Stability and change - in entities and systems

Copyright 2017 Graham Berrisford. One of about 300 papers at Last updated 11/10/2017 18:45


The paper sets out to reintroduce, reinforce and elaborate on the understanding of a "system" that W Ross Ashby set out in his 1956 book.

Not least, because many modern systems thinkers seem unaware of it.

And so, they have taken to speaking of any entity as a system without any reference to, or agreement about, what that system is.


Much systems thinking discussing blurs the distinction between an entity and a system.

This paper explores the distinction by way of discussing the concepts of stability and change.

It acts as preface to each of several other papers, which you can find via these links.

Ashby’s ideas   Introducing general system theory   Marxism, systems thinking and EA   Introducing systems thinkers   Systems thinking approaches  



Origins of systems thinking. 1

Distinguishing systems from entities. 4

Examples of the entity-system relationship. 5

System change. 5

Definitions of system change terms. 7

Evolution with modification. 8

Categories of system change. 9

“Complex adaptive systems”. 10

Footnote: A system classification based on system properties. 11


Origins of systems thinking

What people call “systems thinking” is often said to have started in sociology.

However, systems of regular behaviors clearly existed before human life evolved.


Our solar system

In retrospect, there were describable systems in nature before life on earth.

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

Astronomers observe the stable and orderly behaviors of the solar system.

Stable doesn’t mean static; the solar system is dynamic.

Stable means the system’s 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 a while.


Astronomers’ definition of the solar system is an entity in its own right, which has changed over the centuries.

The description is realised by particular concrete entities we can observe in the sky.

The abstract system description is static, but the concrete system realisation is dynamic.


A bio-social system

There were describable systems in biology before human kind evolved.

W. Ross Ashby (1903-1972) was a psychologist and systems theorist.

“Despite being widely influential within cybernetics, systems theory… Ashby is not as well known as many of the notable scientists his work influenced.

W Ross Ashby was one of the original members of the Ratio Club, who met to discuss [system theory] issues from 1949 to 1958.” Wikpedia in 2017


Biology is abundant with examples of dynamic systems, both inside one organism and in wider bio-social contexts.

Ashby reported this description of a bio-social system.

“The male and female three-spined stickleback form… a determinate dynamic system.

Tinbergen (in his Study of Instinct) describes the system's successive states as follows” Ashby, 1956


Ashby’s narrative is easily converted into a table.

The concrete entity described is a particular thing – one pair of sticklebacks acting in the real world.

The table is an abstract description - the typical process realised by many different pairs of sticklebacks.

Reading it left to right, top to bottom you see how each visual information flow triggers the partner to act in response.

Stickleback mating system

The female’s role is to

The male’s role is to

present a swollen abdomen and special movements

present a red colour and a zigzag dance

swim towards the male

turn around and swim rapidly to the nest.

follow the male to the nest

point its head into the nest entrance

enter the nest

quiver in reaction to the female being in the nest

spawn fresh eggs in the nest

fertilise the eggs


The male and female stickleback roles, shown columns of the table, could be parallel swim lanes in a process model.

The actors play those roles by communicating, by sending information, in a visual form, to each other


Ashby’s simple example has several features important to general system theory.


A regular process, a sequence of event-triggered activities that advance the state of the system

Structural role

A description listing the activities an actor is expected to perform


A concrete individual that cooperates with others by playing definable roles in processes


A cooperation, usually enabled by communication, by the sending and receiving of information flows

Passive structure

An object that is acted in or on (nest, eggs).


Read Ashby’s ideas for more of what Ashby said about systems.


Human social systems

There were describable social systems before people designed business systems.

A human social group is a collection of actors who interact, sometimes systematically.

In a hunter-gatherer society, a hunting party might be describable as system.


Human evolution brought two advances that enabled the design of more formal social systems:

1.      the use of verbal languages to convey information in messages

2.      the ability to stores messages and memories in written records.


These advances have helped us to evolve ever more complex legal, religious and political systems.

Sociologists ask questions about the nature of such systems.

What makes it an island of stable and orderly behavior?

How is its behavior controlled? What are the trade offs between:

·         centralisation of control (think of totalitarianism, or a top-down management hierarchy) and

·         distribution of control (think of individualism, a participatory democracy or anarchy)?


Business systems

There were describable business systems before humans computerised some of them.

Business systems have evolved over millennia by formalising social systems.

They formalise actors’ roles – by listing activities expected of them.

A simple business system



Place order

Send invoice

Send payment

Send receipt


A role may be played by many concrete actors, or only one.

Our sun (a concrete individual) is one of many actors that play the role called “star” (an abstract type).

Jeff Bezos (a concrete individual) is the only actor that plays the role called “Amazon CEO” (an abstract type).


Software systems

Our main interest is social, business and software systems in which actors exchange information.

The actors communicate and perform other activities according to messages received and memories retained.

The information exchanged includes descriptions, directions and decisions - and requests for them.

Information and communication appears in all social and business systems.

However, the “Information Age” arrived when humans started to digitise business systems using software.

A software system, described by software engineer, is an abstract descriptive entity that can realised by many different concrete computers.


General system theory

General system theory (GST) is about what systems in different domains of knowledge have in common.

This table shows how generic system elements may be observed in different domains.

Generic system

Our solar system

A biological organ

A business

A software system


Star and planets interact by

Cells interact by

Humans and machines interact by

Objects interact by

Inter-actor flows

gravitational forces in

exchanging materials and

exchanging information and

exchanging information and


orbital motion that

playing roles in processes that

playing roles in processes that

instantiating classes in processes that


maintains the solar system

sustain the body or help it reproduce.

sustain the business or meet other goals

update memories and send messages


Read Introducing general system theory for more GST terms and concepts.

Distinguishing systems from entities

We commonly blur the difference between an entity and a system.

“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 repeating a behavior] and to say "the system is that thing there".

This method, however, has a fundamental disadvantage: every material object contains no less than an infinity … 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


In other words, a concrete entity is only a system where and in so far as it realises an abstract system description, by performing behaviors of interest.






Abstract system description

“Solar system”

Laws of tennis

The score of a symphony

The roles in a radio play

Concrete system realisation

Planets in orbits

A tennis match

A performance of that symphony

Actors playing those roles


The match of a concrete system to an abstract system may be fuzzy (read Knowledge and Truth for more on that).

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


A key differentiator

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

A system has continuity of roles and rules; meaning it continues to behave in conformance to the same abstract structure and behavior types.



In the laws of logic, in "the principle of identity", identity means sameness in every detail of existence.

But here, in "continuity of identity", identity means sameness in name only.

Examples of the entity-system relationship

This table shows one abstract system can be realised by several concrete entities.

And one concrete entity may realise several abstract systems.

This abstract system

May be realised by


Concrete social entity

Concrete mechanical entity

Four-handed card game

John, Jane, Joe and Janis

Four computers

Tennis doubles match

John, Jane, Joe and Janis



One entity can realise several systems over time

Consider the transformation of a caterpillar into a butterfly.

The identity of the entity (represented in its DNA) remains the same, but the system has changed, meaning its structures and behaviors have changed.


One entity can realise several systems at once

Your body contains many major systems: circulatory, respiratory, digestive, excretory, nervous, endocrine, immune, integumentary, skeletal, muscle and reproductive.

Each system can be decomposed; e.g. the integumentary system is composed of the skin, hair, nails, and exocrine glands.

Two or three of the afore-mentioned systems are not essential, could be removed without threatening your life.

At the same time, you can realise a system that is rowing a boat, or flying a kite.


IBM may be seen as the same entity today that it was last year – as is made evident in its name and in legal documents.

But IBM is not the same system today - except perhaps in so far as it continues to realise an unchanged global accounting system.

IBM contains innumerable systems, some duplicating the same function, some in competition with each other, and some might turn out to be a drain on resources.


Several entities can realise the same system over time

One bus company can be replaced by another company operating to the same timetable.

The actors employed in a system may be replaced by other actors playing the same roles.

Different orchestras may play the same symphony, in the same concert hall, on different days.


Several entities can realise the same system at once

Different orchestras, in different concert halls, can play the same symphony at the same time.

All the parallel tennis matches in a tournament realise the same description of a tennis match in the laws of tennis.


In short, simply naming a thing and calling it a system is meaningless.

Any world, biosphere or business can be infinite different systems.

It is as many systems as we can describe and test it as being

With no system description, no system test, there is no system - just stuff happening.

System change

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.


All living things depend on some things being stable, or at least, stable enough, for a while.

And central to modern human existence is the stability of systems – both natural and designed.


Whereas stability implies continuity, change implies a loss or lack of continuity.

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


Early system theorists were especially interested in homeostatic systems - in which dampening feedback loops maintain system state variables in a desirable range. 

The "classical cybernetics" in this and related papers includes other kinds of system, and amplifying feedback loops. 

For example, an objective of a business is usually to amplify some variable: income, profit, health, happiness, whatever.

The state of the system changes, but the nature of the system remains stable.


System state change

Sociologists interested in the stability of a social system have applied the idea of homeostasis, as in a biological organism.

Bear in mind that a general system theory has to cover:

·         homeostatic systems: which maintain concrete state variable values (e.g. body temperature) within a desirable range

·         information systems: which update information state variable values (e.g. the score in a tennis match) to reflect changes in a reality of interest.


System generation change

Sociologists interested in what changes the system(s) of a social entity have applied the Darwinian notion of evolution.

Charles Darwin (1809 to 1882) was an English naturalist, geologist and biologist.

He is well known for his book “On the origin of species”, and other contributions to the science of evolution in biology.

His central idea, often misquoted and misinterpreted, may be expressed thus:

It is not the strongest species, nor the most intelligent, that survives the longest, but one that fits its environment and evolves to fit changes in that environment.


Evolutionary biology plays two roles in these papers.

It explains why animals developed the ability to model the world in their minds, which helps us to answer questions about the description-reality dichotomy.

It helps us distinguish state changes from generational changes.

·         changing a system’s state means performing behaviors that change the value of a variable: e.g. winning a rally to score a point in a tennis match

·         changing a system’s generation means changing the type of a system behavior or variable: e.g. changing the laws of tennis or the scoring system.


Darwin’s idea is not about state changes in the life time of an individual entity.

It is not about how an entity responds to departures from a homeostatic norm, or even how it learns from experience.

His kind of evolution occurs by natural selection of those individuals best fitted to reproduce.

It depends each system generation being a little different from the previous one.

It works by reproduction with modification – this is the process by which a new, different, descendant system (generation N + 1) is created.

Definitions of system change terms

There are two kinds of system change:

·         the system state change that happens during the regular processes of a system (the changing positions of planets in their orbits)

·         the system generation change that happens when those processes change (an asteroid knocks a planet into a different orbit).


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


Early system theorists focused heavily on homeostatic systems, and system state change was called adaptation.

So, we need another term for system generation change – and call it transformation.

The two kinds of change may be further subdivided as shown below.


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

·         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.


(One might add decay – the gradual loss of an entity’s ability to maintain its state, leading to death; this being necessary for a species with limited resources to evolve.)


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

·         Maturation – the process by which seeds or eggs develop into to 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).

·         Evolution by reproduction with modification - the process by which a new, different, descendant system (generation N + 1) is created.


Transformations might be called mutations.

One may maintain records of such mutations, whether a system is natural or designed.

One may design and plan necessary or requested changes to designed systems under change control.

The purpose of enterprise and solution architecture methods is to help people do exactly that.


Meta system: a system that defines a system or transforms it 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 those human actors who perform processes (as in an analysis and design methodology) to define and change a designed system.


These two triangles may help to explain the relationship of a meta system to a system.

Meta system


System architect role

<define>               <idealises>

Methodologists  <observe and envisage> Architects

System roles

<define>                        <idealise>

Architects  <observe and envisage>  System actors

Evolution with modification

Discrete change from ancestor to descendant

Remember the difference between an entity and a system.

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

A system has continuity of roles and rules; meaning it continues to behave in conformance to the same abstract structure and behavior types.


Generational change possibilities include:

·         One entity changes so that it conforms to a different system description, and so realises a different system. E.g. a caterpillar morphs into a butterfly.

·         One entity is superseded by another entity that realises the same system. E.g. one bus company is replaced by another bus company that operates to the same timetable.

·         One entity is superseded by another entity that realises a different system, conforms to a different system description.


What or who prompts a generational system change from ancestor to descendant?

In natural systems, system transformations happen without forethought.

In designed systems, change decisions can be made by owner/sponsors, observer/designers, actors in the system, or a combination thereof.


What or who creates or changes an entity’s identifier?

In biological organisms, reproductive processes create new generation of DNA.

In other cases, whoever creates and uses a name or other identifier is likely to identify a descendant entity along the lines in this table.


When an ancestor system

the descendant system is likely to be identified as

or else

morphs into a descendant

the same entity, using the same name

the same name with a new generation or version number (*)

continues in parallel with a descendant

a different entity, using a new name


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


When a descendant is born, is the ancestor redundant or dead? Not necessarily.

In the biosphere there are limited resources, so ancestors must die to make space for descendants.

In designed systems, ancestors may continue as long as there is a need and resources.

But version numbering usually implies ancestors will eventually be replaced by descendants.

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.”

Categories of system change

Discrete and continuous system state change


Discrete system state change

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

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

Enterprise architects describe such discrete event-driven business systems.


Continuous system state change

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

Some systems (driven by continuous flows of matter, energy or forces) experience continuous changes.

System Dynamics can be used to model continuous flow-driven changes to stocks.


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

“Often a change occurs continuously, that is, by infinitesimal steps, as when the earth moves through space, or a sunbather's skin darkens under exposure.

The consideration of steps that are infinitesimal, however, raises a number of purely mathematical difficulties, so we shall avoid their consideration entirely.

Instead, we shall assume in all cases that the changes occur by finite steps in time and that any difference is also finite.

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

Discrete and continuous system generation change


Discrete system generation change

To transform a species, evolution requires ancestor individuals to be replaced by descendant individuals that behave according to a different description.

To transform a system, system architects describe a new (target, descendant) system to replace an older (baseline, ancestor) system.

Similarly, to transform a System Dynamics model, you must make a discrete generational change to the stocks, the flows, or the rules by which flows change stocks.


Continuous system generation change?

A system is a transient island of stable behavior in the ever-unfolding process that is the universe.

To paraphrase Ashby, one should on no account confuse:

·         planning a discrete generational change to the roles and rules of a system

·         a continuously changing “complex adaptive system”, as discussed below.

“Complex adaptive systems”

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

But it is difficult to square the concept with the idea of a system in more general system theory.


For example, people say IBM is a complex adaptive system.

But what is complexity here? How to measure IBM’s complexity?

What is an adaptation? How to count or measure IBM’s adaptations?

And surely a "complex adaptive system" is simply named "social entity"?

Can you name an organisation or any other social entity that is not a “complex adaptive system”?

Perhaps the assertion is true only in the circular sense that it defines the term by reference to social entities like IBM?


A system?

A church, or IBM, is a social entity with continuity of identity whose roles and rules can change. Is it also a system?

Perhaps yes - if its roles and rules change in discrete steps, so each system generation succeeds another.

But no - if the roles and rules change continually, there is never any stability, and the ink never dries on a current system description.


An adaptive system?

This does not mean adaptation of system state to achieve homeostasis.

It means the social entity changes its roles or rules in the light of changes to circumstances. Is it also a system?

Again perhaps yes – if actors change their roles and rules in discrete steps, so each system generation succeeds another.

But no - if actors change their roles and rules continually, there is never any stability, and the ink never dries on a current system description.


An adaptive system is a social entity with continuity of identity.

Human actors are capable of stepping outside any social system they play roles in.

They are able to act in a meta system to that entity – change its roles or rules.

But if they do this continually or in an ad hoc manner, then the entity is unpredictable process, never a describable and testable system.


A complex adaptive system?

Here, complexity is not an objective measure derived (somehow) from counting roles, rules and relationships (some use the term “complication” for that).

It is rather a subjective assessment of a social entity.

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


Some put it thus: a social system is complex rather than complicated.

When they might more clearly say: subjectively at least, a social entity is more complex than any definable social system it realises.

But the comparison is unhelpful, because these are different things, which are designed and managed in different ways.


Enterprise architecture regards the enterprise as a system, or system or systems.

EA frameworks are designed to help people transform business systems.

To design, plan and govern discrete changes to a business system from one generation to the next.


At the same time, an enterprise is a social entity, or collection of social entities, which are in continual flux.

You may know the name of a social entity, the goals it has been given, and the names of actors in it.

Still, if its roles and rules, structures and behaviors, are continually changing, it is not a system in the sense defined above.

And managing the human actors in a social entity is not the focus of enterprise architecture.

For a few more notes on EA and management science, read Premises of EA.


Read Complex Adaptive Systems for a longer discussion of “resilient adaptive self-organising systems”.

Footnote: A system classification based on system properties

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

A system classification

Discrete entity

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

Entity that can be described 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.

Dynamic activity system

Orderly or rule-bound behaviour

Naturally-evolved system

does not depend on description by actors

Inorganic natural machine (e.g. solar system)

Organic entity (e.g. tree or cat)

Natural social organisation (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


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.



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