A systems thinking vocabulary – with ambiguities

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?

Contents

Systems in general – recap. 1

On abstract and concrete systems - recap. 1

Some terms and concepts. 1

What makes a social system?. 1

A complex adaptive system (CAS)?. 1

What makes a system complex?. 1

What makes a system adaptive?. 1

Systems in general – recap

System: an entity describable as actors interacting in activities to advance the system’s state and/or transform inputs into outputs

· The actors are structures (in space) that perform activities - in roles and processes that are describable and testable.

· The activities are behaviors (over time) that change the state of the system or something in its environment - governed by rules that are describable and testable.

· The state is describable as a set of state variables - each with a range of values.

· An open system is connected to its wider environment - by inputs and outputs that are describable and testable.

These concepts can be seen in writings of Ashby, Forrester and Checkland.

In Ashby’s cybernetics, a system is modelled as processes that maintain a set of state variables.

In Forrester’s system’s dynamics, a system is modelled as inter-stock flows that maintain a set of stocks (variable populations).

In Checkland’s soft systems method, a system is modelled as actors who perform processes that transform inputs into outputs for customers.

Terms are often ambiguous or undefined in systems thinking discussion.

They can mean different things in different schools, as this table indicates

Term	School	Meaning
Complex	Classical cybernetics	The measurable complication of an abstract system description
Complex	Sociological thinking	The un-measurable disorder or unpredictability of a real world situation
Adaptive	Classical cybernetics	System state change – updating the values of system variables
Adaptive	Sociological thinking	System mutation - changing the roles and rules of the system
System	Classical cybernetics	Actors playing roles and acting according to rules
System	Sociological thinking	A group of self-aware actors who inter-communicate and act as they choose, or a problematic situation
Emergence	Classical cybernetics	A property arising from coupling subsystems into a large system
Emergence	Sociological thinking	Not seen before, new, or surprising.

For more, read on.

On abstract and concrete systems - recap

Ackoff, Ashby, Checkland and others have emphasised that a system is a perspective of a reality.

“Different observers of the same [concrete] phenomena may conceptualise them into different [abstract] systems.” Ackoff 1971

Ashby urged us not confuse a concrete entity with an abstract system that the entity realises.

“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)

A concrete entity is a system only when and in so far as it realises a testable system description.

We do commonly abuse the term “system”.

We point to an entity (e.g. a business organisation or a biological organism) and casually call it "a system".

But with no explicit or implicit reference to a particular system description, this is to say nothing.

Idly calling an entity (or process, problem or situation) a system is meaningless, because one entity can realise countless systems.

Abstract system: is a description or model of how some part of the word behaves, or should behave.

E.g. the standard heart beat is a theory of, or insight into, how some part of the world works.

Concrete system: is a realisation by a real-world entity that conforms well enough to an abstract system.

E.g. your own heart beating is a real-world application, or empirical example, of such a theory.

Science requires us to show the latter conforms (well enough) to the former.

The basis of system theory

Abstract systems (descriptions)

System thinkers <observe and envisage > Concrete systems (realities)

These papers take this triangular, scientific, view of system theory as axiomatic.

Note that the relationship between physical entities and abstract systems is many-to-many.

One abstract system (e.g. the game of poker) may be realised by countless physical entities (countless card schools).

One physical entity (e.g. a card school) may realise countless abstract systems (e.g. poker, whist etc).

An abstract system does not have to be a perfect model of what it describes; it only has to be accurate enough to be useful.

And note that systems thinking hides the full complexity of real-world entities that realise systems.

In discussion and testing of the stickleback mating ritual, no attention is paid to any individual stickleback, or its complex internal biochemistry.

Abstract system	Conceptual	A set of roles and rules (the logic or laws actors follow)	The stickleback mating ritual
Concrete system	Physical	Actors playing the roles and acting according to the rules	Countless pairs of stickleback.

“Since different systems may be abstracted from the same real thing, a statement that is true of one may be false of another.

… there can be no such thing as the unique behavior of a very large system, apart from a given observer.

… there can be as many systems as observers… some so different as to be incompatible.

… studying only carefully selected aspects of things is simply what is always done in practice.” (Ashby 1956, 6/14)

Unfortunately, most of us use the term system for both abstract systems (types), and concrete/real world entities or social networks that instantiate them.

And so, in systems thinking discussions, we often confuse them.

But remember, an abstract system may be realised by several real-world entities - each of which might also do others things

And one entity might realise other abstract systems – each defined by taking a different perspective of the entity.

Reconciling different abstract or soft systems is a theme of Checkland’s "soft systems methodology”.

Some terms and concepts

System: an entity describable as actors interacting in activities to advance the system’s state and/or transform inputs into outputs

· Abstract system: (e.g. the normal regular heart beat) a description or model of how some part of the word behaves, or should behave.

· Concrete system: (e.g. your own heart beating) a realisation by a real-world entity that conforms well enough to an abstract system.

· Natural system: a system that runs before it is envisaged as a system.

· Designed system: a system that is envisaged before it runs.

· Closed system: a system defined (as in a System Dynamics model) without reference to its wider environment.

· Open system: a system defined as consuming/delivering inputs/outputs from/to its wider environment.

Atomic element: a system element that is not further divided in a description.

Behavior: a service or process that changes the state of a system or something in its environment.

In most system thinking schools, a system is dynamic, meaning 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

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.

After Forrester, Meadows 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.

This principle is occasionally called the primacy of behaviour.

Alternatively, the term behavior can refer to the external appearance of a system.

That is, to how a system is observed to change state over time - its state change trajectory.

Chaotic: 1) disorderly, random, with no regular or repeated pattern. 2) unpredictable outcomes arising from variations in initial conditions. 3) non-linear state change trajectory.

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.

Linear: in a straight line or a sequence.

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 oscillating, a curve or jagged.

Complex: a term with scores of different definitions.

A system that is complex in one way may be simple in another.

It may be complex internally and/or appear complex externally

Internally, there may be a wide variety in its variables, its roles or its rules.

Externally, there may be complex convolutions in the observable trajectories of state variable changes.

See below for more discussion of complexity.

Coupling: the relating of subsystems in a wider system by flows.

For discussion of coupling varieties, or design patterns, read this paper on system coupling varieties.

Deterministic system: a system that processes an input, with respect to its memory/state, to produce a result that is predictable.

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.

Emergence: the appearance of properties in a wider or higher-level system, from the coupling of lower-level subsystems.

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 has been used to mean many other things.

E.g. the emergence of order from disorder, the emergence of a system from what seems chaos, or the emergence of system mutations by evolution or design.

And “The concept has been used to justify all sorts of nonsense.” Gerald Marsh.

For more on emergence, read “Emergence”.

Event: a discrete input that triggers a process.

Evolution: a progression of inter-generational system mutations that change the nature of a system.

Exception: what happens 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.

The need to design systems with exception handling is a common source of complexity.

Flow: the conveyance of a force, matter, energy or information.

Hierarchy: the successive decomposition of a system into smaller subsystems or a behaviour into shorter behaviors.

Holism: looking at a thing in terms of how its parts join up rather than dissecting each part.

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.

Since how you identify the parts and join them up is only one perspective out of countless possible ones.

Holistic view: a description of how parts relate, interact or cooperate in a whole.

Feedback loop: the circular fashion in which output flows influence future input flows and vice-versa.

Information flow: the conveyance of information in a message from a sender to a receiver.

Information quality: an attribute of a flow or a state, such as speed, throughput, availability, security, monetary value.

Information state: the information retained in a memory or store.

Information: any meaning created or found in a structure by an actor.

Learning: the processes by which an organism or AI machine remembers past events and responds to differently to new events (implies pattern recognition and fuzzy logic).

Linear: in a straight line or a sequence.

Meta system: a system that defines a system or changes it from one generation to the next.

Organicism: the idea that systems are describable at multiple hierarchical levels.

Process: a sequence of activities that changes or reports a system’s state, or the logic that controls the sequence.

Reductionist view: identifying the parts of a whole, naming or describing parts without considering how the parts are related in the whole.

Stochastic system: a system that processes an input, with respect to its memory/state, to produce a result that (due to some randomness) is predictable only with a degree of probability.

System environment: the world outside the system of interest.

System boundary: a line (physical or logical) that separates a system from is environment, and encapsulates a system as an input-process-output black box.

System interface: a description of inputs and output that cross the system boundary.

What makes a social system?

Two kinds of social group may be distinguished.

Social network: a group of actors who communicate with each other directly or indirectly.

Social system: a system that is realised by a social network.

Social cell: a social network in which actors find that realising the roles and rules of a particular social system is so attractive they resist any change to it.

Although any human society or business might be describable as mix of networks and systems, we surely need to be clear which we are talking about.

Suppose you observe a group of people playing a game of cards.

You identify the actors in that social network, 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 network - composed of actors who meet to play roles in the card game.

The actors in the social network are much more than the roles they play in the game of cards.

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 a social network who talk to each other, 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 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 (or social network) 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 complex adaptive entity 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.

If that thing is a business, problem or situation that requires an "intervention”, you may need some social network thinking as well as systems thinking.

A complex adaptive system (CAS)?

Social systems thinkers often speak of a Complex Adaptive System (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 an adaptation changes one system into a different one.

Complex?

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.

Adaptive?

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.

System?

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.

What is a complex adaptive system?

How about a simple system that adapts by changing state: a simple adaptive system (SAS)?

E,g, a bicycle + rider system, or a motor car + driver system.

Social systems thinkers tend to dismiss such systems as "linear" or "mechanistic" or "deterministic".

How about an entity that mutates continually: a continuously evolving entity (CEE)?

E.g. IBM, or any informal human society.

A continuously evolving entity is not a system in the ordinary sense of the term.

It is rather an ever-unfolding process, whose behavior is not describable and testable.

The following sections expand on these ambiguities.

What makes a system complex?

Complex: a term with scores of different definitions.

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.

What makes a system adaptive?

Adaptation: 1) system state change. 2) system mutation.

Adaptive can mean either system state change (as in homeostasis) or inter-generational system mutation.

System state: the current structure or variable values of a system, which change over time.

System state change: a change to the state of a system.

System mutation: changing the roles, rules or variables of a system.

Self-organisation: can mean various things, including growth and self-regulation or homeostasis.

Sooner or later, the environment of a system changes in a way that threatens its survival.

The two kinds of change below are often confused in systems thinking discussion.

And many social systems thinkers apply the terminology of system state changes to system mutations.

System state change

System state change (within a system generation) is a change to the values of a system’s state variables (e.g. body temperature).

To change Ashby’s system state is to update its variable values in response to an event or condition.

To change Forrester’s system state is to change the quantity of a stock in response to an inter-stock flow.

State change examples:

· A bicycle + rider system - changes state by accelerating, or steering to the left.

· A crystal system - changes state by growing in a liquid.

· A heater system - changes state in response to messages from thermostat.

Tools for System Dynamics show the trajectory of quantitative variable value changes (up and down) in a graph.

A graph may show the values of a system’s quantitative state variables

· steadily increase or decrease over time

· dwell for a while in a stable state (an attractor), then move to another stable state

· change in a non-linear, jagged or “chaotic” way.

It turns out that the simplest of systems can change state in a non-linear or chaotic way.

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.

Some describe a system with a non-linear state or chaotic state change trajectory as a complex system.

But while that trajectory may look complex, the system itself may be very simple and orderly.

System mutation

System mutation (between system generations) is a change to the nature of a system.

To change Ashby’s system is to change its variable types or processes.

To change Forrester’s system is to add/remove stocks or change inter-stock flows.

System mutation examples:

· 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).

To change the roles or rules of a concrete system is to change the abstract system it realises.

This change may be observed in differences in how the system works or what it produces.

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.

Three distinctions related to system change

First, we must distinguish between system state changes (be they linear or chaotic) and system mutations (be they small or large).

Within a generation, by homeostasis or reconfiguration, a system can adapt to changes; let us call that an agile system.

Between generations, by evolution or redesign, a system can be adapted to changes by a higher-level process or entity.

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 whatever entity or a process causes it to mutate.

Self-organisation can mean various things, including growth and self-regulation or 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.

Some social systems thinkers consider it their mission to promote this idea, and the notion of a “participatory democracy”.

Ashby and Maturana said a machine cannot change itself, the change agent must sit outside the system of interest.

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.