Some thinkers and their ideas (short)

Copyright 2017 Graham Berrisford. One of several hundred papers at Last updated 20/09/2018 20:33


The role of system architects is to observe baseline systems, envisage target systems, and describe both.

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


It doesn’t help that sources you can find on the internet are inconsistent.

Some suggest system theory is a branch of sociology.

“Systems theory, also called social systems theory...

Some suggest the reverse, that social systems thinking is branch of general system theory.

“Though it grew out of organismic biology, general system theory soon branched into most of the humanities.” Laszlo and Krippner.


This paper lists some thinkers over the last 300 years.

You can read a little more about them and their ideas in the long version of this paper.

Description and reality: system as types

We have no need of meta physics to explain concepts.

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

Your mental models must represent (model/describe/conceptualise) the world well enough else you could not survive.


Heinz von Foerster may have said we live in the domain of descriptions that we invented.

But that does not mean we live in a world we have invented.

Millions of years ago, animals evolved to conceptualise the world in their brains.

Animals also evolved to communicate concepts to each other - well enough.

Honey bees manage to communicate the direction and distance of a pollen source - well enough.


What made humans unique (c100,000 years ago) was the ability to communicate by symbolising concepts in words.

And the amazing side effect of this was that it enabled us to classify or typify things in an infinite variety of ways

Honey bees recognise real world pollen sources, which must have at least one feature in common.

The ability to symbolise features in words helps us to abstract types such as “pollen source” from realities.

Human intelligence is based on the ability to abstract from the realities we observe and envisage, to create and use descriptive types.



Systems thinking


<create and use>                 <idealise>

Human intelligences <observe & envisage>  Realities

System descriptions

<create and use>                 <idealise>

System thinkers <observe & envisage>  Actors & Activities


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

·        A polythetic type does not imply that all instances of the type will have all its features.

·        A monothetic type, as in mathematics, does require all instances of the type to have all its features.

An abstract system description is a type that defines the roles and rules of entities that instantiate (realise) that type.

·        A “soft” system description of human roles does not necessarily imply that all actors will follow all the rules exactly.

·        A “hard” system description of software classes does require all software objects to follow all the rules exactly.

Some steps in the evolution of social groups and thought

It is more than 3,000 million years since life on earth began, and 500 million years since animals emerged.

By 100 million years ago, animals had evolved ways to cooperate in a social group (or social entity).

This preface recaps some highlights in the evolution of human society.


100 million years ago - communication.

Social animals can share meaning or information with other animals, usually of their own species.

E.g. One honey bee can communicate key facts of their pollen-collecting business to another honey bee.


Honey bee communication

Wiggle dances

<perform & read>        <describe>

Honey bees  <discover & find>  Pollen sources


3 million years ago: technology – the stone age

100 thousand years ago: speech - humans evolved to symbolise their thoughts in the form of words.

We use them to express descriptions, directions and decisions, and share them with each other.


Human communication


<create and use>                   <idealise>

Humans        <observe & envisage>         Realities


A word has no intrinsic meaning; it has meaning only at the moments it is created and interpreted.

Philosophical debates can arise when creators and users ascribe different meanings to the same words.

Read this paper for a philosophy based on the triangle above.


3 thousand years ago: writing.

1300s Thinking about thinking – the Renaissance.

1400s Dissemination of thinking – the printing press.

1500s Sociological thinking – Machiavelli.

1600s Scientific thinking – Newton


You can read a little more about these ideas in the long version of this paper.

Some thinkers who foreshadowed systems thinking

Here are some thinkers whose ideas (in biology, economics and sociology) can be seen in some branch of systems thinking today.

·        Adam Smith (1723 to 1790) subdivision within and competition between systems.

·        Charles Darwin (1809 to 1882) system mutation by reproduction with modification.

·        Claude Bernard (1813 to 1878) homeostatic feedback loops.

·        Herbert Spencer (1820 to 1903) social systems as organic systems.

·        Vilfredo Pareto (1848 – 1923) the Pareto principle.

·        Emile Durkheim (1858-1917) collective consciousness and culture.

·        Gabriel Tarde (1843 –1904) social system as emergent from the actions of  individual actors.

·        Max Weber (1864-1920) a bureaucratic model.

·        Kurt Lewin (1890–1947) group dynamics.

·        Talcott Parsons (1902-1979) action theory.


You can read a little more about these thinkers’s ideas in the long version of this paper.

Some systems thinkers

Classical cybernetics - the science of system control

After the second world war, the general system concept became a focus of attention.

The science of cybernetics was established, and then embraced within a broader system theory movement.

Cybernetics is the study of systems - natural and designed – and how they interact with each other.


Cybernetics is about systems in which biological and/or mechanical actors perform regular activities.

Those activities include consuming, remembering and producing information.

The information is contained in signals/messages and memories that describe or direct some part of reality.

E.g. A control system consumes signals/messages that describe the state of actors in a target system.

The control system produces signals/messages that direct the activities of actors in a target system.

Thus, the control and target systems are connected by an information feedback loop.

These cybernetic ideas appear in systems of many kinds - biological, business, social and software.


The Ratio Club, which met from 1949 to 1958, was founded by neurologist John Bates to discuss cybernetics.

Many of its 21 members went on to become prominent scientists - neurobiologists, engineers, mathematicians and physicists

It members included Ross Ashby and Alan Turing, two of the three thinkers whose ideas are introduced in the long version of this paper.

·        Norbert Wiener (1894-1964) the science of system control.

·        W. Ross Ashby (1903-1972) the law of requisite variety.

·        Alan Turing (1912 –1954) artificial intelligence.


In the 1950s, Turing envisaged that computers would give us insights into how the brain works.

Here, the brain’s abilities are more interesting than its workings.

Systems thinking involves typifying actors using roles, activities using rules, and qualities/values using variables.

The types in systems descriptions range all the way from loosely-defined human roles to rigid software classes and data types.



Roles, Rules & Variables

<create and use>                   <idealise>

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

General system theory – the cross-science notion of a system

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.

And adds some more ideas.


General system theory

Abstract / theoretical systems

<create and use>                    <idealise>

System theorists <observe & envisage>  Concrete / empirical systems


The 1954 meeting of the American Association for the Advancement of Science in California was notable.

Four people at the meeting conceived a society for the development of General System Theory.

They included three thinkers whose ideas are introduced in the long version of this paper.

·        Ludwig von Bertalanffy (1901-1972) establishing a system theory general to all sciences.

·        Kenneth Boulding (1910-1993) applying general system theory to “management science”.

·        Anatol Rapoport (1911 to 2007) game theory and social network analysis.

System Dynamics – animating a system model to predict long-term outcomes

The state of a system is dynamic - it changes over time.

A system’s quantitative state variables may change in one of two ways – homeostatically or progressively.

System dynamics is used model quantitative system changes in a way that can be animated.


System dynamics is a kind of group dynamics that considers interactions between groups (rather than within a group).

It enables us to simulate the effects of flows (interactions) between different stocks (population or resource quantities).

The animation reveals the trajectory of changes over time to the size of a group, population or resource quantity.


System Dynamics

Stock and flow models

<create and use>                 <idealise>

System modellers <observe & envisage>  Interdependent quantities


Here are two system dynamics gurus whose ideas are introduced in the long version of this paper.

·        Jay Forrester (1918 to 2016) every system is a set of quantities that are related to each other.

·        Donella H. Meadows (1941 to 2001) resource use, environmental conservation and sustainability.

Soft systems thinking – loosening the system concept

Bertalanffy didn’t like some directions in “the System Movement”, especially those specific to one science.

But he saw the movement as “a fertile chaos” that generated many insights and inspirations.

General system theory doesn’t start from or depend on sociology, or analysis of human behavior.

However, it stimulated people to look afresh at social systems in general and business systems in particular.


The term “soft system” emerged in the 1970s; but what does it mean?

And is it really any different from a hard system?

Here are three soft system thinkers whose ideas are introduced in the long version of this paper.

·        Peter Checkland (born 1930) the Soft Systems Methodology.

·        Stafford Beer (1926- 2002) management cybernetics and the Viable System Model.

·        Russell L Ackoff (1919-2009) human organisations as purposeful systems.

Second-order cybernetics – undermining the system concept

Checkland observed the distinction between hard and soft system approaches is slippery.

The distinction between hard and soft systems is also questionable.

By contrast, the distinction between classical and second-order cybernetics is fundamental.


Remember Ashby insisted we distinguish two kinds of system change, which need different names here.

·        Regulation: regulating the values of defined state variables to stay within a desired range.

·        Re-organization: changing the state variables, or the rules that update variable values..


Classical cybernetics relates to self-regulating systems.

Regulation refers to how the roles and rules of a system maintain the values of given state variables.

Second-order cybernetics relates to self-organising systems; it is the recursive application of cybernetics to itself.

It was developed between approximately 1968 and 1975 by Margaret Mead, Heinz von Foerster and others.

It allows systems actors to be system thinkers, who re-organise themselves.

So, actors can not only play roles in a system, but also observe and change the roles, rules and state variables of that system.



Second-order cyberneticians speak of social groups and businesses as complex adaptive systems.

IBM is certainly a complex, adaptive, self-organising entity; but is it a system?

If the roles and rules of IBM are continually modified, there is no describable or testable system.

How to extend classical system theory to embrace second-order cybernetics?

The answer is given in the conclusions below.

Conclusions – restoring the system concept

Understanding systems involves drawing three distinctions.

There are forms and functions – actors and activities - within a system.

There are accidental and purposive - natural and designed - systems.

There are descriptions and realisations - abstract and concrete systems.


Differentiating social groups, systems and cells

To call every problem, situation, society or social group “a system” is unhelpful.

We can distinguish social groups from the systems they realise.


A social group (or social entity)

A social system

a set of actors who perform activities they choose.

a set of activities performed by actors.

a continually evolving entity.

a set of roles and rules described and changed under change control.

a concrete entity in the real world.

only that part of a real world entity that conforms to a system description.


To social groups and social systems, one may add social cells.

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


Restoring the system concept to second order cybernetics

How to extend classical system theory to embrace second-order cybernetics?

The answer is to:

·        Distinguish a social group (composed of actors) from the many social systems it can realise.

·        Distinguish meta systems from operational systems.

·        Allow actors to switch between roles as rule followers in operational systems and rule definers in meta systems.


The trouble is, if the roles and rules of a system are continually modified, there is no describable or testable system.

How to maintain the integrity of the system concept?

The answer is to insist that actors make incremental (generation-by-generation) rather than continual changes to system roles and rules.


Thus, it is the social group (not the social system) that has self-organizing dynamics.

Read System Stability and Change for more on separating the meta system from the system


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