Terms and ambiguities in systems thinking discussion
Copyright 2019 Graham Berrisford. One of about 300 papers at http://avancier.website. Last updated 11/05/2019 23:57
We spend most of our time thinking in ways not well-called systems thinking.
They might be called “entity thinking”, “situation thinking”, “analytical thinking”, “creative thinking” or just “thinking”.
To call all of those “systems thinking” would be to overload the term until it means everything and nothing in particular.
So can we pin down what systems thinking is about?
This paper defines some terms central to systems thinking and to discussion in other papers.
The paper on Complex Adaptive Systems explores ambiguities in two academic definitions of that term.
The paper on System thinking ideas used in Agile explores the relevance of systems thinking ideas to agile software development and enterprise architecture.
Here is a first attempt at defining some terms.
· A system, in natural language, is a collection of parts that are related to each other in some orderly way(s).
· Behaviours are processes performed by parts, often called actors or agents, that play roles in a system.
· A soft system is a perspective or description of an entity as a system (Ackoff called this an abstract system).
· Linear means in a straight line or a sequence.
· Deterministic means that a system, in a given state, responds to a given input or event in a pre-determined way (be it natural or designed).
· Coupling is the relating of systems (as subsystems) in a wider system.
· Emergence primarily means the appearance of outputs, effects or state changes that arise from coupling subsystems in a wider system.
· Exceptions occur when actors do not complete actions expected of their roles.
· Complex is a term with numerous definitions (see below).
· Holism means looking at a thing in terms of how its parts join up (e.g. bicycle and rider) rather than decomposing the parts.
· Adaptive can mean system state change (as in homeostasis) or system mutation (see below).
· Self-organisation can mean various things, including growth and self-regulation (homeostasis).
The terms above are often ambiguous or undefined in systems thinking discussion.
They can mean different things in different schools, as this table indicates
The measurable complication of an abstract system description
The un-measurable disorder or unpredictability of a real world situation
System state change – updating the values of system variables
System mutation - changing the roles and rules of the system
Actors playing roles and acting according to rules
A group of self-aware actors who inter-communicate and act as they choose, or a problematic situation
A property arising from coupling subsystems into a large system
Not seen before, new, or surprising.
For more, read on.
This section elaborates on the definitions above, highlighting some ambiguities.
A system, in natural language, is a collection of parts that are related to each other in some orderly way(s).
In most system thinking schools, a system is also dynamic.
In other words, it behaves in ways that involve and change the structural state of the system.
This is true in Ashby’s cybernetics, Forrester’s system dynamics and Checkland’s soft systems
Meadows (after Forrester) defined a system thus:
“A set of elements or parts that is coherently organized and interconnected in a pattern or structure that produces a characteristic set of behaviors."
In short, a system is characterised by what does more than what it is made of – which is occasionally called the primacy of behaviour.
Behaviours are processes performed by parts, often called actors or agents, that play roles in a system.
Ashby put it that cybernetics deals with “all forms of behaviour in so far as they are regular, or determinate, or reproducible.”
In other words, the behaviours of a system are orderly processes that can be described by an observer or designer.
When performed, the processes can be tested because they produce observable outputs and/or internal effects or state changes.
An abstract system is a perspective or description of an entity as a system (Checkland called this a soft system).
One abstract system may be realised by several real world entities (each of which might also do others things).
One real world entity can realise several abstract systems – each defined by taking a different perspective of the entity.
Unfortunately, people use the term system for both abstract systems (types), and concrete/real world entities that instantiate them.
And so, in systems thinking discussions, we often confuse abstract systems with the concrete entities or social networks that realise them.
Ashby pointed out infinite systems can be abstracted from one real word entity (say IBM) or machine (say a motor car).
So, choosing to look at a situation as a system is problematic when discussing it with somebody else who sees it as a different system.
Reconciling different system descriptions is a theme of Checkland’s "soft systems methodology.
Deterministic means that a system, in a given state, responds to a given input or event in a pre-determined way (be it natural or designed).
The response may be to choose one of many actions and/or to complete a complex process.
The choice between options may be made using random or probabilistic rules – which makes the response relatively unpredictable.
Some equate unpredictability with complexity – which is misleading - since even a very simple system can be unpredictable.
Chaotic means disorderly, with no regular or repeated pattern.
Some use the term non-linear as synonym for chaotic; properly speaking it has a different meaning
Linear state change: progress or change over time that is represented in a graph as a straight line.
Non-linear state change: progress or change over time that is represented in a graph as a curve or jagged.
The system of interest can be orderly, yet produce chaotic results.
The system’s behavior in response to one event may be deterministic and predictable.
Yet the long-term behavior - the trajectory of state changes over time - may be chaotically unpredictable.
Coupling is the relating of systems (as subsystems) in a wider system.
Read this paper on system coupling varieties for discussion of coupling varieties, or design patterns.
Emergence primarily means the appearance of outputs, effects or state changes that arise from coupling subsystems in a wider system.
For example, the forward motion of a bicycle and its rider emerges from their interaction; neither can achieve it on their own.
However, the term emergence can instead mean the emergence of order from disorder, or of a system from what seems chaos.
Exceptions occur when actors do not complete actions expected of their roles.
This is common in business systems composed of processes in which human actors play roles.
(People are not well-called the “parts” of any system they play roles in, since they also act outside of their roles.)
The need to design systems with exception handling is a common source of complexity.
Complex is a term with numerous definitions. See below for more.
Holism means looking at something in terms of how its parts join up (e.g. bicycle and rider) rather than dissecting each part.
But how you identify the parts and join them up is only one perspective out of countless possible ones.
So, having a holistic view of a thing does not mean you know the “whole” - all there is to know about the thing and/or its parts.
Adaptive can mean system state change (as in homeostasis) or system mutation.
Some speak of Complex Adaptive Systems (CAS).
Note that a system that adapts by changing state might be a SAS (simple adaptive system) rather than a CAS.
And a system that adapts by mutating (or a “learning organisation”) might be called an EME (ever evolving entity) rather than a CAS.
A continuously mutating entity (or ever unfolding process) is not a system in the ordinary sense of the term.
Because its behavior is not describable and testable as regular, or determinate, or reproducible
Self-organisation can mean various things, including growth and self-regulation (homeostasis).
In sociology, it often means something very different – the process by which actors who play roles in a system define or redefine the roles and rules of that system.
Most social systems thinkers regard a bicycle + rider system as simple.
They regard a car + driver system to be of the same kind.
Some dismiss such systems as "linear" or "mechanistic" or "deterministic".
Social systems thinkers are more interested in what they call Complex Adaptive Systems (CAS).
The trouble is, it isn't clear they agree:
· why they call thing they are talking about a system
· why they call it complex, or how they could measure that
· in what ways they expect it to adapt and
· when if ever they would consider an adaptation to have changed the system into a different one.
Ashby and Forrester stuck to Bertalanffy’s idea that general system theory should be about general, cross-science, principles.
But much social systems thinking is specific only to human situations.
And there are profound terminology clashes between cybernetics and social systems thinking.
In cybernetics, a system is complex if the system description is complex; the roles and rules are complex
To social systems thinkers, a system is complex if the reality is complex, the actors are complex; their roles and rules may be lightly prescribed, if at all.
In cybernetics, a system adapts to feedback from its environment by changing state – which may be called self-regulating.
To social systems thinkers, a system mutates as actors change its roles, rules or aims - which may be called self-organising.
In cybernetics and system dynamics, a system is a collection of repeated or repeatable activities.
In social systems thinking, a system is a collection of actors, who interact as they choose.
The following sections expand on these ambiguities.
Ashby, Checkland and other systems thinkers emphasise that a system is one perspective of a reality.
And that we must distinguish abstract systems from concrete systems.
“At this point we must be clear about how a "system" is to be defined.
Our first impulse is to point at [some real entity or machine] 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 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)
Suppose you observe a group of people playing a game of cards.
You identify the actors in the group, then work out the roles they play and the rules they follow.
You can distinguish three things.
· The soft or abstract system - the rules of the game - a perspective of the group’s behaviour.
· The real machine or concrete system – the actors distributing and playing their cards.
· The social group composed of actors who play roles in the card game.
The actors in the game are much more than the roles they play in the system.
If your aim is to understand or motivate the actors, you need tools other than "systems thinking".
Suppose you observe a second group of people.
You identify the actors in the group, and perhaps some goals they have in common.
You see they communicate and do stuff, guided by their personal and to-some-extent shared goals.
But you cannot identify any roles they play or rules they follow.
You see what may be called a social group or network, but to call it a system adds no useful meaning.
It seems some in the latest generation of systems thinkers are not clear what a system is.
To say every named group of people is a system is to use the term with no useful meaning.
To say every entity composed of inter-related parts is a system is to say nothing of practical use.
To say every problem and situation we encounter is a system, is to denude the term of its value.
Some use the term CAS to describe entities and situations that are unstable and disorderly.
That is, to describe things which are not systems in either a natural language or a general system theory sense.
It is not a business, problem or situation that requires an "intervention”.
Ackoff defined his system of interest by five conditions, the first being “the whole has one or more defining properties or functions”.
He used the term “property” to mean a state variable and “function” to mean a behavior or an aim.
In general, to Ashby, Checkland and Forrester, the functions are orderly processes - be they purposeful or not.
Complex systems were introduced by Ashby thus:
“Not until… the 1920s… did it become clearly recognised that there are complex systems…
they are so dynamic and interconnected that the alteration of one factor immediately acts as cause to evoke alterations in others, perhaps in a great many others.”
Ashby defined complexity as measure of state variety – meaning the number of different states a system can take.
Others define it differently.
E.g. Snowden defines complexity in a way that is particular to his classification of problematic situations.
Others speak of complex systems with reference to at least five different situations:
1 A complicated orderly system
Consider an organism or software system whose processes are variegated, complicated or convoluted.
It seems intuitively reasonable to call that complex, or relatively complex.
2 An unpredictable situation
Surely unpredictable cannot mean complex.
A situation in which actors must respond to unforeseen inputs in ad hoc ways is not a system at all.
And unpredictable state change patterns can be produced by very simple systems (see next point).
3 Non-linear system state change
A non-linear or convoluted state change pattern does not imply a complex system.
Chaos theory showed us it can result from repeating simple processes.
4 A disorderly situation
Consider for example a war zone, or an uncoordinated, decoupled, set of silo systems.
Surely disorder is chaotic rather than complex? (Disorder is simpler than order).
5 A system composed by coupling other systems
It might seem obvious that coupling two subsystems makes a more complex system.
But only if you are obliged to describe or manage the internals of those subsystems.
Else, you can take the holistic view and ignore the internals of the subsystems.
E.g. a card game can be described as simple system, regardless of whether it is played by people or software.
A system is an abstraction from reality, and most human system designers take human abilities for granted, they are axiomatic.
Often, an entity is called a complex system where one or more of the following are true.
· No measure of complexity has been agreed.
· No level of abstraction has been agreed.
· No quantifiable properties are described, which makes any measure of complexity impossible.
· No description of the entity as a system has been agreed, or even made.
· No description is possible, because the entity changes continually, rather than generationally.
Which is more complex out of communism or capitalism? Too difficult to answer?
OK, which is more complex out of IBM, Microsoft, a chicken and a hen’s egg?
A description of IBM as a receiver of money from customers and sender of money to suppliers is simple.
A description of IBM that included every activity of every employee would be complex beyond imagination.
But then, a description of a hen’s egg that included every sub-atomic particle would be even more complex.
Read our paper on complexity for more.
A system is a logical machine that may be realised by a physical (biological, social or mechanical) thing in the world.
To discuss system change we need to make at least three distinctions.
First, we must distinguish between system state changes (be they linear or chaotic) and system mutations (be they small or large).
In system state change, a system’s property values change.
· A bicycle + rider change state by accelerating, or steering to the left.
· A crystal changes state by growing in a liquid.
· A heater changes state in response to messages from thermostat.
It turns out that the simplest of systems can change state in chaotic and unpredictable ways.
Whatever the state change trajectory looks like, it is an inexorable result of actors behaving according to given rules.
While the state of a weather system may change in non-linear way, the laws of physics do not change.
In system mutation, a system’s property types change, it becomes a different system.
· A bicycle is converted into monocycle.
· A motor car is converted into a boat.
· An entity is replaced by another (as a parent is replaced by a child).
The simplest of systems can mutate, or be replaced by a new generation.
Mutation is creative in the sense that it changes the very nature of a system, from one generation to the next.
Mutation can occur in at least three ways:
· redesign by actors outside the system - as a machine or software system may evolve
· redesign by actors who also play roles inside the system - as a card game may evolve.
· self-replication with changes - as a virus evolves.
Second, we must distinguish between continuous and discrete mutation.
A game of cards is a system in which there are regular, determinate and repeatable processes.
People can’t play a game of cards unless the players agree the rules, at least for the duration of one round.
Provided actors change the system incrementally, and all together, the classical concept of a system is upheld.
Continuous mutation undermines the very concept of a system, since it is disorganising rather than organising.
Instead of seeing an island of stability or order, we see an ever mutating entity that is never describable and testable.
Third, we must
distinguish a system from what (be it an actor or a process) causes it to
Ashby and Maturana said the change agent must sit outside the system of interest; a machine cannot change itself.
For them, “re-organisation” requires the intervention of a higher level process or actor.
E.g. The process of biological evolution runs over and above any individual organism.
And to modify the car + driver system, you play a role in a different system, which may be called car design or psycho-therapy.
Read our paper on Complex Adaptive Systems for more.