

System stability

In the ever-unfolding process that is the universe, a system is a transient island of stable and orderly behavior.

The stability of systems – both natural and designed - is central to human existence.

We expect traffic control systems, elevator systems, electricity systems, medical systems, voting systems, insurance systems, pension systems, package delivery systems, payroll systems, tennis matches and church services to be stable.


Stable does not mean static, since activity systems are dynamic.

It means system roles and rules are stable for a generation.

A system is stable if its roles and roles are only changed under change control.

And/or, it is stable for as long as it passes system tests deemed as definitive of the system.


Consider the structures and behaviors of the solar system.

The structural roles (the sun and the planets) and behaviors (the planet’s orbits) are stable.

At least, they are stable enough to match an astronomer’s description of them, for eons.


A concrete entity is a system where and in so far as it realises an abstract system description.

Note that the match of a concrete system to an abstract system may be fuzzy.

The match only needs to be close enough to pass whatever tests we consider to be decisive.


Stable doesn’t mean unchanging.

It only means we expect system mutations to happen in discrete steps.

In business systems, so-called “continuous improvement” is typically managed in discrete cycles (as discussed later).

System change types

Evolutionary biology plays two roles in these papers.

It explains why animals retain mental models of the world, which helps us to answer questions about the description-reality dichotomy.

It also helps us distinguish system state changes from system mutations.


“The word "change" if applied to [a real world entity repeating a behavior] can refer to two very different things.

·         change from state to state, which is the machine's behavior, and which occurs under its own internal drive, and

·         change from transformation to transformation, which is a change of its way of behaving, and occurs at the whim of the experimenter or some other outside factor.

The distinction is fundamental and must on no account be slighted.” Ashby 1956


In other words, one should not confuse system state change and system mutation.


1) The state of an activity system may change.

This holds true in all systems modelled using Forrester's System Dynamics.

Each stock in a system model may grow, shrink or even be exhausted.

Similarly, the state variables (temperature, salinity, etc) of a biological entity vary.


2) The roles and rules of a system are fixed for a system generation.

This holds true in all systems modelled using Forrester's System Dynamics.

The roles of stocks, and the rules governing flows between stocks, are fixed for a generation.

Similarly, the form and functions of a biological entity are encoded for a generation in its DNA.


3) Changing the roles or rules makes a new system version, or a new system.

Changing roles and rules makes a different system - or at least, a new system version or generation.

E.g. In Forrester's System Dynamics, a model of stocks and flows can be changed, and model rerun to see how its outcomes differ.

In Darwin's theory of evolution, the form and functions of a species changes (very slightly) as its DNA changes from generation to generation.


System state change

System state change: a change to the state of a system, which changes the value of at least one variable.

State changes happen during the regular processes of the system.

E.g. winning a set in a tennis match; or the changing positions of planets in their orbits.


Early system theorists were especially interested in homeostatic systems.

The studied dampening feedback loops that maintain system state variables (e.g. body temperature) in a desirable range. 

System theorists called system state changes adaptations, meaning they adapt the entity so as to maintain homeostasis.


We use the term state change instead, because a general system theory has to cover other kinds of system.

E.g. a system that aims to increase a variable, such as income, profit, or happiness.

And any information system that updates state variable values to reflect changes in a reality of interest.


System state change varieties include the following.

·         Information update – the process by which a system’s information state is changed to reflect the state of its environment.

·         Homeostasis – the process by which a system responds to internal and external state changes so as to maintain its state variables in acceptable ranges.

·         Autopoiesis – the process by which a biological cell, given simple chemical inputs, sustains/replicates its own complex chemistry.


The dynamics of a system are how the state of the system changes over time.

Information updates may happen in discrete steps, in response to discrete events.

By contrast, homeostatic and autopoeitic state change may happen continuously, or in such small steps it is regarded as continuous.


Discrete (or digital) system state change

This means that a system’s state advances incrementally in response to discrete events.

The term digital is used to describe signals that are chunked into discrete units, as in a clock that displays the time as numbers. 

Most business systems are driven by discrete events and experience discrete state changes.

So they are modelled as what is called discrete event-driven systems (DEDS).


Continuous (or analogue) system state change

This means that a system’s state advances continually in response to continuous forces or inputs.

The term analogue is used to describe signals that vary continuously, as in a clock with revolving hands.

However, continuous systems can be modelled as discrete event-driven systems.

“We shall assume that the change occurs by a measurable jump, as the money in a bank account changes by at least a penny.

consideration of the case in which all differences are finite loses nothing; it gives a clear and simple foundation;

and it can always be converted to the continuous form if that is desired.” Ashby 1956

System mutation

System mutation: a change to the nature of a system, which changes the type of at least one variable or behavior.

Mutations happen when the processes of the system are changed

E.g. The Lawn Tennis Association publishes changes to the laws of tennis.

Or an asteroid knocks a planet out of its orbit.


System mutation varieties include the following.

·         Reproduction with modification - the process that creates a new, different, descendant system (generation N + 1).

·         Maturation – the process by which seeds or eggs develop into adult forms.

·         Learning – the process by which intelligent actors (animal or machine) respond differently to a new input after recognising its resemblance to past inputs (implies fuzzy logic).


Reproduction with modification happens in clearly identifiable discrete steps, each system generation replacing the last.

By contrast, maturation and learning may happen continuously, or in such small steps it is regarded as continuous.


Meta system: a system that transforms a system from one generation to the next.

Meta systems include biological evolution, which depends on the process of sexual reproduction and DNA to define and change an organic system.

And system architects who use some kind of methodology to define and change systems.


Meta System


System roles

<define>                        <idealise>

System architects  <observe and envisage>  System actors



System actors  <perform>  System Roles


Activity systems are often called transformations.

-1- At the operations level: system actors perform roles to <transform> business inputs into outputs.

-2- At a meta level: system architecture functions <helps managers to transform> system roles.

-3- At a meta meta level: methodologists <help managers to transform> system architecture functions.


System actors can act as architects of the system they work in.

Actors in a given organisation structure may change their roles and processes.

Actors performing given processes may change their organisation structure.

When doing this, when changing the system they work in, they are actors in the meta system.

Discrete system mutation

Discrete system mutation: a step change from one system generation to the next.

The process of Darwinian evolution can be separated from biology.

And can be separated from the processes that sustain a system during its life time.

It is an inter-generational process.

It reproduces system version N with small modifications in system version N+1.

The better that system N+1 is fitted to its environment, the more likely it will be reproduced again.


To change a biological organism is to change its DNA.

Discrete-step generational system change is the natural course of events in biology.


To change a System Dynamics model is to change its stocks and flows.

Discrete-step generational system changes are made by the system modellers to improve the model.


To change a business system is to change its roles and rules.

Change requests may come from owners, sponsors, observers, designers and actors in the system.

In business, so-called “continuous improvement” is typically managed in discrete cycles

The cycle is sometimes called PDCA (plan–do–check–act/adjust), the Deming circle/cycle/wheel, the Shewhart cycle, or the control circle/cycle.


The purpose of an enterprise architecture function is to design and plan discrete-step mutations to business systems.

The enterprise function is a meta system to the business systems it observes and envisages.


How to distinguish descendants from ancestors?

Most parents give children different names; however, the veteran golfer Davis Love III is now a caddy for Davis Love IV.

Version numbering usually implies ancestors will eventually be replaced by descendants.


The ancestor system

the descendant system is named

or else

is replaced by a descendant

using the same name

the same name with a new generation or version number

continues in parallel with a descendant

using a new name


Divergence of descendants into different species

One ancestor system may give rise to two or more descendent systems at generation N+1.

The two descendant systems will likely be regarded as relatives or members of the same species.

Successive generation changes will likely lead to increasingly diverse descendant systems.

Eventually the descendant systems will likely diverge so far that they can no longer be recognised as relatives or members of the same species.


“Since offspring tend to vary slightly from their parents,

mutations which make an organism better adapted to its environment will be encouraged and developed by the pressures of natural selection,

leading to the evolution of new species [eventually] differing widely from one another and from their common ancestors.”

Continuous system mutation

Continuous system mutation: this appears to be an oxymoron – an unstable system

It undermines the concept of system in the normal meaning of the term.

Rather than a system, it is an ever-unfolding process, like the universe as a whole.


The term complex adaptive system is widely used in sociological and management science circles.

And many would say IBM is a complex adaptive system, but it is difficult to square this with the idea of a system above.


Complex? How to detect or measure IBM’s complexity?

The term “complex system” implies an objective measure - derived from counting system elements.

Certainly, every social or business entity is complex in the sense that its actors are complex individuals.

But the internal complexity of atomic actors is ignored in a system description.


Adaptive? How to detect or measure IBM’s adaptations?

If actors continually change their roles, or behave in ad hoc manner, then we have no stable system.

What see is an unpredictable process, not a describable and testable system.


System? Or merely a social entity?

If a social entity changes continually there is no stable system; the ink never dries on a system description.


On self-organising dynamics

Our natural intelligence depends on the brain holding memories in brain state.

Moreover, it depends on mysterious self-organising processes that continually restructure that state.

(An artificial intelligence that mimics that might be a century away.)


Drawing a brain/society analogy doesn't get us far.
The parts of a brain cannot leave it, observe how it works, change it, and join other brains.

The actors in a social system can leave it, observe it, change it, and join other social systems.


Some system thinkers presume a system has self-organizing dynamics.

“Second-order cybernetics, is the recursive application of cybernetics to itself.

It was developed between approximately 1968 and 1975 by Margaret Mead, Heinz von Foerster and others [including Bateson].

Von Foerster referred to it as the cybernetics of "observing systems" whereas first order cybernetics is that of "observed systems".

It is closely allied to radical constructivism, which was developed around the same time by Ernst von Glasersfeld.

Its concerns include epistemology, ethics, autonomy, self-consistency, self-referentiality, and self-organizing capabilities of complex systems.

It has been characterised as cybernetics where "circularity is taken seriously".” Wikipedia 24/02/2018


Observation: Second order cybernetics treats systems actors as system thinkers.

But the idea that actors continually modify the roles and rules of a system they work in undermines system theory.

How to maintain the integrity of the system concept?

How to extend classical system theory to embrace second order cybernetics?


1.      Distinguish a social entity from the many social systems it can realise.

2.      Separate meta systems (in which actors are system thinkers) from operational systems (in which actors are workers).

3.      Allow actors in a social entity to switch between roles in systems and meta systems.

4.      Allow actors to make incremental (generation-by-generation) rather than continual changes to system roles and rules.


Thus, it is the social entity (not the social system) that has self-organizing dynamics.


If there is no system stable enough to be described and tested, there isn’t really a system in the normal meaning of the term.

If we say IBM is a complex adaptive system, the assertion may be true only in a circular sense.

The implication is that a complex adaptive system is something like IBM.

It might better be called a “complicated continuously mutating entity”.

Similarly a “resilient adaptive self-organising system” might better be called a “resilient continuously evolving self-organising entity”.

Conclusions and remarks

It vital to distinguish two kinds of change.

System state change or actor change: changing the values of system state variables; changing the actors that play roles

System mutation or role change: changing the variables themselves; changing the roles or behaviors that update system state.


The following three notions are central to modelling activity systems.

1) The state of an activity system may change.

2) The roles and rules of a system are fixed for a system generation.

3) Changing the roles or rules makes a new system version, or a new system.


A system classification based on system properties

Another paper on the web site concludes with a table that maps system properties (defined in several sources) to a classification of system types.

This work makes no assertion about the usefulness of this classification.

It is only a vehicle for helping people to appreciate the breadth of system varieties.


A classification of system types

Discrete entity

Entity that cannot be described as a system, because it is disorganised, unstable or continually mutating

Entity describable as a system

System boundary


Inter-related components

A passive structure of inter-related items; it may be acted on, but does not itself act.

Activity system

Orderly or rule-bound behavior

Naturally-evolved system

does not depend on description by actors

Natural machine (e.g. solar system)

Organic entity (e.g. tree or cat)

Natural social system (e.g. bee hive, hunting party)

Designed system

depends on description by actors

(symphony, business or software system)

Closed system (can be modelled using System Dynamics)

Open system

Input/output exchange across boundary

Footnote: more about biological organisms

How do organisms stay the same?

The biologists Maturana and Varela characterised living entities as autopoietic (think, self sustaining).

An autopoietic organism manufactures its own body parts from primitive edible chemicals.

The processes make the structures which perform the processes which make the structures which...


How do organisms change?

There is no one definitive list of what characterises a life form, but there are similarities between lists in different sources


According to this source

most living entities

have 7 characteristics

According to this source

living entities

share 8 characteristics

System Theory




System description


Adaptation through evolution

System state change



System mutation


Growth and development

System state change


Cellular organization

System composition


Material input



Material processing


Material output


Information input


Response to stimuli

Information processing



Information processing


It is curious that nobody mentions decay and death (or taxes) as characteristics that define a living entity!


From a system theory perspective, these characteristics are interesting.

Sensitivity to stimuli - the entity can detect changes in the state of its environment.

Response to stimuli – the entity responds to events, typically by triggering some motor actions.

Homeostasis – the entity responds to internal and external state changes so as to maintain system state.

Reproduction – the entity creates a new generation, a new version, of the system.



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