General Description Theory

Copyright 2016 Graham Berrisford. One of about 300 papers at Last updated 17/10/2017 22:57


Enterprise architecture is about business system planning.

It can be seen as applying the principles of general system theory – which we’ll get to later.

First, what theory underpins general system theory?

This paper is one of several on theories of information, communication, language, knowledge and description.


To apply system theory is to describe a system as it is observed now, or envisaged in the future.

What is description?


Systems and descriptions of them... 1

The necessary paradigm shift 2

Organic description as a biological tool 3

Inorganic description as a social tool 3

Description as a human tool 4

What makes humans special?. 4

Three ways that humans describe reality. 6

A general description theory. 8

Conclusions and remarks. 9


Systems and descriptions of them

Systems are islands of orderly behaviour that are describable in terms of:

·         system actors - persistent entities – active structures – locatable in space

·         system activities – behaviors over time – which follow some logic or law.


Consider the solar system in which the sun and the planets interact by following the laws of gravity and motion.

This natural system predated the humans who first observed it.

It is describable because its behavior conforms to laws


Now consider a business system that is designed and created by humans.

It too can be described because its behavior conforms to some logic, for example:

A business system



Place order

Send invoice

Send payment

Send receipt


Only humans have the ability to abstract system descriptions from reality and document them.

Nevertheless, in evolutionary terms, that is an advanced form of what other animals can do.

Other animals can recognise and remember properties shared by similar things - a general type.

This paper discusses the evolution of description from the simplest of pattern recognition.

The necessary paradigm shift

You may find what follows is straightforward.

You may find it is at odds with some instinct or philosophy you already adhere to, and therefore find difficult to put aside.

Many people’s instinct is to divide the universe into mental and physical worlds.

However, the two-way mental/physical dichotomy of Cartesian Dualism has long been rejected by philosophers and scientists.

And since Descartes, people have proposed various three-cornered views of description and reality.

Some are drawn to "the semiotic triangle" and/or Karl Popper's "three worlds view" of the universe.

This work proposes these are not the best way to model the describer/description/reality trichotomy.


First, one has to shake off:

·         the mental/physical dichotomy presumed in Cartesian Dualism (really shake it off, further than Popper did)

·         a language-centric view of philosophy (really shake it off, unlike Wittgenstein)

·         a human-centric view of the universe.


Then acknowledge that:

·         describers are actors who have some intelligence about their environment

·         the ability of actors to describe the world is a side effect of biological evolution

·         in natural intelligence, the mental world is a physical world - though the bio-chemistry of that is deeply mysterious

·         in artificial intelligence, the most notable ability is the ability to abstract descriptive types from observations of similar things

·         describers and descriptions are themselves part of reality - and can themselves be described (if need be).


And finally, acknowledge that:

·         descriptions include models of every kind, both private and public

·         mental, spoken, documented and physical models are all descriptions

·         humans and their machines can translate every description of one kind into a description of another kind.


The philosophy here can be expressed in a triangle.

Description theory

Descriptions (private & public)

<create and use>                   <idealise>

Describers     <observe & envisage>     Realities


The distinction between private and public description is important, especially in any collaborative design exercise.

But it is not as fundamental as the description/reality distinction, and the need for describers to be included in the picture. 

The premise here is that description started in the domain of biology, and the ability to share descriptions is the basis of sociology.

Many kinds of animal share their private mental models through social communication.

Human social systems are more elaborate, and some have been formalised into business systems.

Organic description as a biological tool

Before life on earth, the universe was composed of matter and energy that changes over time.

There was physical matter and energy in space; and a continuous process of change to that matter and energy over time.

This process led to some systems, like the solar system, that repeat behaviors in an orderly way.

But there was no description of the universe.

For eons, there was stuff happening, and even some orderly systems; but there was no description of those systems.

(Or else, there was only a metaphysical description by God that is unknowable).


Eventually, biological evolution led to organisms with the ability to recognise things.

It led to animals that can match a perception of a thing to a remembered description of that thing.


These animals have internal perceptions and memories that describe the world in organic biochemistry.

E.g. Honey bees must hold mental models of pollen sources, in order to tell other bees where to find them.

These mental models are internal descriptions of the world, somehow encoded in their biochemistry (how does not matter here).

They are physical (not metaphysical) models.


All animal knowledge is explainable as a by-product of biological evolution.

Since the survival of intelligent actors is increased by their ability to create and use descriptions of reality.

Remembering descriptions helps animals to recognise food, mates, friends and enemies.

It helps them to manipulate things in the world and predict their behavior.

Having mental models of the world helps animals to survive, thrive, and pass their genes on.


Evolutionary biology does not require an animal to have a perfect description of anything.

All mental models are partial and flawed models of reality.

These descriptions need only be accurate enough to help animals survive and breed.

Animal minds evolved to model reality, not exactly as the world is, but just well enough.

Enough to sense, predict and direct events that matter to survival.

Inorganic description as a social tool

Evolution led further, to social animals that communicate via external messages and memory spaces.

Their symbolic languages include:

·         facial expressions

·         calls, barks, whistles and other sounds

·         smells

·         movements and gestures

·         manipulated materials (e.g. built nests)


By using such symbols, animals inform each other where food, potential mates, friends, and enemies are.

To do this, they must translate between internal/organic mental models and external/inorganic forms of description.

E.g. one animal translates an internal sense of danger into an external alarm call - signalling danger.

Another hears that alarm call and translates it into their own internal sense of danger.

Thus, the two share an internal mental model of the current situation as being what we symbolise as “dangerous”.


That animals do share knowledge is empirically demonstrable.

Again, the knowledge does not have to be precise or perfect, only accurate enough.

Description as a human tool

There is nothing human-specific above; descriptions were not first created by humans.

However, humans are particularly adept at using verbal languages to create symbols.

They easily and instantly translate internal mental models into and out of verbal forms


(Note that verbalising a description is not always necessary or best.

E.g. A drawing of a unicorn describes it better than a verbal description.)


Humans not only continually translate between internal and external models of reality.

They also excel at translating between external forms of description: e.g. between speech and writing.


Computers facilitate human communication of descriptions, directions and decisions.

They require that all descriptions, directions and decisions are translated into patterns of binary digits.

But translating into and out of binary code is no different in principle from translating between other symbolic languages.


Internal organic mental models encode descriptions very differently from external inorganic models.

Internal models tend to be more fragile, malleable and prone to decay.

But translating between internal and external models is no different in principle from translating between different kinds of external model.

What makes humans special?

Our use of words (and graphical representations of words) to remember and communicate information sets us apart.

Much of what makes humans special depends on our use of verbal languages.


Oral communication

Animals encode and communicate knowledge in various ways: smells, colours, body shapes and nests.

Many birds communicate by singing; and famously, honey bees communicate by dancing.

However, evolution gave humans a unique and dramatically well-developed communication tool.

We have the ability to talk, to speak and hear verbal messages, short and long, simple and complex.

Oral communication of descriptions, decisions and directions is essential to most peoples’ lives.


Written communication

Animals can leave a smell on a tree to say “this is my territory”.

They can shape materials into a nest that signals mating intentions to a potential partner.

The smelly tree and the nest enable animals to share information; they are persistent external memories of a kind.

However, we have shared memory spaces that far exceed those in scope, complexity and value.

We record oral descriptions, decisions and directions using that triumph of human invention - the written record.

Written communication is so important to modern society that schools prioritise the teaching of reading and writing over other subjects.


Collaborative intelligence

Many animals show a remarkable degree of intelligence.

They can abstract patterns from experience, remember them, recognise them and act accordingly.

However, our human intelligence is amplified by our ability to communicate orally and in writing.

Those abilities enable us to collaborate with others in complex projects to create complex things.


Domain-specific languages

Natural languages give most of us more than enough communication capability to get by.

But natural languages are biological, not strictly logical.

The vocabulary and grammar we use are loosely defined, and our natural language expressions can be unclear or ambiguous.

Errors and ambiguities in communication might perhaps be essential to life, since they lead to innovation

However, this limits our ability to extract the full and intended meanings of natural language statements.


In short, natural language is not precise and rigorous enough to support all our endeavours.

To specify complex artifacts, machines and systems we need more formal, domain-specific languages.

We have to translate informal mental models and natural language descriptions into more formal documented descriptions.

And to design effective information systems, we must formalise the language used in messages and memories.


Introspection and analysis

Biological evolution led to animals with self-awareness.

Experiments have shown many animals recognise themselves in a mirror, including elephants, apes, dolphins and whales.

However, we are surely not only more self-aware but also more introspective than other animals.

We analyse what is communicated, we challenge it, we test it.


It is easy to make assertions with no evidence.

Persuasive oratory and/or laziness can lead people to believe false assertions are true.

Historically however, the written record changed the game.

“As soon as writing made it possible to carry communication beyond the temporally and spatially limited circle if those present at a particular time,

one could no longer rely on the force of oral presentation; one needed to argue more strictly about the thing itself.” Luhmann


The written record helps us to examine what is thought, said and written, to challenge it, to test it.

We need tools confirm assertions are true, descriptions are definitions, and hypotheses are knowledge.

We have developed tools to test the truth of assertions: mathematics, the laws of logic and the scientific method.

Three ways that humans describe reality

In short, our unique tools – verbalisation, the written record and science - stretch what evolution gave us.

We have a uniquely-well developed ability to describe reality to each other; and we have various ways to do this.

This section skates quickly over three schools of thought, and draws correspondences between them.


Descriptions by mathematicians

The realities described by mathematicians are members of sets (which are often abstractions themselves).

Type theory


create and use


which idealise

Set members


Mathematicians define a set by defining it members in one or both of two ways.

·         by extension, by enumerating its members. E.g. list the decimal numbers, or name the colours of the rainbow.

·         by intension, by typifying one member. E.g. each member in the set of even numbers is as an integer divisible by 2.


In basic set theory, a set is static; it cannot gain or lose members;  change the members and you change the set.

What may be called "type theory" is more directly relevant to system theory than basic set theory.


Dynamic sets

In many domains of knowledge, sets are dynamic, they grow and shrink: e.g. the set of bachelors.

A dynamic set cannot be defined by enumeration (unless the counter is able to stop the world, and still observe it).

It can only be defined by intension, by typifying a set member.


Polythetic or fuzzy types

A type is defined by listing other types/properties that set members share. E.g. Rose: a plant that is flowering, woody and thorny.

Mathematicians usually assume a type is monothetic – defined by listing properties shared by every set member.

In nature and natural language a type is often polythetic - defined by listing properties not all shared by every set member.

For more on such fuzz types, read “Knowledge and Truth”.


This triangle represents how types idealise set members.



<create and use>       <idealise>

Mathematicians <enumerate> Set members


Description by logicians

Like anything else in the universe, verbal descriptions can be described.

The realities described by logicians are verbal descriptions called predicate statements.



created and use

Predicate logic

which idealises

Predicate statements


A predicate statement has the form: subject <verbal phrase> object.

For example: The Lawn Tennis Association <are responsible for defining> the laws of tennis.

For more on the use of predicate statements, read “Language”.


A predicate statement may be true or false depending on the values of its variables.

The use by logicians of logic to describe sentences can also be represented in a triangle.


Predicate logic

<created>                    <idealise>

Logicians <read and write> Predicate statements


Description by scientists

The realities described by scientists are aspects of the universe we inhabit.



create and use

Hyphotheses & knowledge

which idealise

The universe


To generate a hypothesis is easy, and does not amount science.

As Thomas Edison said “Genius is 1% inspiration, 99% perspiration.”

The inspiration, creating a hypothesis, is 1% of the effort; the other 99% lies in analysing and testing the hypothesis.

For more, read “Seven signs of shamanism”.


Science can be represented in the same triangular shape as above.


Hypotheses & Knowledge

<create and use>                 <idealise>

Scientists    <describe and predict>   The universe

A general description theory

The triangular shape repeated in the last section is so universal it can be generalised into a theory of description.

This table abstract the generalisation from three different ways of describing the world.



create and use

Descriptions (logical)

which idealise

Realities (concrete, or relatively so)

Type theory


create and use


which idealise

Set members



created and use

Predicate logic

which idealises

Predicate statements



create and use

Hyphotheses & knowledge

which idealise

The universe


The premise is: there was no description before life.

There could be concept before there was an actor able to conceive ideas.

And there could no description before there was a describer.

Moreover, the survival of describers depends on their ability to create and use descriptions of reality.


Our description theory is based on three propositions.

1.      Describers observe and envisage realities (which they perceive realities as composed of discrete entities and events).

2.      Describers create and use descriptions (stored in memories and conveyed in messages using brains, speech, writing, pictures and other forms).

3.      Descriptions conceptualise realities (they either mimic features of things, or represent them in some encoded form).


These papers use the term “idealise” rather than “conceptualise”.

And express the three propositions in a more graphical form, as a triangle.

Description theory


<create and use>               <idealise>

Describers <observe & envisage> Realities


Note: this triangle does not match some other triangles you may have come across, like the semiotic triangle.

And there is recursion; the universe of realities includes describers and descriptions.

Also, outside of mathematics and computing, fuzzy matching of real things to descriptive types is normal.

So a particular thing may not exactly fit a description of it (which may be called a polythetic type).

The descriptions we regard as “true” are ones that match well enough the things they describe, which implies testing one against the other.

Conclusions and remarks

This paper has drawn correspondences between different ways and examples of describing the world, as in this table.



create and use

Descriptions (logical)

which idealise

Realities (concrete, or relatively so)

Type theory


create and use


which idealise

Set members



created and use

Predicate logic

which idealises

Predicate statements



create and use

Hyphotheses & knowledge

which idealise

The universe

System theory

System describers

create and use

Abstract system descriptions

which idealise

Concrete systems


A concrete, running, system is made of individual things, somewhat different from each other.

It realises (or instantiates) the generalisations in an abstract system description (or type).

Conversely, an abstract system description is made of types that idealise similar things by naming properties they instantiate.


This table lists several applications of system theory.

General system theory

System describers

Abstract system descriptions

Concrete system realisations




“The Dewey Decimal System”

which idealises

Sorting books on library shelves




“The Solar System”

which idealises

Planets in orbits

Lawn tennis



“The laws of tennis”

which idealise

Tennis matches

Classical music



Symphony scores

which idealise

Symphony performances

Business systems

Enterprise architects

create and use

Business models

which idealise

Business actors and operations

Software systems

Software architects

create and use

Software models

which idealise

Software objects and operations


Business and software systems can be seen as products of the biological evolution that created social animals.

The following papers on general system theory treat both as formalised versions of social systems.


Aside on artificial intelligence (AI)

Don’t misinterpret news of AI machines talking to each other and “inventing their own language”.

Look at what they actually said to each other, and you’ll see it looks very unlike a language, and far more like the babble of software going awry.

AI has been hyped for 40 years, and it has recently been joined by “big data”; surely, both are still early on the hype curve.

The ability of a machine to recognise patterns in, or derive types from, data is one thing.

Our ability to create complex system descriptions and then build complex systems is on a completely different level.


Aside on free will

Robots with AI may employ probabilistic fuzzy logic or randomising functions when choosing a response to inputs.

That doesn’t imply they have intention or will.

However, it does prompt the question about whether animals have free will.

My view is that it doesn't matter how animal intelligence works, whether it is deterministic or not.

For almost all practical day-to-day purposes, we have to treat people as making choices of their own free will.

We allow judges and juries to make allowances for cases where people are “out of their minds” or coerced by others to make choices.



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