A brief history of systems thinkers

Copyright 2017 Graham Berrisford. One of about 300 papers at http://avancier.website. Last updated 03/04/2018 22:13


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

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


It is difficult to find a source that is reliable about the beginnings of system theory or systems thinking.

“Systems theory, also called social systems theory... https://www.britannica.com/topic/systems-theory

Systems theory is not the preserve of sociology!

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

Social system thinking did not emerge from general system theory!


Nobody can get the history right.

And you might well conclude some of the thinkers below were naive or academic fantasists.

But this is not a list of the greatest, the wisest or the most effectual.

It is only an attempt to provide a historical context for the main work to follow.



 human organizations as purposeful systems




 the Viable System Model


 homeostatic feedback loops


 general system theory


 system theory in management science


 the logic of science and data processing


 soft systems methodology


 system mutation by reproduction with modification


 collective consciousness and culture


 second order cybernetics


 System Dynamics


 universal pragmatics


 meanings communicated in interactions between actors playing roles


 declarative specification of system state change


 autopoietic social systems

Marx and Engels

 dialectic materialism


 System Dynamics


 social system as homeostatic


 action theory


 using energy to maintain order


 decision making


 subdivision with and competition between systems


 social systems as organic systems


 social system as emergent from the actions of  individual actors


 a bureaucratic model




 the logic of natural language



Sharing thoughts and descriptions of the world



Many kinds of animal share meaningful information about the world, using sounds, movements and other signals.

Experiments have shown that honey bees are able to count and communicate quantities up to four.


3 million years ago: tools

The Stone Age lasted more than 3 million years, during which stone was used to make implements with a cutting edge or point.

If we date human beings to the beginning of the stone age, then we humans spent most of our time in it.

And if the Stone Age started a year ago, then it ended about 3 pm on Dec 31st .


100 thousand years ago: speech

Modern human kind is set apart by the ability of people to share mental models of the world, by symbolising them in spoken words.

Human language and speech seem to have evolved between 100,000 and 50,000 years ago.

This is relatively late in hominid evolution, but critical to our cognitive abilities.


5 thousand years ago: writing

If the rise of civilization is taken to coincide with the development of writing, then civilisation started 3 or 4 thousand years before BC.

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

Human thinking became more disciplined when people learnt to translate spoken symbols into and out of written symbols.

This brief history of systems thinkers starts from one particular landmark in the history of writing.


1 thousand years ago: enterprise architecture on a national scale

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 that “landmark in the triumph of the centralised written record” called The Domesday Book.


1300s Thinking about thinking

Alessandro della Spina of Pisa made reading glasses “and shared them with everyone with a cheerful and willing heart."

Petrarch led the recovery of knowledge from the writers of Rome and Greece; he is often credited with initiating the Renaissance.

The Renaissance was the “rebirth” of thinking inspired by the rediscovery of ancient Roman and Greek thinkers.

Debates today about the nature of description and reality still refer back to Plato and Aristotle.


1400s Dissemination of thinking

The invention and spread of the printing press was one of the most influential events in the second millennium

The printing press enabled the mass communication of ideas, and fundamentally altered society.

Also, the quadrant was developed to help sailors find their way, which facilitated world-wide exploration.


1500s Sociological thinking

Desiderius Erasmus’s “In Praise of Folly” showed many in the church did not live holy lives.

Machiavelli’s “The Prince” showed people who seek political power often do wicked things to get it.

William Shakespeare wrote plays about power and politics in history.

Also, watches were made by Peter Henlein of Nuremberg, assisting more efficient use of time.


1600s Scientific thinking

Hans Lipershey in Holland made first Telescope, leading to a less earth-centric view of the universe.

William Harvey said that blood was pumped by the heart.

Isaac Newton laid the foundations of classical mechanics, contributed to optics, and (in parallel with Leibniz) developed the infinitesimal calculus.


Before we jump ahead to systems thinkers

Understanding systems involves drawing three distinctions.

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

Actors are structures that exist in space and perform activities.

Activities are behaviors that happen over time and change the state of the system or something in its environment.


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

A natural system (e.g. the solar system) evolves without any intent.

Some refer to its outcomes (e.g. stable orbits) as its aims, but really they are unintended consequences.

By contrast, a designed system is created by intent, with aims in mind, and its outcomes may diverge from its aims.

Designed systems are often described in terms of aims (motivations), behaviors (activities) and structures (actors and objects).


There are descriptions and realisations - abstract and concrete systems.

To apply system theory is to form an abstract system description (a type) that defines a system’s roles and rules.

This hides the infinite complexity of actors you observe or envisage as acting to realise (instantiate) those roles and rules.


In this preface and other sections, much is owed to Wikipedia.

Many other system concepts are defined in other papers, starting with Introducing system ideas.

Early systems thinkers


Smith: subdivision within and competition between systems

Adam Smith (1723 to 1790) was a Scottish economist, philosopher and author.

He laid the foundations of classical free market economic theory.

In “The Wealth of Nations” and other works he advanced the idea that efficiency is improved by division of labour.

Consider for example the specialisation of a society into subsystems such as butchery, brewing and baking.

Smith also explained how rational self-interest and competition can increase economic prosperity.

“It is not from the benevolence of the butcher, the brewer, or the baker, that we expect our dinner, but from their regard to their own interest.”


Observation: Smith identified the practical usefulness of assigning different activities to different roles.

By practising the activities expected of their roles, actors become more efficient and effective.

Darwin: system mutation by reproduction with modification

Charles Darwin (1809 to 1882) was an English naturalist, geologist and biologist.

Beyond his famous book “On the origin of species”, he made other contributions to the science of evolution in biology.

His central idea is often misquoted and misinterpreted.

In this context, his theory is about how incremental mutations help systems to change and survive in a changing environment.

Whether by chance or design, some changes make one system more efficient than its rivals in competition for resources or mates.


Observation: Darwinian evolution can be separated from biology.

It is an inter-generational process that reproduces system version N with small modifications in system version N+1.

The better that the new system N+1 is fitted to its environment, the more likely it will be reproduced again.

Bernard: homeostatic feedback loops

Claude Bernard (1813 to 1878) was a French physiologist, considered the "father" of modern experimental physiology.

His promoted the concept of "homeostasis", the idea that life depends on the stability of a body’s internal environment.

"The living body, though it has need of the surrounding environment, is nevertheless relatively independent of it.”

“The tissues are withdrawn from direct external influences and are protected by a veritable internal environment.”

Bernard introduced the revolutionary idea that systems maintain homeostasis by feedback control loops.


Observations: Bernard’s idea led, a hundred years later, to cybernetics, the science of system control.

It also influenced much sociological thinking.

“The organic model and homeostasis are basic for many cybernetic explanations of societal functioning” (Bausch 2001).

Spencer: social systems as organic systems

In 1803, Henri de Saint-Simon had described the idea of describing society using laws similar to those of the physical and biological sciences.

The conceptual origins of the [social systems] approach are generally traced to the 19th century,

particularly in the work of English sociologist and philosopher Herbert Spencer and French social scientist Émile Durkheim.” https://www.britannica.com/topic/systems-theory


Herbert Spencer (1820 to 1903) was an English philosopher, biologist, anthropologist, sociologist, and liberal political theorist.

Herbert Spencer dealt with social systems in terms of organic evolution.


Observation: analogies can mislead or fall short of explaining.

“Organic analogies are inadequate as models of social systems.” (Bausch 2001)

However, it is far from clear that people agree what the word “system” means in the phrase “social system”.

And evolution by natural selection does explain the development of mutually-beneficial social behavior

Marx and Engels: dialectic materialism

Karl Marx (1818 to 1893) studied political economy and Hegelian philosophy

He was a philosopher, economist, political theorist, sociologist, journalist, and revolutionary socialist.

Friedrich Engels (1820 to 1895) was a German philosopher, social scientist, journalist, and businessman.

Together, they founded Marxist theory (sometimes linked to Darwinism) at the heart of which is an ideology called “dialectic materialism”.

Ironically, despite their good intentions, this pseudo-scientific theory led to monstrous dictatorships.

Lenin, Stalin and Mao Zedong infamously starved or otherwise killed tens of millions of their own citizens.


Observation: other systems thinking has been relatively harmless, but some is equally questionable on logical or scientific grounds.

Read Marx and Engels for challenges to the logic of dialectic materialism.

Pareto: social system as homeostatic

Vilfredo Pareto (1848 – 1923) was an Italian engineer, sociologist, economist, political scientist and philosopher.

He made several contributions to economics, particularly in the study of income distribution and in the analysis of individuals' choices.

He observed that c80% of the land in Italy was owned by 20% of the population, and c20% of the peapods in his garden contained 80% of the peas.

Today, the Pareto principle is usually interpreted as meaning 80% of the effects or costs come from 20% of the causes or requirements.


In “On the Equilibrium of the Social Systems" Pareto proposed social entities are homeostatic.

A homeostatic organism restores its state to a norm, if it depart from it.

Pareto’s social system is a social entity that maintains its roles and rules – it resists or counterbalances changes to them.

“Any moderate changes in the elements or the interrelationships... are counterbalanced by changes tending to restore it.”

“[Pareto’s idea was] taken over by many sociologists, especially Parsons” (Bausch 2001)


Observation: many if not most human social groups are not homeostatic in either state or form.

Rather than stay the same, they tend to evolve into different forms.

Pareto’s stable social system is now called a “social cell”, which is discussed in a later paper.

Durkheim: collective consciousness and culture

Emile Durkheim (1858-1917) was concerned with how societies could maintain their integrity and coherence in modernity.

He was eager to promote the acceptance of a society as a meaningfully discrete entity.

So eager that, in his Division of Labour in Society in 1893, he invented the idea of a collective consciousness.

“The totality of beliefs and sentiments common to the average members of a society forms a determinate system with a life of its own.

It can be termed the collective or common consciousness.”


Durkheim presumed people in a group will interact to form a “society” that is a “determinate system” with its own culture.

He believed in “cultural cohesion” and that a “collective consciousness” acts as a unifying force within society.


Observation: How to verify or falsify such a proposition?

A minute’s reflection throws up questions that bedevil discussion of societies as entities.

What is the scope of a society? What does it mean to join it or leave it?

How many societies can an individual be a member of?

Does each inter-personal action belong to all the overlapping societies the two people belong to?

How is an individual’s consciousness apportioned between the several societies they belong to?

What if an individual belongs to several societies with conflicting norms or aims?


How to account for rule breakers, non-conformists, and antagonisms within a group?

Durkheim argued that any apparent cultural diversity is overridden by a larger, common, and more generalized cultural system, and the law.

How to distinguish apparent cultural diversity from real cultural diversity?


Observation: again, biological evolution provides an explanation for cultural cohesion.

Obviously, every society and business depends on cooperation between individuals.

Cohesion results from actors playing compatible roles.

And compatible roles emerge from some combination of natural evolution, self-interest and design.

(Rather than from a collective consciousness.)

Tarde: social system as emergent from the actions of individual actors

Gabriel Tarde (1843 –1904) was a French sociologist, criminologist and social psychologist.

He was very critical of Durkheim’s methodology and theory (see above).

He viewed a society as micro-level psychological interactions among individuals, the fundamental forces being imitation and innovation.


Observation: typically, a large system depends on small interactions between its atomic elements.

In a social group, actors’ imitations of each other may help to create and maintain a system’s roles and rules.

By contrast, actors’ innovations can disrupt a system by changing its roles and rules.

Weber: a bureaucratic model

Max Weber (1864-1920) wrote widely on religion, politics, economics and bureaucracy.

Weber set out three essential principles for human organisations.

·         Roles: labour is divided between roles with identifiable tasks and duties (cf. Adam Smith)

·         Hierarchy: a chain of command with rules that describe a role’s capacity to coerce others.

·         Assignments: allocations of (qualified) actors to play roles.

His principles included the supremacy of rules in bureaucratic organisations.


Observation: it may help here distinguish two things by naming them differently.

A social system - in which actors realise roles and rules (after Weber and Henderson)

A social entity - in which actors choose behaviors to reach personal and/or shared goals (after Tarde and Parsons).


Using these terms, Weber’s roles and rules distinguish a social system from a social entity.

His hierarchical chain of command is one of several possible management structures.

His assignment of actors to roles implies that his system is independent of individual actors.

Henderson: meanings communicated in interactions between actors playing roles

Lawrence Joseph Henderson (1878 to 1942) applied the concepts of physiological regulation to social behavior.

He described social systems with reference to the sociology of Pareto, but also in contrast to him.

His interest was the meanings communicated in interactions between two or more persons acting in roles or role-sets.

He influenced other Harvard sociologists, especially Talcott Parsons and his "The Structure" of Social Action" (1937).


Click here for a discussion of meaning in communication.

Parsons: action theory

Talcott Parsons (1902-1979) was an American sociologist who developed a general theory for the study of society - called action theory.

Parsons presented a metaphysical (not scientific) context for systems theory and cybernetics.


“Parsons carefully differentiated social systems theory from personality theory and a theory of culture.

In his conception, these three theories combine in an overall theory of action,

in which we can understand how we actually do human (social) activity…  (Bausch 2001)


Bausch points to Feynman-like criticisms of Parson’s work

“According to Buckley, any critique of the Parsonian framework must contend with its loose conceptual structure” (1967, p23).

Alexendar finds... Parson’s thought is “internally contradictory”, and “provides support for a variety of partial, often mutually antagonistic interpretations” (Alexander, 1983, p309).” (Bausch 2001)


The pattern of a socio-cultural essay is to invent a classification, then discuss how the classes overlap or interact.

“Parsons describes his action theory as having “our generic types of subsystems,” which are the organism, the social system, the cultural system and the personality

Any cultural activity then results from a combination [of these] activities in these four areas mutually influence, or “interpenetrate” each other.” (Bausch 2001)


Observation: Is Parson’s separation of four subsystems (organism, social, personal and cultural) measurable, testable or logically provable?

Is the interpenetration these four subsystems detectable, measurable and testable?

What can we do with these ideas, other than discuss them?


“Parsons uses this idea of interpenetration to explain how... human beings can engage with each other in mutually advantageous behaviors”. (Bausch 2001)


Observation: What does “explain” mean here? Surely evolutionary biology well explains the mutually advantageous behaviors of animals in a group?


“The idea of concensus plays a major role in Parson’s conception of society, which he conceives as coalescing around accepted values and norms of behavior.

Parson’s deals with how a society maintains its norms and values in the face of deviant behavior and thereby maintains its equilibrium.” (Bausch 2001)


Observation: This harks back to Pareto’s idea of a homeostatic social system (see earlier observations on that).

In my life time, the norms and values of society have shifted continually, not always for the better.


Parsons saw motives as part of our actions.

He considered social science must consider ends, purposes and ideals when looking at actions.


Observation: How could one think otherwise?

The question is how this relates to the distinction between a social entity and a social system.

Parsons is not the only systems thinker whose work can be challenged on logical grounds alone.

Read Introducing system ideas for more on “Goal directedness”.


Most if not all thinkers above may be seen in retrospect as early systems thinkers.

But given their focus was on biology and/or human society, it is not clear they spent much time analysing what the word “system” means in general.

Classical system theory and cybernetics

Bertalanffy, Boulding and others established the Society for the Advancement of General Systems Theory in 1954.

To some, the term "system" means merely "an entity that contains things interrelated in some way or another".

However, general system theorists focus on activity systems in which structures/components interact in behaviors/processes.

Bertalanffy: general system theory

Ludwig von Bertalanffy (1901-1972) was a biologist who promoted the idea of a general system theory in the middle of the 20th century.

His aim was to discover patterns and elucidate principles common to systems in every line, at every level of nesting.

He looked for concepts and principles applicable broadly, rather than to one discipline or domain of knowledge.

In “General System theory: Foundations, Development, Applications” (1968), he wrote

“There exist models, principles, and laws that apply to generalized systems or their subclasses, irrespective of their particular kind, the nature of their component elements.” Bertalanffy


Bertalanffy wrote of concepts such as holism and emergent properties.

“General System Theory… is a general science of wholeness… systems [are] not understandable by investigation of their respective parts in isolation.” Bertalanffy

The properties of a whole system “emerge” from interactions between its parts (say, a bicycle and its rider).

For a designed system, emergent properties are specified in requirements to begin with and tested later.


Bertalanffy related system theory to communication of information between the parts of a system and across its boundary.

connected with system theory is… communication. The general notion in communication theory is that of information.” Bertalanffy


General system theory concepts include:

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 may change over time.

Early system theorists were especially interested in homeostatic systems, which maintain their state via feedback loops.


Observation: Some of Bertalanffy’s ideas are misinterpreted, some are questionable.

Read Introducing systems ideas and then Bertalanffy’s ideas for more.

Weiner: cybernetics

Norbert Wiener (1894-1964) founded cybernetics – about how regulators monitor and control behaviors using feedback loops.

In 1948, he published Cybernetics or Control and Communication in the Animal and the Machine.

The phrase “control and communication” highlights the importance of information flows.

The phrase “the animal and the machine” suggest the principles apply to both animate (inc. human) and inanimate (inc. computer) systems.


Cybernetics separates a control system from the “real machine” or target system it monitors and controls.

The target system is any entity whose behavior is to be monitored and directed.


Much as humans abstract system descriptions from realities, so do mechanical control systems.

A control system models a selection of variable facts (such as temperature) observable in a target system.

E.g. A bimetal strip thermostat models the temperature of its environment.

It switches a heating system on and off, according to the state of its model.


Control system

Model of target system variables


Target system

Target system behavior

Heating system on/off


Read Introducing cybernetics for more.

Ashby: cybernetics and more

“Despite being widely influential within cybernetics, systems theory… Ashby is not as well known as many of the notable scientists his work influenced.

W Ross Ashby was one of the original members of the Ratio Club, who met to discuss issues from 1949 to 1958.”


W. Ross Ashby (1903-1972) was a psychologist and systems theorist.

“Ashby popularised the usage of the term 'cybernetics' to refer to self-regulating systems

The book dealt primarily with homeostatic processes within living organisms, rather than in an engineering or electronic context.” Wikipedia 2017


For some, understanding classical system theory requires making a paradigm shift as radical as is needed to understand Charles Darwin’s evolution theory.

Many find it difficult to understand the implications of what Ashby said.


“At this point we must be clear about how a "system" is to be defined.

Our first impulse is to point at [a concrete entity] and to say "the system is that thing there".

This method, however, has a fundamental disadvantage: every [concrete entity has] no less than an infinity of variables and therefore of possible systems.

Any suggestion that we should study "all" the facts is unrealistic, and actually the attempt is never made.

What is necessary is that we should pick out and study the facts that are relevant to some main interest that is already given.” Ashby 1956


To apply Ashby’s system theory is to apply the scientific method

You observe or envisage a system and model it in an abstract system description - a theoretical system.

The state of the system is modelled as variables whose values can be measured (e.g. the positions of the planets).

The processes that maintain or advance the values of those variables (e.g. the orbits of the planets) are also modelled.

The model (a type) hides the infinite complexity of real-world actors and activities that act to realise (instantiate) the model

If and when the system runs in reality, the reality can be tested against what the model predicts.


Ashby focused his attention on control systems in particular.

His Law of Requisite Variety defines the minimum number of states necessary for a control system to control a target system with a given number of states.

In response, Conant (1970) produced his so-called "Good Regulator theorem" stating that "every Good Regulator of a System Must be a Model of that System".


Observation: The Good Regulator theorem was proved by biological evolution long before Conant articulated it.

Animal brains maintain mental models of things (food, friends, enemies etc.) they care about in their environment.

These mental models must be accurate enough to enable the animals to monitor and manipulate those things.

Read Ashby’s ideas for more on his ideas, some of which are misinterpreted.

Boulding: system theory in management science

Classical system theory concepts and principles can be seen in many sciences.

And sociologists have looked to apply them to human societies.

Kenneth Boulding (1910-1993) wrote in 1956 about applying them to “management science”.


Boulding described social systems at two levels, populations and individuals.

He said behaviors performed by an individual include

·         joining or leaving a population

·         processing information and communicating meaning to others

·         remembering and acting on mental images (internal state data)

·         transcribing mental images into historical records and

·         restoring system state to some kind of norm.


Boulding’s society of humans might be described thus.


Generic system description

Boulding’s social system

A collection of active structures

that interact in regular behaviors

that maintain system state and/or

consume/deliver inputs/outputs

from/to the wider environment.

A population of individuals that interact by

processing information in the light of

mental images they remember, and

exchange messages to communicate meanings

to others and related populations.


Remember Durkheim?


Observation (repeat): How to verify or falsify such a proposition?

A minute’s reflection throws up questions that bedevil discussion of populations as entities.

What is the scope of a population? What does it mean to join it or leave it?

How many populations can an individual be a member of?

Does each inter-personal action belong to all the overlapping populations the two people belong to?

How is an individual’s mental state apportioned between the several populations they belong to?

What if an individual belongs to several populations with conflicting norms or aims?


Boulding on individuals as deterministic

Like many early systems thinkers, Boulding was concerned with how systems maintain their state.

Boulding presumed that a human actor, in a given state, will respond to a stimulus by acting in a predictable way.

He said the difficulty with applying classical system theory is that an actor’s state data (their “mental images”) is unknowable.

And since you cannot know the state of an actor, you cannot predict an actor’s response to an event.


Observation: Boulding didn’t mention that distributing a population’s state data between individual actors creates difficulties.

It makes it hard to maintain the integrity of a population, and difficult to collect management information about it.

Just as distributing a software system’s state data between individual objects makes it hard to maintain the integrity of the system, and collect management information.


Boulding on social systems as complex

Boulding classified systems into nine kinds, on a scale rising from “simple” to “complex”.

He placed social systems at level eight, below “transcendental” systems at level nine.


Observation: This hierarchy implies two questionable presumptions:

·         a higher or wider system is more complex than its component subsystems

·         a human social system is an especially high level and complex kind of system.


The first difficulty is that there is no recognised way to measure the complexity of a thing.

 “In dealing with complexes of 'elements', three different kinds of distinction may be made: according to their number; their species; the relations of elements.” von Bertalanffy

Should we count the element and relationship types in the system description, or the instances of those types in the operational system?

And then, how to combine those numbers in a measure of complexity? There is no agreed answer to that question.


There is a second and more important difficulty.

Our system relates atomic structures (or actors) in larger structures, and atomic behaviors (or actions) in longer processes.

The complexity of our system lies in the complexity of those larger structures and longer processes.

We necessarily ignore the internal structure of the “atomic elements” in our system.

However, on analysing the real world, our system may turn out to be simpler than just one of its atomic elements.


E.g. a game of noughts and crosses (tic tac toe) is a social system with very simple rules.

A game of chess has somewhat more complex rules.

Far more complex than either is the biology of a human or computer actor playing the game.


Boulding on roles and actors

Human actors are not dedicated to one system (bar their own biology); they can act in many.

So like Weber before him, Boulding suggested the essential parts of a social system might be roles rather than actors.

He didn’t dwell on this, but role-centric and actor-centric views of a business are dramatically different.

And ever since Boulding, social systems thinking have tended to fudge the description/reality distinction.


Observation: Ashby told us it is meaningless to point to an entity and call it a system with no reference to a system description.

And as Chris Partridge proposed to me: a system theory requires a philosophy of description and reality, as in this triangle.


System theory

System descriptions

<form>                            <idealise>

Systems thinkers <observe and envisage> Real world entities


In a system description, the parts of an activity system are roles and rules.

In a real-world active system, it can be argued the parts are neither roles nor actors; they are performances of roles by actors.

Go to avancier.website for more on description, reality and philosophy.

Read Boulding’s ideas for more on his thoughts.

Forrester: System Dynamics

The dynamics of a system are how the state of the system changes over time.

The roles and rules of a system may result in that system’s state changing in one of two ways.

The state may be maintained homeostatically, or advanced progressively as in most information systems.


Forrester considered every system to be set of quantities that are related to each other.

Wherever a change to one quantity has an effect on another quantity, the relationship can be seen as an inter-stock flow.

A stock is a quantity of entities or units – the stock level is the total number of the entities or units at one time.

A flow between two stocks typically represents a batch of events that increase or decrease stock levels.

A causal loop connects stocks by flows that amplify or dampen changes in stock levels.

In a System Dynamics model, each stock is incremented and decremented (at discrete time unit intervals) by inter-stock flows.

Each flow has a rate, expressed as events-per-time-unit (e.g. total births per time unit).


System Dynamics

Model of stocks and flows

<create and run>                   <idealise>

System modellers     <observe and envisage>     Causal loops


Why build a System Dynamics model?

Not to model individual entities and events, but to model the trajectories of quantities over time.

The long term effect of events on a system’s stocks or populations is not predictable from knowing the system’s roles and rules.

It is sometimes possible to mimic or predict that effect by simulating the system, by running a model of it.

A software tool can advance a System Dynamics model step by step, where each discrete event is a time unit.

And thereby show what might happen to stocks or populations over long period of time.


Observations: some say complexity “emerges” at the macro level from simplicity at the micro level.

They say that individual actors, following simple behavioral rules, generate “complex behavior” at a macro level.

However, this is to confuse two of many ways to measure complexity.

There is complexity in the structure of system state - as in Ashby’s measure of “variety”.

There is complexity in behavior - as in Mcabe’s measure of procedural complexity.

There is complexity in the trajectory of system state change – which is an interest in System Dynamics.

Read System Dynamics for more.

Meadows: System Dynamics

In "Thinking in Systems – A Primer" Donella H. Meadows began by explained the principles of System Dynamics.

Seven of the eight declarations below are made explicitly in the book, and the fifth is implicit.


A system is a set of things interconnected so as to produce a pattern of behavior over time.

The behavior of a system cannot be known just by knowing its elements.

A system isn’t just a set of things - even interconnected things.

A system is an interconnected set of elements that is coherently organized so as to achieve something.

The achievement of a system is what repetition of its behaviors over time results in (implicit).

So, the way to deduce the system’s purpose is to watch how the system behaves.

And purposes are deduced from behavior, not from a declaration of goals.

Intervening (to change the roles or rules of how elements interact) is to change the system.


Meadows characterised a system by its behaviors and their outcomes.

System: A set of elements or parts that is coherently organized and interconnected in a pattern or structure that produces a characteristic set of behaviors, often classified as its function or purpose."


Observation: In System Dynamics there can be three systems:

A passive structure: a static system model, expressed in a causal loop diagram

An active software system: a dynamic model of stocks and flows, running in a computer.

A concrete reality that the model supposedly represents.


Meadows said the system actors, which perform behaviors, may come and go.

"The elements, the parts of systems we are most likely to notice, are often (not always) least important in defining... the system." Meadows

"A system generally goes on being itself... even with complete substitutions of its elements." Meadows


What defines Meadow’s system is the roles actors play, and the rules that govern their interaction or relationships.

"Change the rules from those of football to those of basketball, and you’ve got, as they say, a whole new ball game.” Meadows


Observation: Like Russell Ackoff, Meadows started from a place rooted in classical or general system theory.

Also like Ackoff, Meadows slipped almost unnoticed from there into speaking of a human society as a system per se.

And into socio-political discussion, as though whole societies and economies can be modelled in a closed System Dynamics model.

Economists and systems thinkers like to think and talk this way, and it may be helpful on occasion.

But it is to step away from testable science and towards “scientistic” speculation.

Read System Dynamics for more.

How does a classic system engineering method work?

System design typically runs from requirements analysis, through system description, building and testing to roll out.

This table generalises from design processes taught to mechanical engineers and business system architects.


A system engineering method

Study the context: goals, constraints, stakeholders, their concerns, problems and requirements

Outline optional solution visions, along with value propositions for stakeholders

Analyse trade offs between solution options with respect to the context

Describe a target system that compromises between conflicting goals and constraints

Plan the work to move from the current to the target system

Follow the plan to build and test the target system

Roll out the target system.


Even mechanical engineers have to trade off between different goals, some of which conflict.

The fact that stakeholders approve a target system description doesn’t mean they share the same perspective of, or purpose for, the system.

Each may have their own perspective and value proposition.


Read Introducing general system theory for notes on system theorists including Weiner and Ashby.

General system theory doesn’t start from or depend on sociology, or any analysis of human-specific behavior, language or motivations.

On the other hand, questions like the optimal balance between centralisation and distribution of control appear in many different kinds of system.

And from the 1950s to the 1970s, general system theory stimulated people to look afresh at social systems.

“Soft” systems thinking

It is common to speak of soft systems as though they are clearly distinguishable from hard systems.

Yet both Ashby (above) and Checkland (below) spoke of systems as input-to-output transformations.

And the term soft system is used in various ways.


Soft system meaning 1: a theoretical/abstract system, a perspective

The notion that systems are abstractions from real world behaviors has been shared and reinvented by many.

W Ross Ashby (supposedly a “hard” systems thinker) pointed out that infinite theoretical systems may be abstracted from a concrete entity.

Russell Ackoff wrote that different observers of the same concrete reality may see it as different systems.

Peter Checkland defined a system as a perspective of a reality, a world view or “Weltenshauung”.


Churchman initially wrote for actors in what was then commonly called the operations research department.

Operational researchers studied regular business operations and proposed how to optimise or otherwise change them.

The operations research department acted as a meta system to business systems.


Operational research

Abstract system descriptions

<create and use>                    <realised by>

Operational researchers <observe and envisage> Business operations


Surely, every system-of-interest is a selective perspective of a reality?


Soft system meaning 2: a system with questionable aims

“Churchman, Ackoff and Checkland consider hard systems methodology to be a special application of systems theory in situations where the objectives are not in question” Bausch (2001)

Surely, the aims of any non-trivial designed system are questionable?

Even mechanical engineers are taught to manage stakeholders and trade off between conflicting goals.


Soft system meaning 3: an empirical/concrete system in which human actors play roles

Surely, giving system actors a free hand to determine the actions they perform is to undermine the concept of a system?

This is to confuse the system of interest with the meta system that observes and changes that first system.

See second order cybernetics below.


Soft system meaning 4: a situation rather than a system

Some say Checkland’s methodology is “situation thinking” rather than “systems thinking”.

However, Checkland uses the term “transformation” where others might say system.

His concern is to model regular business systems (transformations), and change them from a baseline (problematic) state to a target (improved) state.

The table below suggest his concept of a system is much the same as in hard system thinking approaches.


Generic system description

Ashby’s design for a brain

Boulding’s social system

Checkland’s Soft System (Transformation)

An object-oriented software system

A collection of active structures

that interact in regular behaviors

that maintain system state and/or

consume/deliver inputs/outputs

from/to the wider environment.

A collection of brain cells that

interact in processes to

maintain body state variables by

receiving/sending information

from/to bodily sensors/motors.

A population of individuals that interact by

processing information in the light of

mental images they remember, and

exchange messages to communicate meanings

to others and related populations.

A coherent entity, a structure of components

that interact in mechanisms that

maintain the integrity of the entity and

consume/deliver inputs/outputs

from/to each other and external entities.

A population of objects that interact by

processing information in the light of

state data they remember, and

exchange messages to communicate

with other objects and system users.


All four specific cases fit the generic system description reasonably well.

In every case, the real world entity is regarded as a system only in so far as it realises a system description.


It may be clearer to think of soft systems thinking as subtype of hard systems thinking, which is specific to:

·         Open (rather than closed) systems, which have external customers and suppliers.

·         Designed and purposive (rather than naturally evolved) systems, which have owners and stakeholders.

·         Human (rather than naively mechanical) entities, which employ human actors.


Read Systems thinking approaches for more discussion of the hard/soft system distinction.


Checkland: soft systems methodology

Peter Checkland is best known for his Soft Systems Methodology (SSM).

The table below lists the seven steps of SSM against the steps in a typical system engineering methodology.

The steps do not correspond one for one, but the difference are mostly cosmetic, a matter of emphasis or of particular technique.


A “hard” system engineering methodology

Soft Systems Methodology

Study the context: goals, constraints, stakeholders, their concerns, problems and requirements

Enter the problem situation.

Outline optional solution visions, along with value propositions for stakeholders

Express the problem situation (a rich picture)

Analyse trade offs between solution options with respect to the context

Formulate root definitions of relevant systems (validated using the CATWOE analysis below)

Describe a target system that compromises between conflicting goals and constraints

Represent human activity systems as conceptual models (business activity models)

Plan the work to move from the current to the target system

Compare the models with the real world.

Follow the plan to build and test the target system

Define changes that are desirable and feasible

Roll out the target system.

Take action to improve the real world situation.


Read Systems thinking approaches for more on Checkland’s root definitions, CATWOE and conceptual model.

And more about hard/soft distinction.


Checkland proposed a generic design pattern for structuring a business in terms of these essential functions.

·         Defining targets (system aims)

·         Operations (system behaviors)

·         Monitoring and Controlling Operations (system regulation as in classical cybernetics).


Similar ideas can be found in other approaches.

Beer proposed a more complex design pattern, called the Viable System Model.

Beer: the Viable System Model

Stafford Beer (1926- 2002) was a theorist, consultant and professor at the Manchester Business School.

He regarded Ashby as a grandfather of cybernetics (I believe Ashby was a godfather to one of Beer’s children).

He respected Ashby, but was focused more on what might be called “management science”.

So, he set out to apply Ashby’s ideas to business systems.

Beer’s book title “Brain of the Firm” (1972) may well be a deliberate echo of Ashby’s “Design for a Brain” 20 years earlier.


Later, in “Diagnosing the system for organisations” (1985) Beer detailed his “Viable System Model”.

He said the VSM was inspired by the structure of the human central nervous system.


Observation: the VSM doesn’t resemble the known structure or workings of the human brain or nervous system.

It cannot be the VSM, since many viable systems have nothing like a central nervous system (e.g. the solar system, a tree, a bee hive, an oyster).

And there is scant evidence of any business operating in a way you could say closely matches the VSM.

The VSM is primarily a tool for diagnosing human organization design issues, and generating change proposals.

Read Ashby’s ideas and Beer’s ideas  for more on the VSM and requisite variety.

Ackoff: human organizations as purposeful systems

Russell L Ackoff (1919-2009) was an American organizational theorist, operations researcher, systems thinker and management scientist.

He blurred the distinction between social systems and social entities (and so, between classical and second order cybernetics).


System concepts

In “System of System Concepts” (1971), Ackoff’s first three concepts were:

1.      A system is a set of interrelated elements,

2.      In an abstract system, the elements are concepts.

3.      In a concrete system, the elements are objects.


“Different observers of the same [concrete] phenomena may conceptualise them into different [abstract] systems” Ackoff 1971

Thus, Ackoff endorsed Checkland’s point that observers of one concrete social entity may describe it as different abstract systems.

But later, Ackoff contradicted himself:

“A church, a corporation or a government agency is a system”. Ackoff 1971

In other words: one concrete social entity is a system, regardless of any observer, conceptualization or abstract system.

Which is to say: Ackoff confused social systems and social entities.


System classification

Systems thinkers like to classify systems: e.g. into organisms, animal societies and machines.

Ackoff wrote: “There are many different ways of classifying systems.” Ackoff 2003.

He rejected Boulding’s classification, and analogies between societies and organisms.

His own four-way classification evolved from 1971 to 2003, and had some curious features.

E.g. his “animate systems” excluded lower animals; his “social systems” excluded non-human social groups.


“Purposeful systems”

Ackoff’s focus was on human organizations of the kind discussed in “management science”.

What did he mean by “All organizations are social systems”?

He was not concerned with informal organizations that have little or no bureaucracy (e.g. a pick-pocket gang, or a choir).

By “organization” he meant a hierarchical bureaucracy that administratively organizes people and their work - in the public or private sector.

And like others in the 1970s, Ackoff considered government institutions to be on the point of collapse.


Presuming that any human organization or institution can be called a “system”, Ackoff classified them as “purposeful systems”.

Meaning that people in organizations (being self-aware) have their own purposes, which shape what they choose to do.

In 1972, Ackoff wrote a book with Emery about purposeful systems which focused on how systems thinking relates to human behavior.

He defined a human-created system as "purposeful" when its "members are also purposeful individuals”.

These individuals intentionally and collectively formulate objectives and are parts of larger purposeful systems.

He said a purposeful system or individual is also ideal-seeking if it chooses objectives that lead towards as wider and more strategic ideal.

"The capability of seeking ideals may well be a characteristic that distinguishes man from anything he can make, including computers".


Observation: again, there are two very different things here.

A social system - in which actors realise roles and rules.

A social entity - in which actors choose behaviors to reach personal and/or shared goals.

Like many social systems thinkers, Ackoff blurred the distinction.

However, his concern was not to design system roles and rules.

His agenda was to rather to bemoan the state of institutions, diagnose problems, and propose interventions to change them.

Read Ackoff’s ideas for more.

Second order cybernetics

The hard/soft system distinction is questionable, for reasons explained above.

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

If the distinction is worth drawing at all, it is not as strong as another distinction in systems thinking.

The primary schism in systems thinking is between first and second order cybernetics.
And the trouble with second order cybernetics is that it can eviscerate the concept of the system.


“Second-order cybernetics, is the recursive application of cybernetics to itself.

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

Von Foerster referred to it as the cybernetics of "observing systems" whereas first order cybernetics is that of "observed systems".

It is closely allied to radical constructivism, which was developed around the same time by Ernst von Glasersfeld.

Its concerns include epistemology, ethics, autonomy, self-consistency, self-referentiality, and self-organizing capabilities of complex systems.

It has been characterised as cybernetics where "circularity is taken seriously".” Wikipedia 24/02/2018


Observation: Second order cybernetics treats systems actors as system thinkers.

But the idea that actors continually modify the roles and rules of a system they work in undermines system theory.

How to maintain the integrity of the system concept?

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


1.      Distinguish a social entity from the many social systems it can realise.

2.      Separate meta systems (in which actors are system thinkers) from operational systems (in which actors are workers).

3.      Allow actors in a social entity to switch between roles in systems and meta systems.

4.      Allow actors to make incremental (generation-by-generation) rather than continual changes to system roles and rules.


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

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

Some remarks and conclusions

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

“The sociological tradition suggests two alternatives: either persons [think actors] or actions [as may be assigned to roles].”


Understanding systems involves drawing not one but 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.


To call every problem, situation or business “a system” is unhelpful.

Do you see a system as:

·         a set of actors who perform activities they choose? Or a set of activities performed by actors?

·         a natural and continually evolving entity? Or one that is designed and changed under change control?

·         a concrete entity in the world? Or only that part of the entity which conforms to a system description?


Enterprise architecture favours the latter answer to each of those three questions.


Social systems thinking

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

Perhaps different words might be used indicate different kinds of social group?

·         A social entity – a social group in which actors choose behaviors to reach personal and/or shared goals – in an ad hoc way.

·         A social system - a social group in which actors realise given roles and rules – which are changed only incrementally, under change control.

·         A social cell – a social group in which actors find the roles and rules of a particular social system so attractive they resist any change to them.


Enterprise architecture is primarily focused on the second.


An unforeseen application of classical system theory

It is said that the most complex natural system is the human brain.

Today, unforeseen by Bertalanffy, Weiner and Ashby, the most complex designed systems are software systems.


Generic system description

An object-oriented software system

A collection of active structures

that interact in regular behaviors

that maintain system state and/or

consume/deliver inputs/outputs

from/to the wider environment.

A population of objects that interact by

processing information in the light of

state data they remember, and

exchange messages to communicate

with other objects and system users.


Usually, a concrete activity system matches an abstract system description only well enough.

But a concrete software system matches its abstract system description perfectly.

At run time, it can only do what is described in its code – no more, no less.


The ubiquity of classical cybernetics

Cybernetics can be seen in business systems.

A business system is connected to its wider environment by feedback loops.

It receives message about the state of entities and activities in its environment.

It records that information in memory.

It outputs messages to inform and direct entities and activities.

The information in messages and memories must model reality well enough, else the system will fail.


Cybernetics can be seen also in software system.

A software system monitors and/or inform entities in its environment.

Information feedback loops connect the software system with entities in its environment.


Naturally, the regular behaviors of business have increasingly been automated, supported and enabled by software systems.

Half a century after we entered the “Information Age”, people talk of using software systems to make “digital transformations”.

In the future, this will increasingly involve artificial intelligence.


If you want to read this work in sequence, you might want to move on to Introducing system ideas.

What follow is not irrelevant, but is not immediately helpful to proceeding.

Footnote: Other thinkers and observations

Erwin Schrödinger: using energy to maintain order

A fundamental characteristic of systems is orderliness in their structural elements and behaviors.

Bertalanffy considered a biological entity as a thermodynamic system in which homeostasis maintains order and keeps entropy at bay.

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

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

It turns out that the systems we describe can grow, shrink, die and lead to chaotic outcomes.


Erwin Schrödinger (1887 –1961) also discussed the principle by which an organism maintains itself in an orderly state.

His core ideas may be distilled as:

 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.” Wikipedia 2017


Note that some modern scientists call negative entropy "information" rather than “order”.

However, in most systems thinking "information" has a different meaning: it is a meaning created or found by an actor in an encoded description of a reality.


“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.


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.

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

And in the evolution of the nation state, the highest energy consumption per head is not found in countries that are especially orderly.

Energy consumption is highest in countries that are rich and either:

·         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.


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


Observation: Thermodynamics is tangential to most social and business systems thinking.

Wittgenstein: the logic of natural language

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

He considered many philosophical propositions to be poorly formulated, and that debates arose from misunderstandings.

He favoured dissolving philosophical disagreements and confusions by analysing the use and abuse of language, rather than producing new theories.

“Our investigation is a grammatical one. Such an investigation sheds light on our problem by clearing misunderstandings away..” Philosophical Investigations Sect. 90


Observation: a relatively formal language is needed to describe system structures and behaviors.

However, a study of natural language is a poor starting point.

Click here for an introduction to theories of description, information and communication that support system theory.

Rudolf Carnap: the logic of science and data processing

Rudolf Carnap (1891 – 1970) was a member the Vienna circle.

He said many philosophical questions (expressed in natural language) were meaningless, and favored the logic of science.

His “Principle of Tolerance” is that the concern (in choosing a linguistic framework) is not truth, but the pragmatic considerations of simplicity and usefulness.

We are free to build up our own language or logic as we wish.

Carnap designed the logical syntax used to exactly formulate the results of logical analysis.

It is said that his work foreshadowed the logic used designing software.


Observation: we do need a formal language (vocabulary and grammar) to describe system structures and behaviors.

The grammars used in programming and data definition languages are useful in business system definition.

Hoare: declarative specification of system state change

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

His contributions to system theory include a logic for verifying process correctness, and a formal language to specify the interactions of concurrent processes.


Hoare logic describes how performing a process changes the state of a system.

It is based on the Hoare triple, which may be expressed as: {Precondition} Process {Post condition}.

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


For many years, Hoare’s Oxford University department worked on formal specification languages such as CSP and Z.

These did not achieve the expected take-up by industry, and in 1995 Hoare was led to reflect upon the original assumptions.

“Programs have now got very large and very critical – well beyond the scale which can be comfortably tackled by formal methods....

There have been many problems and failures, but these have nearly always been attributable to inadequate analysis of requirements or inadequate management control.

It has turned out that the world just does not suffer significantly from the kind of problem that our research was originally intended to solve.”


Observation: In formal methods, the pre and post conditions are assertions or formulae in predicate logic.

But Hoare logic also underpins all informal methods for analysis of requirements and ways to declare what a business process does.

It can be seen in the definition of “value streams”, “business scenarios”, “service contracts” and other tools used in business architecture definition.

Defining the post condition of a business process is to define a requirement; a result, goal or outcome of value to the business (be it on a micro or macro scale).

Luhmann: autopoietic social systems

Niklas Luhmann (1927–1998) was a German sociologist and student of Parsons.

Like writers a century earlier, he presumed a system is homeostatic and sustains itself, though in a very curious way.


In analysing Luhmann’s ideas, David Seidl (2001) said the question facing a social system theorist is what to treat as the basic elements of a social system.

“The sociological tradition suggests two alternatives: either persons [think actors] or actions [think roles].”

Luhmann chose actions. He occupied a position in an extreme wing of activity-centric systems thinking

He proposed the basic elements of a social system are communication acts about a code that lead to decisions that sustain that code-centric system.

Luhmann’s system of transient communication events is radically different from systems as understood by most other system theorists.


Observation: Luhmann’s view of systems is well-nigh diametrically opposed to that of classical system theory.

The system has no persistent structure, no persistent state, and no memory of communication events.

However, his idea of a social system based on a code (like “justice”, or “sheep shearing”) does have a counterpart in normal system theory

That is the notion of “domain-specific language” for communication of information between actors playing roles in one system.

Read Luhmann’s ideas for more.

Habernas: universal pragmatics

Jürgen Habermas (born 1929) was a critic of Luhmann’s theory of social systems

He developed the social theory of communicative reason or communicative rationality.

According to Wikipedia, this distinguishes itself from the rationalist tradition, by locating rationality in structures of interpersonal linguistic communication rather than in the structure of the cosmos.

It rests on the argument called universal pragmatics – that all speech acts have an inherent "purpose" – the goal of mutual understanding.

He presumed human beings possess the communicative competence to bring about such understanding.

And hoped that coming to terms with how people understand or misunderstand one another could lead to a reduction of social conflict.


Observation: a theory of why and how animate entities communicate has to start from the evolutionary advantage it gives them.

Natural human language is inherently fluid and fuzzy; it is a tool for social bonding and communication, but can easily lead to misunderstandings.

Click here for an introduction to theories of description, information and communication that support system theory.

Simon: decision making

Herbert Alexander Simon (1916 to 2001) was a political scientist, economist, sociologist, psychologist, and computer scientist.

According to Wikipedia, he argued that fully rational decision making is rare: human decisions are based on a complex admixture of facts and values.

And decisions made by people as members of organizations are distinct from their personal decisions.

He proposed understanding organizational behavior in humans depends on understanding the concepts of Authority, Loyalties and Identification.


Observation: a theory of why and how humans conform to group norms has to start from the evolutionary advantage it gives them.

Authority, Loyalties and Identification are matters for management science rather than a general system theory.

Further reading

If you want to read this work in sequence, move on to Introducing system ideas.


Sources used in some way in writing this

1906 Space-time continuum (Hermann Minkowski)

1948 Cybernetics or Control and Communication in the Animal and the Machine” (Norbert Weiner)

1952 “Design for a Brain” (W Ross Ashby)

1956 “General Systems Theory” (von Bertalanffy)

1956 “Introduction to cybernetics” (W Ross Ashby)

1956 “General System Theory – The Skeleton of Science” Kenneth Boulding

1971-72 System Dynamics (Forrester; Meadows et al.)

1971 “System of System Concepts” (Russell Ackoff).

1972 Second order cybernetics (Bateson)

1972 Soft systems methodology (Peter Checkland)

1979 “Ecological System theory” (Bronfenbremner)

1997 “A Realist Theory Of Science” London: Verso. (Bhaskar, R.)

2001 “Luhman’s autopoietic social system” David Seidl

????  Charles Handy

2011 “The positive and the negative” Justin Cruickshank

2014 “Types and tokens” Stanford Encyclopedia of Philosophy

2018 “Splitting Chairs” in Philosophy Now, Jan 2018


Ten more sources

1962 Systems engineering (Hall)
1963 Socio-technical systems (Trist et al.)
1965 RAND-systems analysis (Optner)
1981 Strategic assumption surface testing (Mason and Mitroff)
1988 Cognitive mapping for strategic options development and analysis (Eden)
1983 Critical system heuristics (Ulrich)
1990 System of systems methodologies (Jackson)
1990 Liberating systems theory (Flood)
1991 Interpretive systemology (Fuenmayor)
1991 Total systems intervention (Flood and Jackson).


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