The philosophy of systems

Copyright Graham Berrisford 2014. Last updated 19/10/2019 20:45 One of a hundred papers on the System Theory page at


System theory can be related to questions that have been debated by philosophers for millennia.

This paper addresses that relationship from the perspective of science, especially biology.

It relates system theory to philosophical positions - with passing references to Nietzsche and Wittgenstein.

It presents a modern view of "knowledge" and "truth" that might dissuade some from being led by "second order cybernetics" towards an anti-science "relativist" position.


A few system ideas. 1

A few philosophical ideas. 1

Perception as realistic rather illusionary. 1

Knowledge as a biological phenomenon. 1

Description versus reality. 1

More on knowledge. 1

Subjectivity and objectivity. 1

Relativism and perspectivism.. 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



A few system ideas

Here, the word “entity” means “an observable or conceivable part of the world”.

It could be a planet, a hurricane, a group of people, or a performance of a symphony.

In his work on cybernetics, Ashby urged us not confuse an entity with any 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-world entity] 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)


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)


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

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

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


General system theory is general in the sense of being cross science.

Systems thinking can and should be an application of the general scientific method.

An abstract system is a theory of how an entity works.

We can test that a real-world entity empirically exemplifies that theory - to the degree of accuracy we need for practical use.

A few philosophical ideas

System theory can be related to questions that have been debated by philosophers for millennia.

The idealism/realism dichotomy goes back to time of Plato and Aristotle

To begin, let us contrast idealism and solipsism with realism and empiricism.


Idealism is the view that reality as we know it is a construction of the mind.

Solipsism is the view that we cannot logically prove that things (we think we know) exist in reality.

Also, that the past is an illusion we construct to account for our present state of mind.

These views may lead people to conclude all ideas about the world are equally valid.

And since abstract systems are constructs of the mind, all systems are equally valid.

This is a kind of "relativism" that devalues science and system theory.


Realism is the view that things exists in reality, independently of our perception of them and conceptual schema.

Empiricism is the view that our knowledge of entities in the world comes from our perception of them.

Most scientists would probably describe themselves as realists and empiricists.

They test how well some entity behaves according to what a theory predicts.

Just as systems theorist tests that some entity behaves as a system predicts


You may reasonably conclude that system theorists take philosophical positions – like realism and empiricism - that support science.

But there is something misleading about the contrast drawn above

Because system theory is also compatible with "epistemological idealism".

Epistemological idealism is the view that reality can only be known through ideas, that only psychological experience can be apprehended by the mind.


Many variations on idealism and realism (ontological, epistemological, whatever) have been articulated over the millennia since Plato and Aristotle

The variations overlap; some seem to turn the idealism/realism distinction on its head.

The paper doesn't attempt to take sides.

It jumps over the philosophical word play, with reference to biological evolution, classical cybernetics and the scientific method.


Darwin undermined the views of most old-world philosophers.

Even many modern philosophers seem not to understand biological evolution and its implications for the development of

a)     our "mental models" of reality and

b)     the verbal language we use to our share mental models.


Idealism (even solipsism) is compatible with a Darwinian analysis of how animal intelligence evolved.

We need only accept what Ashby exemplified in his "gale warning broadcast" example.

That is, one person can share some of their knowledge of the world with another (who can test its validity).

To share knowledge, the first person translates a psychological representation of a reality into a communicable representation, and the second translates it back again.


Scientists and systems theorists don’t presume any model of the world is perfectly true in an absolute way.

Their aim is to create models that are consistent and coherent, and useful.

But different models can be inconsistent with each other.

E.g. Is light a stream of particles or waves? Physicists find each model has its practical uses.


Aside: Ian Glossop tells me the view above is compatible with many philosophers.

Including Searle, Dennett, Dretske, Fodor, Kim, Davidson, McGinn, Putnam, Popper and Russell.

But I don't promise they would endorse all of what follows, which is largely what I read as said or implied by Darwin and Ashby alone.


There are many kinds of model, there are mathematical models and narrative models.

Natural language is so flexible and ambiguous that a narrative model can be misleading.

We can do better by creating and using a "domain-specific language" when describing a "bounded context”

A statement that is true in one domain-specific language may be untrue in another.


Heinz von Foerster (1911 to 2002) was an Austrian American scientist who combined physics with philosophy.

He is widely credited as the originator of “second-order cybernetics.”

Followers of this school of systems thinking often refer to the quotes below.


“Knowledge is a biological phenomenon” (Maturana, 1970).

“We cannot know the essences of things in themselves; all we can know is what we know as abstracting nervous systems.” Alfred Korzybski

“Each individual constructs his or her own reality" (von Foerster, 1973).

"The environment as we perceive it is our invention." (von Foerster, 2007).

“Knowledge "fits" but does not "match" the world of experience” (Glasersfeld, 1987).

“All experience is subjective (Gregory Bateson).

"Objectivity is the delusion that observations could be made without an observer." (von Foerster).


Do these ideas mean we should endorse relativism or perspectivism?

Must we say that knowledge and truth exist only relation to a particular culture, society, historical or bounded context?

This paper explains how system theory addresses this question - with passing references to Nietzsche and Wittgenstein.

For extensive analysis of the quotes above, read on.


In short, all the quotes are both true and misleading.

Knowledge is not restricted to one biological entity, since social animals do, successfully, share knowledge of the world.

Sharing and testing the knowledge we acquire from experience can assure us it is objective to the degree we need.

We can and do verify the knowledge that we construct - socially, logically and empirically.

Perception as realistic rather illusionary

A sensor is a (biological or technological) machine that can represent some features of a reality.

A sensation or perception is a representation (model or image) of those features. 

It is not the actual features; it may be fuzzy, incomplete and malleable.

Nevertheless, the accuracy of the model can be tested by using it.


At the most external point of a sensor (e.g. the eye's lens), there may be no or minimal filtering or censorship.

As the sensation progresses through the nervous system, processes can be applied to it.

E.g. The retina of a cat's eye is especially sensitive to thin wiggly lines - like mouse tails.

Further into the brain, cognitive processes may reshape the incoming message.

According to this Anil Seth talk, research suggests the brain combines

·       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


That does not mean what you perceive and remember is purely an invention - or does not represent knowledge of the external world.

It only means your brain (given the time and resources at its disposal) makes the best bet it can as to what your senses tell you about the world.

If animals could not compare new and old perceptions, and match them correctly enough, then they could not learn to recognise and manipulate things in the world.


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 an entity - otherwise it would be the entity

That does not mean (as Seth implies) that the entity 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 entities 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.

Knowledge as a biological phenomenon

“Knowledge is a biological phenomenon” (Maturana, 1970)

In "Life's irreducible structure" (1968), Polanyi argued the information contained in the DNA molecule is not reducible to the laws of physics and chemistry.

Polanyi proposed there are higher levels of reality and of causality.

Nobody really understands how higher-level knowledge and consciousness emerge from our DNA.

But we do have a basis for that understanding, in principles articulated by Darwin and Ashby


From Darwin, we have the principle of why biological systems evolve from one generation to the next.

Millions of years ago, animals evolved to conceptualise things they perceived in the world.

To remember a thing, they encoded a representation of it (or some of its features) in a neural memory.

And to recall a thing, they decoded that memory.

Why did memory encoding and decoding processes evolve in animals?

Because those processes proved helpful, they enabled some animals to survive and thrive better than other animals.

Today, machines created by human animals can encode and decode memories that represent things.


From Ashby, we have the principle that an abstract system is a model of any reality that realises that model.

A representation or model is not the reality it models, and it may be an imperfect model.

It need only represent a reality well enough to prove useful later, when observing or manipulating a reality.

E.g. A cat remembers a mouse's features; later, the cat spots and catches a mouse.

A honey bee communicates the location of some pollen; later, other honey bees find that pollen.

You learn Newton's laws of motion; later, you use them effectively.


Also from Ashby, we have the principle that “coding is ubiquitous”.

A brain’s biochemistry, a cave wall drawing, a word expressed in sound waves, a honey bee’s wiggle dance.

Each is a physical structure that an actor can use to encode some information, to remember it or communicate it.

But the structure is meaningless on its own; it is only meaningful to the actor who creates it, and any actor who uses it.

If I decode a structure, and it passes my test of usefulness, I (retrospectively) see the structure as knowledge.

But if you decode the same structure, and it fails your test of usefulness, you (retrospectively) see the structure as useless information.


Accordingly, this table distinguishes knowledge from information and information from data.





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 by an actor


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



Any structure (a shadow, a door, a dance movement or word) can be used to hold data.

·       You may read the time of day from the direction of a shadow on a sundial.

·       You may tell people whether you are open to visitors or not by leaving your office door open or closed.

·       A honey bee can tell other bees about pollen locations using dance movements.


You can create or find information or meaning in any structure or motion that is variable - has a variety of values.

The spoken word gives humans the ability - with almost no effort - to form infinite data structures.

And the written word gives us the ability to preserve those structures in shared memory spaces.



There is no information or meaning in a data structure (shadow, door, dance movement or words) on its own.

Meaning only appears in moments when data is created/written or used/read, using a given language, by an intelligent actor.

If you read data created with intention by another actor, you may find the meaning they intended you to find.

What if you read data created by a mindless recording process - in a big data store?

You can only find the meaning already-given to that data by those who defined the structure of the data store, or a meaning that you are looking to find in it. 

Correlations and patterns don't emerge from data on their own.

They only appear when the processes of an intelligence (human or artificial) are applied to the data.


In short, there is no meaning in data per se.

Meaning only exists in the process of creating or using a data structure.

Meaning is encoded in a data structure when it is created and decoded when it is read.

To encode or decode data, an actors must use of a code.

Encoding and decoding processes are ubiquitous, appear in all forms of communication.



For an act of communication to succeed in conveying information, two roles must be played.

·       One actor (a sender) must encode some information or meaning in a data structure or message.

·       Another actor (a receiver) must decode the same information or meaning from that data structure or message.


Consider how one bird (acting as a receiver) understands the alarm call made by another bird (acting as a sender).

The sender and receiver must share the same language or code for encoding and decoding the message.

But, prior to exchanging that message, they may be entirely unknown to each other.


Consider the transmission of an SOS message, which conveys the idea that help is needed.

It is broadcast by a sender to any and every actor able to receive it.

It is understood only by receivers who can decode the message, using the language it was created in.

It might be a fake, intended to waste the time of its receivers – which is to say it conveys misinformation rather than knowledge.


One message can be and often is interpreted differently by different receivers (using different codes, in different states, using different rules).

However, social communication would never have evolved if senders did not manage to share knowledge with receivers often enough.

That sharing of knowledge takes place is evident from empirical observations - regardless of how it works.

Worry not how weakly the model in a message represents a reality, and how different the internal models of a sender and receiver may be.

Consider only this - the evidence is that we can and do share knowledge.

E.g. You tell me a train is coming and I then step off the railway track.

That evidence indicates we share a considerable amount of knowledge about the world.


Humankind brought four innovations to communication.

1.     Words: an infinitely flexible box of sounds for communicating, which cost almost nothing to create and use.

2.     Oral speech: speaking and hearing words, using sound waves to symbolise meaning in messages.

3.     Writing: recording words in persistent memory structures.

4.     A “domain-specific language” in which the ambiguities of words in natural language are minimised.


The fact that social actors share knowledge is demonstrated whenever they cooperate successfully.

In a human social network, information about the world is represented:

·       Internally, and mysteriously in the neurons of individual actors, and

·       Externally, in messages actors exchange and in memories/records they share access to.


To overcome the limitations of natural language, people use controlled vocabularies in which words have universally agreed meanings (like SOS).

They also "talk around" around a message, express it several ways, to ensure its meaning is conveyed.

The stronger their social relationship, the more likely they will do this long enough to understand each other.



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.


If the information created or found in a message or memory is accurate enough to be useful, then it may be called knowledge.

E.g. the knowledge of where to find some pollen can be communicated by one honey bee to another.

E.g. knowledge of Newton’s laws of motion is useful, even though Einstein showed them to be only approximations.


(What a message sender considers true and useful knowledge, a message receiver may consider false, and vice versa.

E.g. I feel the swimming pool is warm; I tell you that and you “take me at my word”.

You dive in, but find the swimming pool is colder than you expected, and complain that I lied.)



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

The application of wisdom to knowledge implies a higher level of intelligence than communication alone.

Description versus reality

 “Knowledge "fits" but does not "match" the world of experience” (Glasersfeld, 1987).

“We cannot know the essences of things in themselves; all we can know is what we know as abstracting nervous systems.” Alfred Korzybski


This is axiomatic: a description is not the reality it describes.

However, no biologist would accept the view expressed by one system thinker that "internal cognitions do not reflect any external reality".

First, the neural systems of animals evolved to represent things in their environment (food, friends and enemies) in bio-chemical memories.

This helps individual animals survive and thrive by recognizing and manipulating things in their environment.

Social animals evolved further to share knowledge of things in their world, by translating internal representations into external messages.

For example, birds make alarm calls, and honey bees tell other honey bees where to find some pollen.

Human animals evolved further to communicate information about the world (descriptions, directions and decisions) by using words.


Discussion of the difference between description and reality goes back to the ancient Greeks.

This triangular graphic separates descriptions from the realities they describe, symbolise or represent.


Abstraction of description from reality


<create and use>          <represent>

Describers  <observe & envisage> Realities


A description is created by a process of encoding, and used by a process of decoding.

A description can never be more than a very selective perspective or model of a reality (else it would be the reality).

Recursively, descriptions of realities are also real.

Descriptions are abstractions that appear in the physical forms of memories and messages – and can be described.

More on knowledge

“Each individual constructs his or her own reality" (von Foerster, 1973).

"The environment as we perceive it is our invention." (von Foerster, 2007).


It is axiomatic in biology and psychology that your brain is unique to you.

However, it also axiomatic that one animal can communicate some knowledge about the world to another

E.g. I tell you there is an apple in the blue box behind that door; you open the door, open the blue box and eat the apple; QED, I transferred some knowledge to you.

In basic cybernetics also, it is presumed that two systems can exchange some knowledge about the state of the world.

Yet von Foerster’s aphorisms (above) lead some to deny that knowledge can be shared.

His second order cybernetics leads people to make other assertions that range from questionable to nonsense.


Remembering knowledge

Contrary to the initial hopes of cyberneticians (Weiner and McCullough, Ashby and Turing) it has been clear since c1960 that the brain does not work like a computer.

It does not store static persistent data structures, its mental images are incomplete, fuzzy and malleable.

Experiments show that people find it difficult to apply the rules of logic.

Nevertheless, the brain is not empty; it does remember some features of entities and events it has perceived.

It evidently does process representations of those features when it remembers and recalls them.

You do know a dollar bill is rectangular, and green, and has a one on it.

You remember enough to recognise a dollar bill when you look at one – though, yes, you might well be fooled by a fake.

How information is stored is a mystery, perhaps it is stored in bio-electro-chemical processes.

But if no information was stored how you would describe a dollar bill or a friend to somebody else?


Sharing knowledge

In biology, there is no need for a mind-body separation.

Even a single-celled organism has some knowledge of its environment.

But it is convenient here to speak here of mental models.                                                                                                                                          

Obviously, a mental model is unique to the mind that holds it.

No two of us share the same biochemical mental model of the world.

But for a systems thinker to say “no one shares the same knowledge of the world” is clearly untrue, since it denies the success of social animal species.


“We cannot transcend ourselves as organisms that abstract” Alfred Korzybski

The evidence suggests we can and do transcend ourselves as individual organisms when we successfully cooperate socially.

All social animals depend on being able to communicate descriptions that usage proves to be accurate.

Demonstrably, animals do manage to share “facts” that we abstract from observations of reality.

Humans use domain-specific languages, the rules of logic and the scientific method to transcend our subjective experience.


Examples of knowledge that is shared

Many have used Newton's laws of motion to calculate a force.

The accuracy of the calculation is revealed in its successful use, every day, all over the world.


You ask someone to call you at 11.00 hours; they call you at the appointed time.

Demonstrably, you share an understanding of the abstraction labelled “the time of day”.


Your fishmonger advertises cod steaks; you buy some and eat them.

Demonstrably, you share an understanding of what the abstraction labelled “cod steak” means.


A honey bee can encode their mental model of a pollen location in a dance.

Another bee can decode the dance into a mental model of where that pollen is.

To find the pollen, the second bee must share the mental model of the first.

This shows that both mental models represent the same facts – the distance and direction of the pollen source.

The facts recorded in these mental models are objective and accurate enough for us to call them “true”.

Suppose you see one honey bee finds the pollen described to it by another honey bee.

You (3rd party observer or experimenter) have evidence that they have shared an objective description of the world.

That is the very definition of objective (not limited to one intelligent entity - and confirmed by empirical evidence).

Moreover, in an example of cross-species communication, scientists can read the dance of a honey bee and find the pollen themselves!


We (you and I) can both read and remember this sentence.

Our two mental models of the sentence are different and yet the same.

They are different – our mental models are bio-chemically distinct and different.

They are the same - we can both recall and recite the sentence accurately.

The objectivity of our mental models is found not in their biochemistry.

It is found in communication showing we both recall and recite the sentence.

A 3rd party can test what we recite is objectively accurate.


Tacit knowledge?

The focus in this paper is on knowledge that is explicable and shareable.

An individual actor’s knowledge may be defined as encompassing everything an actor can learn in various ways.

        Learning by rote of descriptive facts (e.g. the colors of the rainbow)

        Learning a cultural procedure (e.g. to say please and thank you)

        Learning a logical procedure (e.g. multiplication, algebra).

        Abstracting a general pattern or type from data gathered or remembered (cf. “machine learning”).

        Learning from physical sensation (e.g. that it hurts to fall over)

        Learning a physical process (e.g. to walk, to swim, to play music).


Polanyi’s "tacit knowledge" is knowledge that cannot be shared.

And that includes “know how” such as the ability to swim. 

Yet, we do teach people to swim using words and gestures.

So is the explicit/tacit distinction fuzzy rather than sharp?


I am told Polanyi said: "in the end all knowledge is personal and tacit".

Which is interpreted to mean real knowledge is not shareable.

Codified or explicit knowledge is shareable, but does not become real knowledge until it is internalized.

I am arguing that is misleading!

I am saying "real knowledge" only appears at Polanyi's higher level of reality and causality,

In evolutionary terms, memories came before messages.

Memories encode "internalized knowledge" in private (bio-chemical) models.

Messages encode "externalized knowledge" in a public (oral written or other) models.

Both are encodings of information.

The "real" meaning/information/knowledge only appears in the process or creating or using a coded representation, be it internal or external.

(This is why the "semiotic triangle" doesn't work, and Peirce struggled to explain his philosophy.)



Designators that biological organisms create to identify or typify real-world objects are physical, but not rigid.

There is no absolute truth in any verbal or non-verbal representation of a reality.

There are however domain-specific languages in which people agree to share a vocabulary

And there are degrees of truth or usefulness in statements formed using those languages.

And there is DNA – the unique identifier of an organism.

Subjectivity and objectivity

The ideas explored above are often discussed in the context of second order cybernetics.

But they are perfectly compatible with Ashby’s first order cybernetics, and with a Darwinian analysis of how animal intelligence evolved.

They might be extended thus.

·       Knowledge is a biological phenomenon - there was no description before life. 

·       Knowledge "fits" but does not "match" the world of experience - a description is a reality, but not the reality it describes. 

·       Each individual constructs his or her own reality - our mental models are bio-chemically distinct and different.


The trouble is that these quotes can lead some towards a kind of "relativism" that undermines science.

Other well-known quotes can lead people to the same conclusion.


"All experience is subjective." Gregory Bateson.

For sure, but readers of this quote may mistakenly conclude that everything we believe, since it is subjective, is equally valid.

How do we find food and eat it if we have no objective-enough knowledge of the world?


"Objectivity is the delusion that observations could be made without an observer." Heinz von Foerster

For sure, there could be no observations before observers, and no description before life.

However, readers of this quote may mistakenly conclude there can be no objectivity in a description of reality.

How many subjective interpretations can different observers make of Newton's f = ma or Einstein's e = mc2?


Treating objectivism/subjectivism as a dichotomy is problematic - it hinders more than it helps.

Science does not divide views into 100% subjective or 100% objective or true.

Subjective does not mean wrong; it means personal, perhaps influenced by personal feelings, tastes, or opinions.

Objective does not mean infallible; it means not restricted to one individual and/or verifiable in some way, at least to a degree.


By social, logical and empirical verification we increase the objectivity of what may start out as a personal or subjective description.

Some descriptions (e.g. Newton’s laws of motion) have proved so objective – to have such a high degree of truth – that we trust our lives to them.

Physicists may describe light as a stream of particles or of waves.

They do not say either description is “true”, they say only that each model can be useful.


Evidently, animals can perceive and know some things with a sufficient degree of truth for practical uses

This was the motor for the evolution of animal memory and social communication

To deny that would be to deny the survival and flourishing of life on earth.

It was also the motor for the evolution of science.

To deny that would be to deny the success of science in developing the technologies and medicines we rely on.

Relativism and perspectivism

Relativism is the idea that knowledge and truth exist only relation to particular culture, society, or historical context.

For sure, 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 can be and are shaped by 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 transcend 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.


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).