System stability and change

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Copyright 2017 Graham Berrisford. One of about 300 papers at http://avancier.website. Last updated 13/10/2018 16:46

 

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.

And history repeats - some conditions and reactions to them recur – which is symptomatic of a system.

 

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

The conclusion is that it is vital to distinguish two kinds of change.

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

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

And three principles are central to modelling activity systems are outlined.

Contents

Differentiating systems from entities. 1

Two kinds of system change. 4

Two kinds of system state change. 5

Two kinds of system mutation. 6

Conclusions and remarks. 9

Footnote: on stability and change in biology. 10

 

Differentiating systems from entities

Often, a social or business entity is called a “complex adaptive system”.

Which begs three questions: What do “complex”, “adaptive” and “system” mean?

This paper primarily addresses what it means to adapt or change.

But first, what is a system?  Is every entity also a system?

 

In short:

·        entities are structures that persist

·        systems are entities that exhibit orderly behaviors

·        concrete systems are entities that realise abstract systems

·        one entity can realise several systems

Entities are structures that persist

Entities are things we identify and perceive as existing in space and time.

An entity may be seen as a structure in space, and its structure may be decomposed into parts

E.g. an axe may be decomposed into a handle and a blade.

And the organisation structure of an enterprise (say, IBM) may be decomposed down to the level of parts that are human actors.

Ultimately, every part in the structure of an entity may be decomposed down to the level of sub-atomic particles.

 

Note that an entity may gain, lose or replace parts over time.

So it is not well defined in terms of its parts at one particular moment.

 

An entity also has continuity of identity over time.

During its life history, an entity may move through space and be modified.

E.g. A planet (say Mars) has continuity of identity to astronomers as it moves through space.

A bicycle ride is identifiable as a pairing of a rider and a bicycle.

A biological organism has continuity of identity both to observers and in its DNA.

A social entity (say a choir, or a marriage) is an entity we can identify and observe over time.

Any legally-constituted business (say IBM) is an entity we can identify and observe over time.

Systems are entities that exhibit orderly behaviors

If every entity is a system, the term “system” has no value.

W Ross Ashby said the systems of interest are entities that exhibit behaviors over time.

To be called a system, an entity must exhibit behaviors that are “regular, or determinate, or reproducible.”

 

The systems of interest here are islands of orderly behavior in the ever-unfolding process of the universe.

The physical parts of the system may be replaced, but its roles and rules persist.

You might think of the solar system, riding a bicycle or the cardio-vascular system.

A particular interest is social and business systems in which entities act in response to information encoded in messages and memories.

 

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

We expect at least temporary stability in the observable behavior of

·        banking services

·        church services.

·        electricity supply

·        elevators

·        health care operations

·        package delivery operations

·        payroll operations

·        tennis matches

·        traffic control systems

·        voting systems.

 

Stable does not mean static - all the above are dynamic activity systems

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

At least, long enough to pass whatever system tests are deemed as definitive of the system.

 

E.g. The structural roles (sun and planets) and behaviors (planetary orbits) of the solar system are stable for eons.

At least, long enough to match an astronomer’s description of them.

 

Stable does not mean unchanging   the roles and roles of the system can be changed.

It means only that system mutations happen in discrete steps.

In business systems, so-called “continuous improvement” is typically managed in discrete cycles – under change control.

Concrete systems are entities that realise abstract systems

System theorists distinguish abstract system descriptions from concrete entities that instantiate (realise) them.

W Ross Ashby stressed that systems are (must be) abstracted from concrete entities.

“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

 

Russell Ackoff also differentiated abstract and concrete systems.

He defined an abstract system as a system description in which the elements are concepts.

And defined a concrete system as a real-world system that has two or more related objects or parts.

The triangular graphic below captures the relationship between the two concepts.

 

Systems thinking

Abstract (theoretical) systems

<create and use>                    <are realised by>

System thinkers <observe & envisage>  Concrete (empirical) systems

 

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

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

The match of a concrete entity to an abstract system may be fuzzy.

It only needs to be close enough to pass whatever system tests we consider to be decisive.

 

So, simply pointing to an entity (somewhere in space and time) and calling it a system has no useful meaning.

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

One entity can realise several systems

One entity can realise many different systems – as many as we can describe and test it as realising.

 

 

Over time

At one time

Different entities

can realise

one system

E.g. One bus company may replace another.

It runs the same buses to the same timetable.

The human actors change.

But the roles and rules of the system remain the same.

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

The players and instruments in each orchestra are different.

But the roles and rules they follow are the same.

One entity

can realise

different systems

E.g. A caterpillar mutates into a butterfly.

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

But its structures and behaviors (the system) differ.

E.g. You may play several roles, act as a heat generator, and

as a “system of systems” (circulatory, respiratory, digestive, etc.)

 

When the actors in social entity realise testable roles and rules, they realise a system.

A socio-technical entity, like IBM, may contain or depend on many such systems.

It is certainly an ever changing entity in which lots of stuff happens, and many systems are realized.

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

IBM realises as many systems as we can describe and test it as realising.

And some of those systems may be in conflict with each other.

Two kinds of system change

Ashby distinguished two kinds of change thus:

“The word "change" if applied to [an 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

 

Ashby’s two kinds of system change might be defined thus:

·        System state change: e.g. regulating the values of defined state variables to stay within a desired range.

·        System mutation: e.g. reorganizing or changing the state variables, or the rules that update variable values.

 

Ashby’s distinction may be expressed in terms of three principles.

 

Principle 1: The state of an activity system may change.

E.g. In Forrester's System Dynamics, the stocks in system model may grow, shrink or even be exhausted.

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

 

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

E.g. In 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.

 

Principle 3: Changing the roles or rules makes a new system or system version.

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

In Forrester's System Dynamics, the model of stocks and flows can be changed, and the 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.

 

In short, it is important not to confuse system state change with system mutation.

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.

 

·        Individual organisms experience system stage changes – as discussed in cybernetics and general system theory.

·        A species undergoes system mutations - via reproduction and evolution.

Two kinds of system state change

This section distinguishes discrete state change from continuous 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.

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

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

 

But not all state changes are homeostatic, or well called “adaptations”.

Some systems continually amplify or increase a variable, such as income, profit, or happiness.

And most information systems update state variable values to reflect changes in a reality of interest.

 

So, system state change varieties include the following.

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

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

·        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 that system changes over time.

State changes may happen in discrete steps, in response to discrete events, or continuously.

 

Discrete (or digital) system state change

This means 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. 

 

Continuous (or analogue) system state change

This means 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.

 

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

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

However, continuous systems can also 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

Two kinds of system mutation

This section distinguishes discrete state mutation from continuous 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

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.

E.g.

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

 

Evolution by discrete step mutation

The process of Darwinian evolution can be separated from biology, and from the processes that sustain a system during its life time.

It is an inter-generational process that 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.

 

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.” http://www.oxforddictionaries.com/definition/english

Continuous system mutation and self-organisation

Above, a system is an island of orderly behavior in the ever-unfolding process of the universe.

The notion of continuous system mutation is an oxymoron; since it undermines that basic notion of a system.

 

Often, a social or business entity is called a “complex adaptive system” where one or more of the following are true.

·        No measure of complexity has been agreed.

·        No quantifiable properties are described, which makes any measure of complexity impossible.

·        No description is possible, because the entity changes continually, rather than generationally.

·        No description of it as a system has been agreed, or even made.

 

Is IBM a complex adaptive system?

That is true in a circular sense, if we define a complex adaptive system as being something like IBM.

But that is to undermine the very concept of a system.

If the entity’s roles and rules are continually modified, there is no describable or testable system.

The entity is an ever-unfolding (and unpredictable) process rather than a system.

Better to call IBM a “complicated continuously mutating entity”.

 

Some speak of social and business entities as self-organising systems, but the same objections arise.

Better to call IBM “a self-organising entity in which actors can change the system(s) they realise”.

 

Some draw an analogy between social entities and human brains, which is misleading.
The actors in a social system can step outside it, observe it, re-organise it, leave it and join other social systems.

The parts of a brain cannot step outside it, observe it, re-organise it, leave it or join other brains.

 

How to extend system theory to embrace “self-organisation”?

The answer is to:

·        Distinguish a social entity (composed of actors) from the many social systems it can realise.

·        Distinguish meta systems from operational systems.

·        Allow actors to switch between roles as rule followers in operational systems and rule definers in meta systems.

 

The trouble is, if the roles and rules of a system are continually modified, there is no describable or testable system.

How to maintain the integrity of the system concept?

The answer is to insist that actors 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.

 

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

Conclusions and remarks

The conclusion is that it is vital to distinguish two kinds of change.

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

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

 

And three principles 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 or system version.

 

Complexity

Note that to measure the complexity of an entity is to measure a description of it.

In a description of IBM’s organisation structure at the topmost level, IBM appears simple.

But a description of IBM at the bottommost level of decomposition would be complex beyond imagination.

A description of a tennis club or an axe at the bottommost level of decomposition would also be complex beyond imagination.

To compare the complexity of two entities requires that its parts are described at the same level of abstraction (from the bottom up).

Having said that, one should measure the complexity if behaviors as well as structures.

And there is no widely agreed measure of complexity.

 

A system classification

Another related paper 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

Wholeness

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: on stability and change in biology

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

Various system theory concepts can be found in the two lists below.

It is curious that neither source below mentions decay and death (or taxes) as characteristics that define a living entity!

 

According to this source

most living entities

have 7 characteristics

According to this source

living entities

share 8 characteristics

System Theory concepts

 

Heredity

System description/definition

 

Adaptation through evolution

System mutation

Reproduction

Reproduction

System mutation

Growth

Growth and development

System state change

 

Cellular organization

System composition

Nutrition

System input (material)

Sensitivity

System input (information)

Excretion

System output (material)

Respiration

Metabolism

System processing (material)

Movement

Response to stimuli

System processing (information)

 

Homeostasis

System processing (information)

 

A species undergoes system mutations - via reproduction and evolution.

Individual organisms experience system stage changes – as discussed in cybernetics and general system theory.

 

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 (repeat).

 

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