The philosophy of systems

Copyright Graham Berrisford 2014. Last updated 09/08/2019 18:33 One of a hundred papers on the System Theory page at



Enterprise architecture is much about the consistency and coherence of a business's systems with respect to its aims.

But we long ago learned complete consistency and coherence is beyond us.

There are too many (overlapping) "bounded contexts" and "domain-specific languages".

A statement that is true in one of them may be untrue in another.

Does this mean all knowledge and truth exist only relation to particular culture, society, or historical context?

This paper develops a philosophical position statement summarized at the end.

It does this by relating system theory to science and philosophy, with passing references to Nietzsche and Wittgenstein.


System theory. 1

Perception and knowledge. 1

Communication. 1

Truth. 1

A scientific perspective. 1

A mathematical perspective. 1

The reality (not ethereality) of types. 1

A philosophical position statement for systems thinkers. 1

A table of philosophical dichotomies. 1

Footnote on the evolution of concepts and numbers. 1



System theory

Ashby, Ackoff, Checkland and other systems thinkers emphasise that a system is a perspective of a reality.

They distinguish abstract and concrete systems.


The basis of system theory

Abstract systems (descriptions)

<create and use>                              <represent>

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


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

A concrete system (in reality) is anything that conforms well enough to an abstract system (in description).

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


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

E.g. we may describe planetary orbits as ellipses - ignoring general relativity, and other planets

Occasionally, we do presume an abstract system is a perfect model.

E.g. we presume the general formula for the area of a circle will work every time we use it.

And the code of a computer program perfectly represents every run-time execution of that code.


Perception and knowledge

A sensor is a (biological or technological) machine that can perceive some features of a reality and form a sensation.

The sensation or perception is a model (image, description, or other representation) of those features. 

It is not the actual features; however, the accuracy of the model can be tested.


When we perceive a thing, we form an internal model of it.

According to this Anil Seth talk, research suggests the brain does this by combining

·       Observation: sensing information input from what is out there.

·       Envisaging: making a best guess as to what has been sensed, with reference to what is expected


What we expect to see is not purely fanciful - invented out of nothing.

It is what a mix of inheritance and experience predicts is likely to be true.

Thus, the brain optimises its matching of perception and experience.

Else, it would have the hopeless task of analysing each perception from scratch.


A perception can only model a thing - otherwise it would be the thing.

That does not mean (as Seth implies) that the thing does not exist, or that perception is hallucination.

The existence of humankind depends on the presumption that:

·       things exist out there

·       our perceptions and memories of those things are useful models of them and

·       we can share those models by translating them into and out of messages.


We may sometimes hallucinate - perceive something where there is nothing.

A mental model may be a poor representation, it may fade to nothing.

But still, our brains are designed or evolved to perceive what does exist out there.

And our survival depends being able to do this reasonably well, most of the time.


To know a thing is to have access in our thoughts to a useful model or representation of it.

We can never know – perfectly - what a thing is; that is not even a meaningful suggestion.

We can only know a thing as it is represented in some kind of model, description or theory.


Moreover, we can share our knowledge with others.

And we can test that things do turn out in the way our knowledge leads us to predict.

To deny that sharing and testing help to confirm our knowledge of the world would be to deny the history of mankind.


Relativism and perspectivism?

Does all knowledge and truth exist only relation to particular culture, society, or historical context?

For sure, biological differences mean people perceive the world a little differently from each other.

And people see the world somewhat differently from how birds, bats and bees see it.

But more importantly, their conceptualisations are shaped by objectively testing them against reality.


Historical figures including Protagoras, Nietzsche and von Foerster have subscribed to a kind of relativism or perspectivism that can be misleading.

Friedrich Nietzsche (1844 to 1900) was a philosopher whose metaphysical ideas influenced many Western intellectuals.

“Nietzsche claimed the death of God would eventually lead to the loss of any universal perspective on things, along with any coherent sense of objective truth.

Nietzsche rejected the idea of objective reality, arguing that knowledge is contingent and conditional, relative to various fluid perspectives or interests.

This leads to constant reassessment of rules (i.e., those of philosophy, the scientific method, etc.) according to the circumstances of individual perspectives.

This view has acquired the name perspectivism.” Wikipedia December 2018


Protagoras, Nietzsche and von Foerster have a lot to answer for, as discussed in Postmodern Attacks on Science and Reality.

Some Marxists and postmodernists interpret perspectivism as meaning all descriptions of the world are subjective, and perhaps, therefore, equally valid.

At the extreme, this leads to the view that the “dialectic” is more important than evidence.

That any persuasively argued or widely believed assertion carries the same weight as science.

Or even that any personal opinion is as true as the facts the world’s best scientists agree.


Scientists are aware that our sensory tools, perceptions, memories and communications are subjective and imperfect.

That doesn’t mean science is unreliable and should be discarded; the reverse is the case.

The scientific method is the best tool we have to overcome our limitations as individual observers.

It involves testing of results against predictions, logical analysis and peer group review.

That is how we incrementally improve our confidence that a model or theory is valid.


This table distinguishes some concepts related to communication.





the ability to respond effectively to knowledge in new situations


information that is accurate enough to be useful


any meaning created or found in a structure or behavior by an actor


a structure of matter/energy in which information has been created or found


Communication involves sending and receiving messages that convey information (descriptions, directions and decisions).

Senders and receivers may use speech, writing, gestures and other kinds of signal or data structure.


For two actors to communicate, they must share a language for creating and reading a message.

Speakers and hearers must share the same language for encoding and decoding a message.

They do not need to be related in any other way. 

E.g. An SOS message conveys one particular "idea" from a sender needing help to every receiver who decodes the message using the language it was created in.


Note that a message can convey a false idea. 

E.g. An SOS message might be a fake, intended to waste the time of its receivers.

In the classification scheme above, it communicates some information, but not knowledge.


Speakers create meanings in messages; hearers find meanings in messages.

Communication succeeds when created and found meanings are the same.

But there is no information/meaning in a message on its own

Information/meaning exists only in the act of creating or using a message.

And knowledge only exists where the information is useful.


To overcome the limitations of natural language, people "talk around" a message to ensure its meaning is conveyed.

And the better their relationship, the more likely they will do this long enough to understand each other.

Or else, they use controlled vocabulary in which words have universally agreed meanings (like SOS).


The Hermeneutic principle of communication says "The hearer, not the speaker, determines the meaning of a message."

This misleading principle makes innocent speakers guilty of causing offence where none was intended.

The intention of a speaker does matter, practically, logically and morally.


Truth has no meaning in a world without description.

Truth is a measure we apply to our descriptions of the world.

The truth of a model = the degree to which the model proves accurate and useful.


There can be no absolute truth; since a description is not the reality it describes.

There are degrees of truth - or confidence - in the accuracy and usefulness of descriptions.


If an animal couldn’t form a decent model of things in the world, it wouldn’t survive.

Its ability to perceive things in the world has evolved to represent those things.

Moreover, as members of a social species, we necessarily see the world similarly.

Since our ability to communicate about things has evolved so we can share our knowledge of them.

Sharing knowledge helps us determine our actions, cooperate socially, and survive.


The more we check a belief by testing and agreement with others, the more confidence we have in it.

Our survival as a species depends on that confidence being justifiable most of the time.

And the stunning success of hard science is ample proof that testing and peer review maximise the degree of truth.


Neither classical physics nor quantum mechanics is only a speculative description of reality.

Tests show that, in particular circumstances, they each represent or symbolise reality well enough.

Which is to say, both descriptions are true, or at least, true enough to be useful.


There are three ways to test the truth of a theory, description or model:


It is empirically true - supported by evidence from test cases.

It can help you recognise and predict what exists and happens in reality.

E.g. If you stay on the railway track, your belief that the train will strike you will be confirmed.


It is logically true - can be deduced from other concepts within a body of knowledge.

It follows logically from axioms (presumed truths) that a body of knowledge is based on.

E.g. The force on a body struck by a moving train can be calculated from its mass and speed.


Last and least convincingly, it is socially true - widely believed in your social network.

Social animals who usually communicate what is true (rather than false) are better able to survive.

In the absence of empirical and logical evidence, we may retreat to the Nietzsche-like presumption that “shared perception is reality”.


Animals do sometimes lie to each other, as this video illustrates.

However, biological evolution has favoured social animals that usually communicate what is empirically true.


The trouble with human communication is that our words are so easily spoken, so flexible and so open to interpretation.

People often form incoherent sentences, and speak of misconceptions, or purely fanciful things.

Which is partly why scientists put experimental evidence and logical analysis ahead of simply asking others to confirm a view.

A scientific perspective

We all observe the world and describe it to others.

We test what we are told against our experience of the world.

The scientific method formalises this natural approach to describing and testing reality.


The basis of science


<create and use>             <represent>

Scientists          <observe and envisage >        Realities


Every description of real-world object is a simplification; it cannot reveal its infinite complexity.

Every description of description is open to the criticism that to attempt this involves some kind of illogical circularity

But our practical concern is not to worry whether a description is perfect or circular.

It is simply to ask: does it prove useful; does it help us to understand, test and predict things in reality?


Regarding physics in particular, read this paper The physics of systems.

A mathematical perspective

Numbers are the basis of mathematics, hard science and types used in software systems.

Where do numbers come from?

That's easier for a psycho-biologist (than a mathematician or physicist) to answer.


Recognising the size of family

Animals evolved to perceive the universe in terms of discrete things in space.

And to recognise family resemblances between things. E.g. food items, friends and cliff edges.

An earthworm knows enough to recognise another worm of the same type – for mating purposes.

Certainly, a worm can recognise others members of the worm set, though it may not remember them, and surely cannot count them.


Eventually, animals evolved to recognise if a family of things gains or loses a member

Experiments show dolphins can recognise which of two boards has, say, five dots rather than six.

And babies (before they have words) can recognise when a small group of things gains or loses a member.


Communicating the size of a family

Animals evolved to communicate facts about things that resemble each other.

The members of a species must have a similar idea of what food items and friends have in common.

The survival of a social group depends on its members sharing ideas, like where food can be found.

Many animals can communicate facts about things of interest by gestures and/or noises.

Honey bees can communicate the direction and distance of a pollen source.


Astonishingly, experiments suggest honey bees can count up to four and communicate that amount to other bees.


Defining a type to define a set

Sentient animals evolved to recognise things that resemble each other (food items, friends, cliff edges).

Humans go further; they have the urge to formalise the description of a family member into a “type”.


The proposal here is that all types and mathematical concepts emerged out of:

1.     the animal brain's ability to recognise "family members"

2.     the particularly human ability to more formally describe/symbolise a family member using words.


Numbers emerge from enumerating things – the members of a family - that resemble each other.

As soon as we have a family in mind, we can count the members of that family.

As soon as we can count the members of a family, we find some families have something in common.

That is, they share the number that enumerates how many members belong to the family.


The basis of mathematics


<create and use>      <represent quantities of>

Mathematicians   <observe and envisage >  Families of things


Thus, a number acquires the status of a type (quality or concept) that can be instantiated many times.

Numbers are types that represent what families of the same size have in common:

·        “oneness” is the property shared by all families with one member

·       “twoness” is the property shared by any one-thing family to which we have added one.

·       “empty (zeroness)” is the property of any family that has lost all its members.


It appears the Sumerians were the first people to develop a counting system.

And the number zero was invented later, perhaps independently by the Babylonians, Mayans and Indians

But surely the concept of an empty family was understood eons before that.

The reality (not ethereality) of types

Realities are composed of matter and energy that exists; meaning it can be located in space and time.

A description is also physical matter/energy structure.

But is intentionally created by a described to represent something else – be it observed or envisaged.


The basis of description


<create and use>        <represent>

Describers   <observe and envisage >  Realities


There was no description of reality before life.

Description is an ability that helps animals to survive.

Animals create descriptions in the form of internal memories and external messages (speech, writing and other kinds of representation).


Internal and external descriptions are different in many ways.

But they are similar in the most important way; they are created to be retrieved/read and used.


There is no ethereal description aside from what exists in one or more copies of it.

Many more or less accurate copies of a description can be made.

Delete all copies of a description and it disappears from the universe.


Descriptions are created when actors encode them in some form of matter and/or energy.

Descriptions are used when actors decode them from those forms.

Communication between actors succeeds when the encoded and decoded meanings are the same.

Or rather, near enough the same to be useful, because there can be degrees of truth.


Since individual rocks share some common qualities, they are readily perceived as instances of the same kind.

The ability to describe things and ideas using words (and graphical symbols of them) dramatically extended human descriptive/typification ability.


The basis of verbal description


<create and use>          <symbolise>

Humans   <observe and envisage >  Realities


The types we symbolise using the words “rock”, “plant” and “circle” have been created, remembered and communicated countless times.

That does not mean any type exists independently of its appearance in a description, in a more ethereal form.

It only means that many observers have generalised the same or similar type from a set of similar concrete realities.


Surely when all “rock”, “plant” or “circle” descriptions are destroyed, then that concept or type will disappear from the universe?

There is no type or concept outside of a description encoded in a matter and/or energy structure?


Many (perhaps most) mathematicians are reluctant to believe that there were no numbers before mankind, or life.

But surely, you cannot have numbers until you have types, of which instances can be counted?

And you cannot typify things until there is some kind of intelligence.

So, numbers only existed the form of types when life forms started to create, remember and communicate types


On the other hand, there were always things that can (in retrospect) be regarded as similar.

In the history of the universe, this was first true at the level of atomic particles, then stars and planets.

So, numbers always existed in the sense that numerous similar instances of (what we now choose to describe as) a type have existed.


The more general question is whether there were any types before life.

The premise here is that there were no types, no descriptions, before life.

The idea of an ethereal type is useless, redundant, and better cut out using Occam’s razor.

A philosophical position statement for systems thinkers

Realism versus idealism

We describe particular situations, entities and events by attributing universals to them.

A universal is a property, quality, characteristic, attribute or type such as “tall”, “yellow”, “circular” or “dangerous”.

Sometimes we attach a measure or degree to the property, like 2 metres tall, or very dangerous.


The basis of verbal description


<create and use>             <typify>

Describers   <observe and envisage >  Particulars


The “problem of universals is the question of whether universal properties exist - or what it means to “exist”.

Traditionally, “realist” and “idealist” philosophers disagree on whether universals exist independently of thought and speech (and records of those).

Philosophers, mathematicians and scientists have debated this for millennia, at least since Plato and Aristotle.


Another paper - The problem of universals - concludes that to a psycho-biologist, the problem of universals is something of a fake problem.

Or at least, the distinction between realist and idealist philosophical positions is a false distinction.

Over centuries, idealism and idealism have evolved into a confusingly diverse and overlapping mess of different positions.

And today, what that other paper calls Scientific Idealism and Scientific Realism are much the same.

Wittenstein’s Tractacus Logico Philosophicus

Ludwig Wittgenstein (1889-1951) influenced the “Vienna circle” of logical empiricists (aka logical positivists).

He argued philosophical disagreements and confusions can be resolved by analysing the use and abuse of language.

In his “Tractatus Logico-Philosophicus” he set out seven propositions.

The propositions are famous for being a tough read, and have been interpreted in various ways.

That doesn’t matter here, because Wittgenstein later realised his tractatus was self-contradictory.

In “Philosophical Investigations”, published after his death, he developed an entirely different linguistics.

He turned his focus from the precision of language to the fluidity of language.

He dropped the metaphor of language “picturing” reality and replaced it with language as a tool.

A new Tractacus Logico Philosophicus

In so far as philosophy is about language, knowledge and truth, it seems to have been overtaken by biological and software sciences.

This new attempt at a “tractacus” is written from the perspective of a psycho-biologist rather than a linguist or mathematician.


1 Reality is what exists in matter and energy, in space and time.

The context is the universe we live in, as observed and described by physicists.


2 A description is created by an actor (internally or externally) to represent a reality that is observed or envisaged.

A description is a model of a reality that is observed or envisaged

(Other kinds of information - directions and decisions - are mostly out of scope here.)


3 A description is also a part of reality

Description is not an ethereal concept; descriptions are real and can be described.


4 There are degrees of truth in a description – on its creation and in its use

The words “true” and “false” may be read as “true enough” and “false enough”.

Because truth and falsehood are judgements made by description creators and users at a moment in time.

And those judgements may be different on different occasions.

The scientific method is the best tool we have to determine how true an assertion is.


5 A description (e.g. of a unicorn) is fanciful to an actor who believes it represents an imaginary reality

However, it might later turn out to true.


6 Communication succeeds when the meanings/information in a description are the same when encoded and decoded - near enough.

Communication is a process that conveys a description (and/or other information) from a creator to a user.

The description and communication processes are performed by actors that may be animals or machines.

“Thinking” and “intelligence” includes the ability to create and use descriptions, and more that is out of scope here.


7 Communication requires that speakers and listeners share the same language for encoding and decoding a description.

A language contains a set of symbols used in the process of creating and using descriptions.

Mostly, we are talking about languages with verbal or graphical symbols, but symbols can also be gestures or even smells.


8 To share a language, human speakers and listeners must share a lot more.

They largely share same biology, psychology, experience of the world and education.

9 A description typifies what is described; it attributes general properties or qualities to particular things.

Every description could, potentially, be realised in several realities.


10 Natural language types are loose, fuzzy and flexible (as Wittgenstein observed).

However, the process of forming a system description involves formalising descriptive types - as follows.


11 A description may be a singular type (e.g. tasty) or a compound type (hot, tasty, liquid).

A system description, however large and complex, can be seen as a compound type.


12 A singular description/type is explained in a circular fashion in terms of other descriptive types.

E.g. A “rock” might be described/typified as “dry”, “perceptibly discrete entity”, “solid body” and “mineral material”.


13 To create a consistent and coherent domain-specific language we must break the circularity by agreeing some basic axioms or base types.

To describe a system, we must create a domain-specific language.


14 In languages for describing systems, the base types divide along the lines of space and time.

System describers typically perceive and describe systems in terms of:

·       actors (cf. objects) that occupy space at a moment in time and

·       activities (cf. motions) that occur over time.

A table of philosophical dichotomies

The table below is an attempt to help me and readers compare and contrast the terms and concepts therein.

The second and third columns were edited from the three sources below.

·       The philosophy book. ISBN 978-1-4053-5329-8


· (this may be a dead link)


The first column contains my view, distilled from history of life on earth in this paper The science of system theory.

Since posting the table in 2014 I’ve had many reservations about it.

Some terms are defined differently in other sources and/or have multiple meanings.

Some terms presented as “different” are arguably not opposites.

Some definitions depend on other terms, such as “existence”, whose meaning is debatable.

And some philosophical positions seem like meaningless babble to me.

In so far as philosophy is about language, knowledge and truth, it seems to have been overtaken by biological and software sciences.


My view

Some philosophical positions

Some different philosophical positions

On “existence

Matter and energy exist, but are mysterious, beyond our full comprehension.

All our perceptions, descriptions and mental models of matter and energy also exist in the form of matter and energy.

Idealism: existence is mental or spiritual.

Foerster’s Constructivist Postulate:

"Experience is the cause, the world is the consequence."

Materialism: existence is material.

Foerster’s Realist Postulate:

"The World is the cause, experience is the consequence."

The modern view is “cognitive embodiment”.

The mind is part of the body rather than separable from it.

Cognitive embodiment: mental states and activities are bodily states; the mind is inseparable from the body.

Cartesian Dualism: views the mind as standing apart from the body; the mind controls, interacts with and reacts to the body. (After Descartes)

Wisdom is the ability to respond effectively to knowledge in new situations

Knowledge is information that is accurate or true enough to be useful.

Knowledge represents what exists – to help us manipulate it or predict its behavior.



Information is meaning created or found in a structure or behaviour by an actor.

Communication requires speakers and hearers to share a language for encoding and decoding the structure of behaviour.

The Hermeneutic Principle: "The hearer, not the speaker determines the meaning of an utterance."

The communication principle: Speakers create meanings in utterances; hearers find meanings in utterances; communication succeeds when the created and found meanings are the same.

Data is a structure of matter/energy in which information has been created or found.

Facts are encoded in the data structure by a sender and can be decoded from it by a receiver.



Knowledge acquisition

The members of a social species necessarily see the world similarly.

They evolved the ability to perceive and communicate about the world.

They do this well enough to survive.

We humans learn from a mix of

1.      empirical experience of real-world entities and events

2.      logical deduction

3.      social interaction


Each kind of learning has helped our species to understand reality and manipulate it.

Perspectivism, radical constructivism and post-modernism are dangerous ideas that people use to undermine science and its importance to society.

Empiricism: knowledge is acquired from information obtained from the senses rather from reasoning.

Interpretative: we understand things by perceiving them.

Functionalism: we build mental structures through maturation and interaction with the world.

Cognitive constructivism: knowledge is acquired by creating mental structures in response to experiences. (Piaget)


Social constructivism: knowledge is acquired from social interaction and language usage, and is a shared rather than individual (Prawatt & Floden).

Epistemological Postulate: "He who organises his experience organises the world". The world is unique to each individual.

Radical constructivism: knowledge is acquired from experience, but is not, in any discernible way, an accurate representation of the external world or reality (von Glasersfeld).

Perspectivism: There is no objective truth; knowledge is conditional upon personal perspectives or interests. (Nietzsche)

Rationalism: knowledge is acquired by reason and logical analysis.

Formalism: we understand things by manipulating symbols. E.g. Mathematics does not require the existence of objects or properties.

On language

Whether there is some truth in structuralism or not, the human mind is plastic and language is infinitely flexible.

To describe a testable system, an artificial domain-specific language is needed.

Structuralism: we are born with structures that determine how perceptions (phenomena) of concrete things (noumena or a priori objects) are brought together and organised in the mind.

Structuralism in linguistics: language consists of rules that enable speakers to produce an infinite number of sentences. (Wilhelm Wundt (1832-1920) and Chomsky).

On determinism

At a micro level, the world as we experience it is deterministic.

We can predict the next discernible event - at least in theory.


At a macro level, the world we experience appears indeterminate.

The long-term outcomes of events are unpredictable (aka chaotic).


At a psychological and sociological level we have no reasonable or acceptable option but to treat people of sound mind as having free will.

Deterministic: every state and event is the consequence of antecedent states and events. This implies that prediction is possible in theory.

Deterministic automaton: a machine in state Si,

when it receives input Ij,

will go into state Sk and

produce output Ol

(for a finite number of states, inputs and outputs).

Self-determination: choices arise from reasons or desires (regardless of how the processes of choice work).

Indeterministic: a state or event is not wholly the consequence of antecedent states or events. This seems to imply some kind of randomness in state transitions.

Random: haphazard, not-predetermined. In maths it is a measure of how unpredictable a future state or event is.

Chaotic: disorderly. In maths it means behavior in which small differences in an initial state or event yield widely diverging outcomes (even though the system is deterministic, with no random elements). This makes long-term prediction impossible.

Both holist and reductionist views of a system are important and helpful different times. Enterprise architecture is deprecated by some “systems thinkers” as being reductionist.

The implication is that other kinds of “systems thinking” are better for being purely holistic.  In practice, both enterprise architects and systems thinkers take both views of systems.

Holism: treats a system’s parts as inseparable. The properties of the whole system are not the properties of any part. These “emergent properties” emerge only from the interaction between parts

Reductionism: explains the properties of one thing by the properties of another (lower level) thing. Or else, ignores the higher thing in favour of discussing the lower thing(s).