The science and philosophy of systems

Copyright 2018 Graham Berrisford. One of several hundred papers at Last updated 18/12/2018 15:25


Use this link to book a place on the next System Theory for Architects Tutorial in London Saturday March 2nd 2019.


Preface: on business systems

The systems of interest here are islands of orderly behavior in the ever-unfolding process of the universe.

That does include the solar system, riding a bicycle and the cardio-vascular system.

But the main interest is narrower:

·         Systems in which actors respond to information encoded in messages and memories.

·         Systems in which information describes or directs entities or events of importance to the system’s actors.

·         Human activity systems in which processes depend on messages received and memories retained

·         Computer activity systems in which all the messages and memories are digital.


Increasingly, human activity systems are supported and enabled by computer activity systems.

Up the 1970s, business systems were often analysed and designed by people in an Operational Research department.

At the start of the Information Age, many Operational Research departments were absorbed into IT departments.

Enterprise Architecture (EA) emerged in the 1980s.

EA grew out of IBM’s "business system planning", James Martin’s “information engineering” and other approaches.

All these approaches urged enterprises to take a strategic and cross-organizational view of business systems.

And the PRISM report of 1986 divided business system description into four domains.

The table below positions those four domains in the columns , and the cross-organizational view in the top row.







Infrastructure technology



Business roles & processes,

standardisation, integration

and road maps

Business data

standardisation, integration

and road maps

Business application portfolio

standardisation, integration

and road maps

Platform technology portfolio

standardisation, integration

and road maps



Outline design of a solution’s

required processes

Outline design of a solution’s

data architecture

Outline design of a solution’s

application(s) architecture

Outline design of a solution’s

IT production environment



Detailed design of

processes & use cases

Detailed design of

data stores and flows

Detailed design of

software architecture

Detailed design of

IT production environment


The table positions descriptions of human activity systems to the left, and computer activity systems to the right.

The two kinds of system meet wherever digital data is created and used by humans.

System architects are supposed to observe baseline systems, envisage target systems, and describe both.

So, you might assume they are taught about system theory and systems thinking; but this is far from the case.


Thinking about systems is often considered the domain of sociologists.

But if you are looking for discussion of social systems, you’ll have to wait for a while.

This paper (longer than any other on the web site) outlines the science and philosophy of systems.


You can’t understand systems without answering questions about the nature of description and reality.

These questions are often considered the domain of philosophers and linguists such Nietzsche and Wittgenstein.

There is some philosophy here, but the perspective is primarily scientific.


Contrary to some Postmodern Attacks on Science and Reality, this story respects science.

It positions systems theory in a brief history of the universe and human evolution, and as a branch of science.

The story of description and reality below is rooted in biology.

It is about humans came to communicate using languages, and describe the universe in terms of types, and systems.



The emergence of description. 3

Animal memories and messages. 3

General communication principles. 6

Human communications. 7

Human languages, natural and artificial 9

Description and reality - recap. 11

Propositional logic. 13

Types and instantiations of them.. 14

Systems as complex types. 14

Describing a human activity system.. 16

Describing a computer activity system.. 17

Conclusions and remarks. 18

The science of systems thinking. 18

The philosophy of systems thinking. 19


Footnote 1: Describing the world in terms of structures and behaviors. 21

Footnote 2: How to separate the signal from the noise?. 21

Footnote 3: Numbers as types. 22

Footnote 4: Thermodynamics. 23

Footnote 5: Postmodern Attacks on Science and Reality. 25


The emergence of description

Heinz von Foerster (1911 to 2002) was a thinker interested in the circularity of ideas.
(His contribution to systems thinking will be challenged later.)

He is reputed to have said “We live in the domain of descriptions that we invented.”


We do live in a society with laws, roles and rules invented by people.

But we don’t live in a world we have invented.

Scientists believe our universe started with a big bang about 14,000 million years ago.

The earth was formed about 4,500 million years ago.

And life on earth began at least 3,500 million years ago, possibly more.


Before life emerged, there were no perceptions or memory of the universe.

There was no conceptualisation or model of structures and behaviors in the universe.

Nothing was created to represent or symbolise what exists and what happens in the real world.

There was no description of the universe before life; description is a side effect of biological evolution.

Animal memories and messages

All animals must know something of the world they live in.

Even 3,500 million years ago, the earliest organisms had to recognise chemicals in their environment.

And they knew enough not to eat themselves.


Later, through evolution, animals developed ever more sophisticated ways of knowing the world.

By about 700 million years ago, Jellyfish had nerve nets that enabled them to sense things in the world and manipulate them.

In a nerve net, intermediate neurons monitor messages from sensory neurons and react by sending messages to direct motor neurons.

Thus, animals evolved to process transient sensations – each an encoded description of a reality.


By about 550 million years ago, some animals had a central hindbrain to monitor and control homeostatic state variables.

An internal information feedback loop connected that hindbrain to the organs and motors of the body.

The hindbrain had to sense the state of the body state variables and send messages to direct actions that maintain those variables.


About 250 million years ago, the paleo-mammalian brain evolved to manage more complex emotional, sexual and fighting behaviors.

A wider information feedback loop was needed to connect that higher brain to the external world.

The higher brain had to sense the current state of food items, friends and enemies in the world, and direct appropriate actions.


Recording knowledge in memory

Animals can not only sense and react to things, but also retain descriptions of things - if only as vague sense memories.

Like all biological traits, human memory is the result of a very long history, most of it shared with other animals.

At each stage in the path from vertebrate to mammal to primate to anthropoid to human, we acquired a different kind of memory

The result, this research suggests, is that humans have seven different kinds of memory.


Animals don’t just remember static images; they can remember the sequences in which dynamic behaviors unfold.

This other research suggests even rats can replay memories in order to recognise things in sequence.

You can remember the sequence of steps in a dance, notes in a melody, or words in a story.

And of course, the sequence of words in a sentence or message is important to its meaning.


This article on how the brain works suggests we know very little about how it works.

But how brains create and use descriptions of reality doesn’t matter here; it only matters that they evidently do.


The need for descriptions to represent realities

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

He took the view, called “perspectivism”, that our conceptualisations of the world are shaped by how we view it.


Some postmodernists (and Marxists) read Nietzsch as saying there is no objective truth or accurate knowledge of the world.

Some interpret his assertion as meaning all descriptions of the world are equally valid.

Any appealing belief or poetic assertion carries the same weight as scientific evidence.


True, to some extent, different people do perceive the world differently from each other, and from birds, bats and bees.

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

All animal life depends on two facts:

·         there is a real world, and

·         only some descriptions of reality prove accurate enough when tested.


This situation (and the philosophy here) can be represented in a triangle.



<create and use>           <symbolise>

Living entities  <sense & envisage>  Realities


We don’t need metaphysical philosophy to explain concepts, be they in minds, messages or memories.

An animal’s mental models must describe the world well enough; else the animal would not survive.

The physical world includes not only you, your food, friends and enemies, but also your concepts of those things.

Your conceptualisations can be “true” in one or both of two ways:

·         empirically, they help you recognise and predict what exists and happens in reality.

·         logically, they are consistent with and follow logically from other concepts within a body of knowledge


Communicating via messages

Even very primitive animals signal mating intentions to each other.

By 100 million years ago, some animals cooperated in groups.

Perhaps the earliest social acts were related to marking territory, signalling danger and locating food.

E.g. Cats spray scent to mark their territory; other cats smell that scent.


Communication requires both the creation (encoding) and interpretation (decoding) of messages.

Messages are created by manipulating physical matter and energy to form symbols (smells, gestures, sounds etc.).

The symbols identify or represent things of interest, such as territorial claims, friends, enemies and food.

E.g. A honey bee can symbolise the direction and distance of a pollen source in the form of a wiggle dance.


Honey bee communication

Wiggle dances

<perform & read>        <symbolise>

Honey bees  <find & seek>  Pollen sources


Symbols can both identify things and describe their features (qualities, characteristics, attributes, properties).

E.g. Astonishingly, experiments have shown that honey bees can communicate quantities up to four.


Some system thinkers promote the “hermeneutic principle” that the hearer alone determines the meaning of an utterance.

This dreadful postmodern idea makes speakers guilty of causing offence where none was intended.

The principle for success might be called the communication principle.

Communication requires that a receiver decodes the same meaning from a message that a sender intentionally encoded in that message.


Note that biological evolution has not demanded animals communicate perfectly accurate descriptions or absolute truths.

Animals send messages that represent reality accurately enough, often enough, for message receivers to find them useful.

Communications do fail when symbols are ill-formed, lost or obscured in transit, or misread or misinterpreted on receipt.

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

General communication principles

Many animals communicate facts to each other.

Facts can include descriptions, directions and decisions.

E.g. A bird’s alarm call may communicate the fact that a cat is near.

E.g. A honey bee’s dance communicates facts about a pollen source – its distance and direction.


Animals communicate by organising some matter and/or energy to symbolise facts of interest.

E.g. An alarm call is made by producing sound waves that can be heard.

E.g. A dance is made by moving limbs in a way that can be sensed by sight or by touch.

Our general term for such a matter and/or energy structure is a data structure.


A message contains a data structure and is conveyed (say by sight, sound or electronics) from sender to receiver.

The sender encodes some intended meaning in a data structure. E.g. one bird makes an alarm call.

A receiver decodes some meaning from a data structure. E.g. another bird hears the call and takes flight.


Information is meaning created or found in a data structure – at the point it is created or found.

To find an intended meaning in a message, the receiver must decode it using the same language the sender used to encode it.


Nothing above depends on humans or human-invented technologies.

But the same general communication principles apply to communication between humans and computers.

Human communications

The earliest human brain, though larger than other mammals, was about the same size as a chimpanzee’s brain.

Over the last six or seven million years, the human brain tripled in size.

By two million years ago, homo erectus brains averaged a little more than 600 ml.

And by 300 thousand years ago, early homo sapiens brains averaged 1,200 ml, not far from the average today.


Why this growth?

Three million years ago, human-like primates learnt to make tools with a cutting edge or point.

Humans needed a bigger brain to make and use increasingly complex tools to hunt and cultivate food.

At the same time, intelligence was needed for the increasingly complex language humans used to cooperate.


Neither conceptualisation nor cooperation by communication is unique to humans.

But only humans communicate by inventing words to symbolise things and their qualities.


The spoken word – transient messages

Many non-human animals use sounds to communicate information about things of interest to them.

But they use sounds instinctively, with fixed meanings.

Between 150 and 300 thousand years ago, humans started inventing sounds (words) to convey meanings.

This emergence of speech may well have reflected changes in human society.

Notably, the change from a gorilla-style dominance hierarchy to the more cooperative and egalitarian lifestyle of hunter-gatherers.

Increasingly, humans used words to express descriptions, directions and decisions, and share them with each other.


The ability to create words and assign meanings to them had a profound effect on thinking.

In describing pollen sources, honey bees describe things that resemble each other, but they don’t discuss what those resemblances are.

Words enable humans to discuss the resemblances between things; inventing words such as “pollen source” to label all similar things.


To idealise a thing means to abstract some features or qualities of the thing, and represent them in a symbolic form – such as words.

We observe and envisage realities; we create and use descriptions; our descriptions idealise or symbolise realities.

These three relations can be shown in a triangle.

Human communication

Verbal descriptions

<create and use>         <symbolise>

Humans  <observe & envisage>  Realities


The ability to describe realities in words makes humans unique.

We don’t inherit words with particular meanings; we imitate and invent words.

We can invent words to symbolise infinite concepts – not only realistic ones but also impossible ones, like a flying elephant.

This freedom to invent words and sentences enables creative thinking and scientific postulations.


Words are unreliable; we not only abuse words, we also change their meanings.

The popular meaning of a word can evolve rapidly and change dramatically.

In every oral communication, a word has a meaning when spoken to its creator, and a meaning when heard to a receiver.

There is no guarantee that the two meanings are the same.


Note again: biological evolution has not demanded that words express perfectly accurate descriptions or absolute truths.

It requires only that spoken words are understood well enough, often enough.


See footnote 1 for a little on “how to separate the signal from the noise”


The written word – persistent memories

5 or 6 thousand years ago, people found ways to persist spoken words using written symbols.

Scholars suggest this may have happened separately in Sumeria/Egypt, the Indus River, the Yellow River, the Central Andes and Mesoamerica.

Writing made one person’s thoughts available for inspection and use by others in different places and times.


The invention of writing enabled the development of civilization.

People could do business and conduct trade on the basis of facts recorded on clay tablets or papyrus.


Translating spoken words into and out of written words helped people clarify their thoughts and communicate over distance and time.

The written record revolutionised our ability to think deeply, think straight, remember things and communicate.


One “landmark in the triumph of the centralised written record” recorded the enterprise architecture of a nation state.

After the Norman Conquest of England (1066), King William ordered an audit of locations in England and parts of Wales

The aim was to record who held what land, provide proof of rights to land and obligations to tax and military service.

This survey resulted in The Domesday Book, which classifies towns, industries, resources and people into various types.

Human languages, natural and artificial

Language is not the basis of thinking.

Eons before animals had languages to communicate, they could think.

They could a recognise a thing they had perceived before, even if that thing had changed a little

And so, they could recognise “family resemblances” between any two things they perceive to be similar.

In effect, they could classify things they perceive into fuzzy, informal, overlapping classes or types


Only we humans invent words to describe things, and to describe a thing is to typify it.

The fluidity and fuzziness of a type defined in natural language assists survival and creativity in an ever-changing world.

But to specify the regular or repeatable behaviors of an activity system, we have to be more formal.

We have describe/typify the roles of actors, the messages they exchange, and the rules they follow, more formally.

And to do this in an unambiguous and testable way, we need an artificial, logically consistent, language.


Family resemblances and natural languages

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

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


To describe something as the game is to suggest it is the only one of that named type.

To describe something as a game is to suggest it is one of a set containing many things of that named type.

Wittgenstein considered games as a set, and “game” as a type name.

The set includes activities as varied as chess, archery and Super Mario.

He argued the set members have overlapping lists of features, but no single feature in common.

Thus, Wittgenstein used “game” as an example to tell us that words (in natural language) are not type names.

Rather, games exhibit family resemblances.


This is disappointing if you are a mathematician who had hoped that a word defines the members of a set.

But it is no surprise to a biologist or psychologist coming at this from a different direction.

Natural language is a biological phenomenon rather than a mathematical one.

We use words to indicate one thing resemble another in a loose and informal way.

No word, description or message has a universally-agreed meaning.

And since the words and grammar we use are so flexible, there is ambiguity and fuzziness in natural language

There are degrees of truth in how well a reality matches a description we make of it.


The marvel is not that words are used so loosely in natural language.

The marvel is that we can force them to act as the names of types that do have one or more features in common.

E.g. A biologist might define a game as “an activity that serves as a direct or indirect rehearsal of skills useful to survival.”

And to create a holistic, unambiguous and testable description of a system, we must do this.

We have to create an artificial domain-specific language in which words do act as type names.


Types and artificial domain-specific languages

A. J. Ayer (1910 to 1989) was a philosopher who wrote about language, truth, logic and knowledge.

He rejected metaphysics and much philosophical discussion as meaningless, that is, not provable or disprovable by experience

He pointed out that every coherent description of an individual thing or situation is a type definition.

“In describing a situation, one is not merely registering a [perception], one is classifying it in some way,

and this means going beyond what is immediately given.” Chapter 5 of “Language, truth and logic”


As Wittgenstein indicated, the set of things people call a game is elastic, and it is difficult to agree a type that defines every set member.

However, words do give humans the ability to classify or typify things more formally and rigidly.

Within a bounded context, or domain of knowledge, we can determinedly fix the meanings of words.

A domain-specific language is an island of inter-related words with stable meanings, in the ever-unfolding evolution of natural language.

Words are treated as type names, and each type is defined by statements relating it to other types.

A type defines features (qualities, characteristics, attributes, properties) shared by things that instantiate or realise that type.



In the domain of mathematics, type names include: “number”, “division” and “remainder”.

Type definitions include: “An even number is a number that is divisible by two with no remainder.”


In the domain of physics, type names include “force,” “mass” and “acceleration”.

Type definitions include: “A force equals the mass of a body times its acceleration.”

(By contrast, in the language of management science, a force is a pressure acting on a business, such as competition or regulations.)


In any business domain, people define the rules of their specific business in terms of relations connecting types.

“An employee has a salary and may be assigned to a project.”

“An order is placed by a customer; a customer can place several orders.”

Description and reality - recap

There was no description of reality before life.

Description is a side effect of biological evolution.

Descriptions appear mental phenomena and in external representations of them in speech, writing and models of various kinds.


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

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

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.


With those assertions in mind, here is a short Tractaco Logico Philosophicus - different from Wittgenstein’s.

1.      Reality is what exists in matter and energy.

2.      A description is a representation of a reality, but also a reality in itself.

3.      A true description represents a reality well enough. E.g. my elephant cannot fly.

4.      A false description misrepresents a reality, it is a lie. E.g. my elephant can fly.

5.      A fanciful description represents an imaginary or impossible reality. E.g. the notion of flying elephants.

6.      A true description typifies what is instantiated in one or more realities.

7.      A description is a type or concept composed of one more descriptive qualities or properties.


Nothing said above depends on human language or linguistics.

However, the ability to form descriptions using words (and graphical symbols of them) dramatically extended human descriptive/typification ability.


The logical syntax of language

Rudolf Carnap (1891 – 1970) was a member the Vienna circle who contributed to the philosophy of science and of language.

Carnap has been called a logical positivist, but he disagreed with Wittgenstein.

He considered philosophy must be committed to the primacy of science and logic, rather than verbal language.


Carnap’s first major work, Logical Syntax of Language can be regarded as a response to Wittgenstein 's Tractatus.

“the sentences of metaphysics are pseudo-sentences which on logical analysis are proved to be either empty phrases or phrases which violate the rules of syntax.

Of the so-called philosophical problems, the only questions which have any meaning are those of the logic of science.

To share this view is to substitute logical syntax for philosophy.”

— Carnap, Page 8, Logical Syntax of Language, quoted in Wikipedia.


He defined the purpose of logical syntax thus:

“to provide a system of concepts, a language, by the help of which the results of logical analysis will be exactly formulable.”

“Philosophy is to be replaced by the logic of science – that is to say, by the logical analysis of the concepts and sentences of the sciences...”

Foreword, Logical Syntax of Language, quoted in Wikipedia.


He defined the logical syntax of a language thus:

“the systematic statement of the formal rules which govern [the language] together with the development of the consequences which follow from these rules.

Page 1, Logical Syntax of Language, quoted in Wikipedia.


Carnap’s second major work, Pseudoproblems in Philosophy asserted that many metaphysical philosophical questions were meaningless.

His Principle of Tolerance says there is no such thing as a "true" or "correct" logic or language.

His concept of logical syntax is important in formalising the storage and communication of information/descriptions.

Computers require that logical data structures are defined using a formal grammar called a regular expression.

It is said that Carnap’s ideas helped the development of natural language processing and compiler design.


As I understand it, Carnap said:

A statement is only meaningful with respect to a given theory - a set of inter-related domain-specific predicate statements.

And only true to the extent it can be supported by experience or testing.

Propositional logic

Again, to create a holistic, unambiguous and testable description of a system, we need an artificial domain-specific language.

A natural language can contain several popular and alternative meanings for the same word.

By contrast, in a domain-specific language, terms are fixed to meanings and defined with reference to each other.


To define a domain-specific vocabulary, we make statements in a disciplined manner.

The basic logic of statements is called propositional logic (or calculus).


A proposition is a statement that asserts a fact that may be true or false.

In natural language it takes the form of a sentence: e.g. the sun is shining.

A predicate is a verbal phrase, with or without an object, which declares a feature of a subject.


Proposition (in the form of a predicate statement)



Verb phrase


A particular thing

or instance of a general type

A verb or verbal phrase

that either stands alone or

relates the subject to the object

A particular thing or a general type

related to the subject by the predicate.

The sun

is shining

A game of monopoly

results in

a winner with the largest credit amount

A game

is kind of


A game

is played by

one or more animate players

A game

results in

a measure of achievement

An order

is placed by

a customer

A customer

has placed

one or more orders


A subject or object above might be read as one instance in a set.

E.g. “An instance of the customer type has placed one or more instances of the order type”.


Compound propositions

Connectives (e.g. and, or, not and if) express logical relationships between predicates in a compound proposition.

E.g. pigeons fly <and> eat corn.


Predicate logic

Propositional logic is the foundation of first-order or predicate logic.

Here, a predicate is a statement that contains variables; it  may be true or false depending on the values of these variables.

For more on predicate logic, try

Types and instantiations of them

To paraphrase von Foerster: “We live in the domain of types that we invented.”

The types idealise and symbolise the realities we observe and envisage.

These relations can be shown in the triangle you may now becoming familiar with.




<invent>                 <symbolise>

Human intelligences <observe & envisage> Realities


A type can be presented as a proposition with a compound predicate.

E.g. “A game is a kind of activity <and> is played by one or more animate players <and> results in a measure of achievement>.”


The words in a domain-specific language serve as type names.

A predicate statement defines (or partly defines) a type by relating it to other types in the same language. 7

Generally: “An instance of type A is related by a verb phrase to one or more instances of type B.”

E.g. “An instance of the customer type has placed one or more instances of the order type”.


A type is a description; moreover, a description may be viewed as a type.

Any coherent description, even a long and complex one, can serve as a type definition.

E.g. To describe the Statue of Liberty is to describe something of which many instances could be made.


Fuzziness in the instantiation of a type

The matching of a thing to a type can be incomplete.

Given a monothetic type like “even number”, every instance must have all its features.

Given a polythetic type like “game”, an instance need not have all its features.


Moreover, there can be degrees of truth in a predicate statement.

Newton’s laws describe the motion of things in the reality we normally experience.

The laws are true to the degree of accuracy we need, but only approximations, neither wholly true nor wholly false.

For more on fuzzy logic and fuzzy sets, try this link.

Systems as complex types

Humans have long classified people using types.

The Greeks divided dramatic roles into types: hero, ingénue, jester and wise man.

In the 11th century, The Domesday Book classified people into types according to their rank and role in a feudal society.


1800s Social systems thinking

In the 19th century, sociologists, taking a lead from biologists, looked at whole societies as systems.

This paper reviews ideas promoted by Adam Smith, Charles Darwin, Claude Bernard, Herbert Spencer, and Vilfredo Pareto.

Also Emile Durkheim, Gabriel Tarde, Max Weber, Kurt Lewin, and Talcott Parsons.


David Seidl (2001) said the question is what to see as the basic elements of a social system.

“The sociological tradition suggests two alternatives: either persons or actions.”

Is a system a set of actors who perform activities; or a set of activities performed by actors?

The system theory below is concerned with activity systems.


1900s System theory

After the Second World War, the general concept of a system became a focus of attention.

To begin with, the interest was in biological and mechanical systems.

And all systems in which entities process information encoded in memories and messages.

And more generally still, systems that are islands of orderly behavior in the ever-unfolding process of the universe.


How to design activity systems to meet some aims?

We can define the aims, design activities to meet them, then consider what actors are needed to perform those activities.

We can describe the system by typifying its activities in role and rule definitions.



Cybernetics focuses attention on how information feedback loops connect systems.

A control system receives messages that describe the state of actors in a target system.

In response, the control system sends messages to direct the activities of those actors.


E.g. In a missile guidance system, a control system senses spatial information and sends messages to direct the missile.

A brain holds a model of things in its external environment, which an organism uses manipulate to those things.

A business database holds a model of business entities and events, which people use to monitor and direct those entities and events.

And (as Michael A Jackson taught me in the 1970s) a software system holds a model of entities and events that it monitors and directs in its environment.



Roles, Rules & Variables

<create and use>                   <symbolise>

Systems thinkers <observe & envisage> Actors, Activities & Values


The science of cybernetics was quickly embraced within a broader system theory movement.


General system theory

Thinkers looked for what it is common to systems in all disciplines, from hard sciences to the humanities.

General system theory incorporates cybernetic concepts such as:

·         System environment: the world outside the system of interest.

·         System boundary: a line (physical or logical) that separates a system from is environment.

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

·         System state: the current structure or variables of a system, which changes over time.


System theorists distinguish abstract system descriptions from concrete entities that instantiate (realise) them.

Again, a system description is a complex type; it symbolises both the structures and the behaviors of each entity that realises the system.


General system theory

Abstract / theoretical systems

<create and use>                    <symbolise>

System theorists <observe & envisage>  Concrete / empirical systems

Describing a human activity system

The systems of interest here are islands of orderly behavior in the ever-unfolding process of the universe.

Especially human activity systems in which processes depend on messages received and memories retained

And computer activity systems in which all the messages and memories are digital.

Both kinds of activity system are specifiable using the same general tools and an artificial domain-specific language.


How to specify a message or memory?

We use a domain-specific language to specify the data types contained in messages and memories.

We define each data type in terms of a generic data type (number, text, date etc.) plus a domain-specific meaning.

We relate the data types in larger structures

We define the structure of a message using the grammar of a regular expression (as in a structure of sequences, selections and iterations.

We define the structure of a memory using the grammar of predicate logic (as in an entity-attribute-relationship model.


How to specify a system actor?

We define the role of the actor in terms of the activities they are expected to perform.


How to specify a system activity?

We define the inputs and outputs of the activity and the rules that apply to it

A rule definition states a precondition or post condition of an activity.

Each rule refers to the particular values of data types created and used by the activity.


Charles Antony Richard Hoare (1934 - ) is a British computer scientist.

Few have taken up his work on formal specification languages such as CSP and Z.

But many use Hoare logic to describe how an activity changes the state of a system.

The logic is based on the Hoare triple, which may be expressed as: {Precondition} Activity {Post condition}.

The meaning is: If the precondition is true AND the activity proceeds to completion THEN the post condition will be true.


Hoare logic underpins many ways to analyse requirements and define business activities.

It can be seen in definitions of “value streams”, “business scenarios”, “use cases” and “service contracts”.


How to specify a process that connects activities in a logical flow?

We use the concept of an algorithm, which was known to Greek mathematicians and was formalized in the 1930s.

(See Wikipedia for references to Gödel–Herbrand–Kleene, Alonzo Church, Emil Post and Alan Turing.)

We often specify an algorithm using a process flow chart.

Describing a computer activity system

The concept of an activity system is part of general system theory

And it is the kind of system discussed in “Introduction to Cybernetics” (1956) by W Ross Ashby.

By accident or intent, humans rarely realise an activity system perfectly in accord with its general description.

That fuzziness in instantiating a system type is surely vital to success of human society.

But computers can and do realise an activity system perfectly in accord with its general description.

And a software system can be seen as an exceptionally perfect example of Ashby’s system.


Software engineering exemplifies the scientific method

Scientists develop a theory that typifies how structures in the universe behave.

Then run experiments to test that the structures do behave that way.

Similarly, software developers write code that describes process types (behaviors) that create and use data types (structures).

Then runs test to show that data is processed as expected.


Software engineering exemplifies a philosophy of description and reality

Software engineering illustrates the philosophy expressed in this triangular graphic.




<create and use>           <symbolise>

Living entities  <sense & envisage>  Realities


In general, a description of real world behavior is more or less accurately performed by actors.

Moreover, the actors may do more or less than is described.


Software is strange and wonderful in that it perfectly aligns description and reality.

The software is a description of system behavior that can and will be perfectly performed.

The computer actor can only behave as described in the software.


There may be a perfect correspondence between description and reality.

But don’t confuse the software with the run-time operation of the system.

The software version of the triangle is this.



Software system code

<creates>                     <symbolises>

Programmer <envisages> Run-time system operation


The software code is a relatively simple abstract description

The run-time system operation a very complex, electrically powered machine that reads the software and performs to the instructions in it.

Conclusions and remarks

The science of systems thinking

System architects observe baseline systems, envisage target systems, and describe both.

This paper traces the pre-history of systems thinking and concludes with a few modern ideas.

It discusses the relationship of real world actors and activities to descriptions of them (as in data structures).

And the specification of rules as pre and post conditions of activities (as in business processes).

Below are some of the points made above.


Reality and descriptions of it

·         The systems of interest here are islands of orderly behavior in the ever-unfolding process of the universe.

·         Especially systems in which entities act (systematically) in response to information encoded in messages and memories.

·         And usually, systems in which that information describes or directs some entities or events in reality.

·         Our descriptions of reality are digital in the sense that we divide reality into discrete entities (structures) and events (behaviors). 2


Animal memories and messages

·         Only some descriptions of reality prove useful when tested.

·         Communication requires that a receiver decodes the same meaning from a message that a sender intentionally encodes in that message. 3


Human communications

·         Only humans invent words to symbolise things and their qualities.

·         The written record revolutionised our ability to think deeply, think straight, remember things and communicate. 5


Human languages, natural and artificial

·         The fluidity and imprecision of natural language enables human creativity and assists survival in a changing world.

·         For a system description to be holistic, unambiguous and testable, an artificial domain-specific language is needed.

·         A domain-specific language is an island of inter-related words with stable meanings, in the ever-unfolding evolution of natural language.


Thinking about systems  9

·         A system description is a complex type that symbolises the structures and the behaviors of each entity that realises the system.

·         To make testable assertions about a system’s behavior, we specify processes by their pre and post conditions.

·         A concrete system is composed of actors performing activities.

·         An abstract system typifies actors in role descriptions and activities in rule descriptions.

·         A role is a list of activities performable by an actor.

·         A rule is a precondition or post condition of an activity.


By the way, some systems thinkers speak of systems maintaining order, or “negative entropy”.

It turns out that thermodynamics is tangential to most practical applications of general system theory.

Having said that, a few notes on thermodynamics are included in the footnotes below.


Read Systems thinkers and their ideas for more on the history of systems thinking in the 19th and 20th centuries.

On side issues, other papers of possible interest include:

This paper for a philosophy based on the triangular graphics included above.

Personality classification for more on personality types.

The Domesday Book for more on that.

The philosophy of systems thinking

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


What “existence” means is open to question.

Matter and energy exists, but is deeply mysterious, beyond our full comprehension.

Our perceptions, descriptions and mental models of material reality also exist in material form.

Both in mental phenomena and in external representations of them in speech, writing and other forms.


Contrary to Cartesian dualism, the modern view (cognitive embodiment) sees the mind as a part of the body rather than separable from it.


Wisdom is the ability to respond effectively to knowledge.


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

Knowledge represents what exists well enough to help us manipulate what exists, and predict its behavior.

Knowledge is acquired in various ways.

We learn from a mix of

·         evidence, experience of the world,

·         education/interaction by/with others

·         logical analysis.

I believe assertions should be tested against evidence, but also that some things can be concluded from education and logical analysis.

After all, education and logical analysis are products of biological evolution that have proved useful to the survival of our species.

The members of a species necessarily see the world similarly, since our ability to perceive, remember and discuss the world evolved over millennia to represent the world accurately enough that we can determine our actions, and cooperate socially, to survive.


Radical constructivism and post-modernism are dangerous in that they undermine science and its importance to society.


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

The hermeneutics principle makes innocent speakers guilty of causing offence where none was intended.

What matters, what must be investigated, is whether speakers and hearers share the same language for encoding and decoding a message.


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.


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.


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.


Both holist and reductionist views of a system are important and helpful different times.

Enterprise architecture is deprecated by some “systems thinkers” as being rationalist or deterministic or reductionist.

The implication is that other kinds of “systems thinking” are better for being not rationalist, or not deterministic, or not reductionist.

In practice, both enterprise architects and systems thinkers take either or both positions, according to the problem domain or work to be done.


Footnote 1: Describing the world in terms of structures and behaviors

We humans all perceive and describe the universe in the same very general way.

That is, we see it as composed of objects that occupy space at a moment in time and change over time.

Our descriptions of reality are digital in the sense that we divide it into discrete entities (structures) and events (behaviors).


“Modern physics strongly suggests ... reality is very much like what was inferred by some remarkable thinkers in the ancient world:

a universe composed of elementary objects that move around in an otherwise empty void.” Postmodern Attacks on Science and Reality


Even physicists have invented different ways of describing objects and their motions.

In classical physics, human-scale structures and behaviors are described as continuous in space and time (cf. analogue signals).

In quantum mechanics, tiny atoms, particles and changes are described as discrete objects in space and jumps in time (cf. digital signals).


But note that neither of those descriptions is only a speculation.

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.

(A later paper discusses converting a discrete-event-driven system model into a continuous system dynamics model.)

Footnote 2: How to separate the signal from the noise?

What if the receiver of a message use words a little differently from the sender?

As senders, we commonly overload communications with redundant information.

Describing one thing in several ways reduces the chances of miss-communication.


What if the message we send may lose some data in transit?

We may add some redundant data, or repeat the whole message.


What if the message we send may gain some meaningless noise in transit?

How to separate the signal from the noise?

It depends what you mean by the question.

Because the phrase “signal-to-noise ratio” has one scientific meaning and one or two metaphorical meanings.


What if a message may gain some meaningless noise in transit?

How to separate the signal from the noise?

It depends what you mean by the question.

Because the phrase “signal-to-noise ratio” has one scientific meaning and one or two metaphorical meanings.


Signal-to-noise ratio in engineering

The strength of an electrical or other signal carrying information, compared to that of unwanted interference.

Here, the signal is the data encoded by a sender within a message.

The receiver wants to remove or ignore any noise that gets added to data, in order to find the original signal/data.


Signal-to-noise ratio in sociology

The ratio of useful information to false or irrelevant data in a message or series of messages.

Here the signal is the message(s) that contain data encoded by senders.

The receiver wants to remove or ignore data that they regard as misleading, mistaken or irrelevant to their particular interest.


Signal-to-noise ratio in data analysis

Here the signal is a conclusion to be drawn from examining a sample of data values.

The conclusion is made by ignoring small, random or statistically insignificant variations.

And by focusing on the large variations that matter most to the observer.

Footnote 3: Numbers as types

Did numbers exist before life?

It is arguable that numbers only emerged when animals could recognise similarities between discrete things.

And that mathematics only developed when we could describe those similarities - as types.


Animals evolved to

·         perceive the universe in terms of discrete things in space.

·         recognise similarities between things - such as food items and enemies.

·         recognise if a group of similar members gained or lost a member (experiments show babies do that before they have words).

·         count members in a group of somewhat similar things.


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


Then, we humans evolved the ability to

·         create words, to suggest and discuss similarities between things

·         abstract communicable descriptive types from reality.


Numbers can be seen as types we use to describe groups containing somewhat similar members.

·         “onesomeness” is the property shared by all groups with one thing in.

·         “twosomeness” is the property shared by any onesome to which we have added one.

·         “empty (zero)” is the property of any group from which we have removed all members.


The question is not so much whether there were numbers before life.

It is whether there were any types before life.

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

The infinite variety of types we manipulate depend on our ability to identify similarities between things and typify them.


However, there were always things that can (in retrospect) be regarded as similar.

This was first true at the level of atomic particles, then stars and planets.

Numbers did not exist in the form of types before life forms started to create, remember and communicate types

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

Footnote 4: Thermodynamics

Some systems thinkers speak of systems maintaining order, or “negative entropy”.

It turns out that thermodynamics is tangential to most practical applications of general system theory.

“Cybernetics depends in no essential way on the laws of physics.”

“In this discussion, questions of energy play almost no part; the energy is simply taken for granted.” Ashby

Having said that, a few notes on thermodynamics are included below.


The need for energy to maintain order

Generally, a system is an island of orderly behavior in the ever-unfolding process of the universe.

To maintain order (or negative entropy) in its structures and behaviors, a system must consume energy.


Ludwig von Bertalanffy (1901-1972) considered an organism as a thermodynamic system in which homeostatic processes keep entropy at bay.

“By importing complex molecules high in free energy, an organism can maintain its state, avoid increasing entropy…."


Observation: while homeostasis was a focus of many early system theorists, it is not a property of all systems.

The fact is that social and business systems can grow, shrink, die and produce chaotic outcomes.


Information as a subtype of order

Erwin Schrödinger (1887 –1961) also discussed the thermodynamic processes by which organisms maintains themselves in an orderly state.

 “Living matter evades the decay to thermodynamical equilibrium by homeostatically maintaining negative entropy (today this quantity is called information) in an open system.”

“The increase of order inside an organism is more than paid for by an increase in disorder outside this organism by the loss of heat into the environment.” Cornell University web site.


Observation: In 2009, Mahulikar & Herwig re-defined the negative entropy (negentropy) of a dynamically ordered sub-system.

Negentropy = the entropy deficit of an ordered system relative to its surrounding chaos.

Negentropy might be equated with “free energy” in physics or with “order”; some equate it with "information".

But in cybernetics and systems thinking "information" usually has a more specific meaning.

Information is the meaning created or found by an actor in a description of a reality.

To encode meaning in a description (or data structure) is to use some energy to create a very specific kind of order.


The tendency of systems in competition to optimise their use of energy

“Nature's many complex systems--physical, biological, and cultural--are islands of low-entropy order within increasingly disordered seas of surrounding, high-entropy chaos.

Energy is a principal facilitator of the rising complexity of all such systems in the expanding Universe, including galaxies, stars, planets, life, society, and machines.

Energy flows are as centrally important to life and society as they are to stars and galaxies.

Operationally, those systems able to utilize optimal amounts of energy tend to survive and those that cannot are non-randomly eliminated.” Cornell University web site.


Observation: this “optimal use of energy” principle has been at work in the evolution of biological systems.

But where minimising energy consumption is of little or no advantage, evolution proceeds in a suboptimal way.


The tendency of systems, where resources are cheap, to sub-optimise use of energy

The highest energy consumption per head is not found in countries that are especially orderly.

Energy consumption is highest in countries that are:

·         Too cold: Iceland, Canada,

·         Too hot: Trinidad and Tobago, Qatar, Kuwait, Brunei Darussalam, United Arab Emirates, Bahrain, Oman, or

·         Too rich to care about the cost: Luxembourg, and the United States.


Many modern software systems are over complex and suboptimal, because we give them as much memory space and electricity as they need.

Footnote 5: Postmodern Attacks on Science and Reality

Victor J. Stenger, Ph.D.


Recent trends in some academic circles have called into question conventional notions of truth and reality. The claim is made in these circles that all statements, whether in science or literature, are simply narratives -- stories and myths that do nothing more than articulate the cultural prejudices of the narrator. In this view, one narrative is as good as another, since each is expressed in the language of its particular culture and thus contains all the assumptions about truth and reality embedded in that culture. Texts have no intrinsic meaning. Rather, their meanings are created by the reader. The conclusions are then drawn that no narrative can have universal validity and that "Western" science is no exception..


Today's college students, in the United States and elsewhere, hear this line of reasoning from many of their social science and humanities professors. "Alternative medicine" proponents often use similar arguments to reject science as a method of determining health-related truths.


The assertion that "Western" science is unexceptional begins with a plausible, though ultimately misleading, notion that we humans lack access to any mechanism by which we can learn the truth about an objective reality that exists independent of human thought processes. Certainly, science relies on thought processes and does not always follow a clear, logical path to the conclusions it makes about reality. True, it never proves the correctness of these conclusions. Science knows nothing for certain about the world and must always couch its results in terms of probabilities or likelihoods. Often the choice between competitive scientific theories is based on taste, fashion, or subjective notions of simplicity or aesthetic appeal.


Agreed. Scientists can never be certain of the "truth" of their theories. Nevertheless, the predictions of scientific theories are very often sufficiently close to certainty that we all bet our life on them, such as when we are in an airliner or on an operating table. When predictions are that reliable, we can rationally conclude, if not prove, that the concepts on which they are based must have some universal validity. That is, they must somehow be connected to the way things really are.


For example, we cannot predict with complete certainty what will happen if we jump off a tall building. It is always possible that we might land in a crate of feathers that, by luck, just happens to protrude from a window on the floor below. However, based on the law of gravity, we can predict with high likelihood that we will pass that floor and hit the ground with an unhealthy splat. The law of gravity has been tested with enough experiments to safely conclude that the concept of gravity is "real."


Reality acts to constrain our observations about the world, preventing at least some of those observations from being completely random, arbitrary, or what we might simply like them to be. Although much of what we do in fact observe is random -- far more than most people realize -- not everything is. And while we humans can exert a certain amount of control over reality, that reality is not merely the creation of our thought processes. In a dream about jumping off a building, we might float to the ground unharmed. In thinking about jumping off the building, we can imagine whatever we want about the outcome. Superman can fly by and rescue us, in our fantasies. An airplane with a mattress on its wings can appear just in time.

But, in reality, we fall to the ground no matter how we might wish otherwise.


Without getting too pedantic about defining reality, let me just say that our own observations in everyday life make it quite clear that we and the objects around us are subject to externally imposed constraints that neither we nor those objects can completely control. If I could control reality with my thoughts, I would look like I did when I was twenty and still be as smart as I am now. I don't. In science, we use our observations about what happens when we are not dreaming or fantasizing to make reasonable inferences about the nature of what supplies the impetus for the constraints we record with our measuring apparatus.


Modern physics strongly suggests a surprisingly uncomplicated, non-mysterious "ultimate reality" that may not be what we wish it to be, but is supported by all known data. Furthermore, this reality is very much like what was inferred by some remarkable thinkers in the ancient world: a universe composed of elementary objects that move around in an otherwise empty void. I call this atomic reality.


This proposal flies in the face of current fashion. That fashion repudiates all attempts, within science and without, to describe a universal, objective reality. I repudiate that fashion. Where the validity of certain ancient and modern concepts of truth and reality are denied, I affirm them. Where arguments are made that Western science tells us nothing of deep significance, I assert that it remains our foremost tool for the discovery of fundamental truth.

Many natural science professors, with their heads buried mainly in research, have ignored the attacks on science and rational thought. When they happen to hear assertions that science is just another tall tale, they typically dismiss the notion as nonsense. Instead, they should be speaking out.


Dr. Stenger is professor of physics and astronomy at the University of Hawaii. He received doctoral degree from UCLA in 1963 and has had an active research career in elementary particle physics and astrophysics. His projects have included elaborating the properties of quarks, gluons, neutrinos, CP violation, and the weak neutral current. He has worked on high-energy gamma ray andneutrino astronomy. He is currently a collaborator on Super-Kamiokande, an experiment in a mine in Japan that recently confirmed the solar neutrino anomaly and is expected to be the decade's most definitive experiment on solar neutrinos, proton decay, and neutrino oscillations. His writings include many articles for skeptical publications and three books published by Prometheus Books: Not By Design: The Origin of the Universe Physics (1988); Psychics: The Search for a World Beyond the Senses (1990); and The Unconscious Quantum: Metaphysics in Modern Physics and Cosmology (1995), which the Times Literary Supplement described as "an interesting, provocative, informative and impassioned attempt to rescue physics from the contemporary unscientific or anti-scientific appropriations of its softer-edged theoretical self-description."