Some system thinkers and their ideas

Copyright 2017 Graham Berrisford. One of several hundred papers at Last updated 06/01/2019 12:21


System Theory Tutorial in London Saturday March 2nd 2019


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

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.


Preface. 1

Classical cybernetics - the science of system control 3

General system theory – the cross-science notion of a system.. 5

System Dynamics – animating a system model to predict long-term outcomes. 6

Soft systems thinking – loosening the system concept 7

Decision theory (or theory of choice) 7

Two more sociological systems thinkers. 9

Second-order cybernetics – undermining the system concept 10

Conclusions. 11



Read the science and philosophy of systems thinking for a brief history of the universe.

The discussion there is of how humans came to conceptualise the world in terms of systems.


Willard Gibbs (1839 – 1903) was a scientist, instrumental in the development of chemistry into a science

He defined a system as “a portion of the ... universe which we choose to separate in thought from the rest of the universe."

But if every entity we think of is a system, one for one, then the concept of a system has no value.

Let us use the term “entity” for any portion of the universe we can locate in space and time

And look at how the term “system” has been given more interesting and useful meanings. 


Read thinkers who foreshadowed system theory for ideas attributed to the thinkers below.

·         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.

·         Lawrence Joseph Henderson (1878 to 1942) meaning in communication

·         Talcott Parsons (1902-1979) action theory.


These thinkers may not have spent much time analysing what the word “system” means.

The term may have been used some to mean only "a group of interrelated things".

And not all of their ideas stand the test of time; however, they did influence 20th century systems thinkers.


When “system theory” became established as a topic in its own right is debatable.

Some suggest system theory is a branch of sociology.

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

Others 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.


It is certainly true that the general concept of a system became a focus of attention after second world war.

And there was a burst of systems thinking in the period 1945 to 1980.

Before we review ideas that emerged in that period, here three distinctions to be born in mind.


Actors and activities

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.

We typify the real world actors and activities in a system by defining abstract roles and rules.

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

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

Some see a set of actors who perform activities; others see a set of activities performed by actors.


Natural and designed (accidental and purposive) systems

A designed system is often described in terms of aims (motivations), activities (behaviors), actors and objects (structures).

It is created by intent, with aims in mind - though its outcomes may diverge from its aims.

By contrast, 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.


Cooperation and conflict between actors

When actors interact in a system, they don’t necessarily help each other.

They may cooperate, as within a football team or a business system.

They may compete, as in a game of chess or a market, or hurt each other, as in a boxing match or a war.

Cooperation, conflict and conflict resolution is a focus of bio-mathematics and game theory.

Classical cybernetics - the science of system control

Cybernetics is the science of systems in which biological or mechanical actors process information.

It was established after the second world war - then soon embraced within a broader system theory movement.


The general idea is of an information feedback loop.

·         a control system receives messages that describe the state of a target system.

·         the control system responds by sending messages to direct activities in the target system.

The information, encoded in messages and memories, describes and/or directs something in the world.


The general idea is found in both organic and mechanical systems

·         A missile guidance system senses spatial information and sends messages to direct the missile.

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

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

·         A software system holds a model of entities and events that it monitors and directs in its environment.


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 Alan Turing and Ross Ashby.

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

Here, the brain’s ability to typify and predict things is more interesting than its workings.)


Abstraction of description from reality

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

People point at a machine or a business in the real world (like IBM) and say "the system is that thing there".

To Ashby, the question is not so much "what is this thing?" as ''what does it do?"

Classical cybernetics is about systems that exhibit regular or repeatable behaviors.

The concrete thing that is IBM can be (can manifest, instantiate, realise) countless different systems.


“A system is any set of variables which he [the observer] selects”.  Ashby

A concrete system contains actors and their actions on selected objects/variables.

An abstract system contains roles and rules that actors and their activities adhere to in acting on those objects/variables.

E.g. The abstract roles and rules of the stickleback mating ritual are realised by countless pairs of sticklebacks.

An abstract system hides or ignores the infinite complexity of any real world actors and activities that realise the system.

E.g. In describing and testing the mating ritual, no attention is paid to the complexity of a stickleback’s internal biochemistry.


These system theory concepts are ubiquitous in modern systems analysis and design methods.

This table presents them in a way that can be used in discussion of social entities.


Abstract social system

A set of roles and rules (the logic or laws actors follow)

Concrete social system

Actors playing the roles and acting according to the rules


There is a many-to-many relationship between abstract systems and concrete entities.

One abstract system may be realised many times. E.g. the roles and rules of tennis may be realised in many concrete tennis matches.

Conversely, a concrete entity may realise any number of abstract systems. E.g. a pair of people may realise many tennis matches and many games of chess.


A social group in the real world is distinct from any abstract social system it realises.

A social system description hides the infinite complexity of the actors and activities in a social group.

And to be scientific, we must describe a system in a way that enables us to test whether a social group instantiates it or not.


In short, classical cybernetics describes a system by

·         typifying actors in terms of roles they play

·         typifying activities in terms rules that actors follow.

·         typifying the values of acted-on things in terms of variable qualities or values.



Roles, Rules & Variables

<create and use>                   <idealise>

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


Differentiation of system state change from system mutation

Ashby insisted we should on no account confuse two kinds of change.




For example

System state change

the value of at least one state variable

homeostatic regulation of values to stay within a desired range

System mutation

the type of at least one variable or behavior.

re-organization: changing the variables or the rules that update their values.


Read Introduction to Cybernetics for more on the cybernetic ideas discussed by these three well-known thinkers.

·         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.

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

Bear in mind, system theorists distinguish abstract system descriptions from concrete entities that instantiate (realise) them.

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>                    <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 that meeting conceived a society for the development of General System Theory.


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

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

He looked for concepts and principles applicable to several disciplines or domains of knowledge rather than to one.

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


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

“Another development which is closely connected with system theory is that of… communication.

The general notion in communication theory is that of information.

A second central concept of the theory of communication and control is that of feedback.”

“Every living organism is essentially an open system. It maintains itself in a continuous inflow and outflow…”


So, 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.


Read Introducing general system theory for more on the ideas of three well-known thinkers who met at that meeting in1954.

·         Ludwig von Bertalanffy (1901-1972) the cross-science notion of a system

·         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 an activity system is dynamic - it changes over time.

In mathematics, long-term changes to system state are addressed by dynamical systems theory and chaos theory.

The interest is not to predict the next state of the system in response to a single event.

It is rather to answer long-term questions like:

·         "Will the system settle down to a steady state?

·         “What steady states are possible?"

·         “Will the system crash or halt?

·         "How does the long-term behavior of the system depend on its initial condition?"


System dynamics is an approach that reveals the trajectory of quantitative state changes over time.

What makes it different from other approaches is the modelling of

·         feedback loops in flows that

·         increase and decrease the quantities of stocks.


The stocks are populations or resource quantities of any kind – materials, energy, organisms, happiness, whatever.

System dynamics reveals how feedback loops and time delays affect the stock levels over the long term.


System dynamics considers interactions between whole stocks (rather than between items within each stock).

Animating the model reveals the trajectory of changes over time to the number of items in each stock.


System Dynamics

Stock and flow models

<create and use>                 <idealise>

System modellers <observe & envisage>  Interdependent quantities


System dynamics may reveal a system behaves in a way that

·         is non-linear - its stocks increase and/or decrease in irregular or chaotic way.

·         settles in a steady state – its variable value(s) settle on fixed value(s).

·         has periodic points – it states repeat over time.


Read System Dynamics for more on the topic and on the ideas of two well-known system dynamics gurus.

·         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.


Meadows, in introducing system dynamics, made the same two points as Ashby, above.

Today, akin to system dynamics, there are agent-based approaches to the analysis of systems.

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?

Read Soft Systems for more on ideas of these three well-known soft system thinkers:

·         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.


The main problem with soft systems is the confusion of social entity, group or network with social system.

This confusion is explained and resolved in later papers.

Decision theory (or theory of choice)

One of Ackoff’s contributions to soft systems thinking might be distilled as follows.


The actors in a system that is described as per classical cybernetics and system dynamics act according roles and rules.

They actions they perform are constrained by their roles; the choices they make between actions are constrained by the rules of the system.

(In Ashby’s view, that is what it means to be actors in a system.)


By contrast, the actors in a social system (which might better be called a society or social network) have free will.

They can choose not only between actions offered to them, but also choose what actions are possible.


This prompts the question as to how people do, or should, make choices.

A biologist or psychologist may look to instinct, homeostasis, emotions or Maslow’s hierarchy of needs as the basis for making decisions.

A sociologist or mathematician may take a different perspective.

A sociological perspective

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.



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 biology and management science rather than a general system theory.

A mathematical perspective

Game theory (after Anatol Rapoport) is concerned with choices made by agents in interactions with other agents - as in a game of poker.

Decision theory is concerned with the choices made by individual agents between options, regardless of such a structured interaction.


“Decision theory (or the theory of choice) is the study of the reasoning underlying an agent's choices.

Decision theory can be broken into two branches:

·         normative decision theory, which gives advice on how to make the best decisions, given a set of uncertain beliefs and a set of values; and

·         descriptive decision theory, which analyzes how existing, possibly irrational agents actually make decisions.

Empirical applications of this theory are usually done with the help of statistical and econometric methods, especially via the so-called choice models, such as probit and logit models.


Advocates for the use of probability theory point to the work of Richard Threlkeld Cox, Bruno de Finetti, and complete class theorems, which relate rules to Bayesian procedures

Others maintain that probability is only one of many possible approaches to making choices, such as fuzzy logic, possibility theory, quantum cognition, Dempster–Shafer theory and info-gap decision theory.

And point to examples where alternative approaches have been implemented with apparent success.” Wikipedia 31.12/2018.



Decision theory is beyond the scope of this work on system theory.


For some practical case studies, look here

They feature (for example) the use of a Bayesian approach with Markov Chain Monte Carlo numerical methods.

The applications include assessing the risks of mechanical system failures, in order to inform decisions about their use and maintenance.


Be cautious about claims made about mathematics-based decision making.

First, the comparison to be drawn is not between making a decision that way and doing nothing.

It is between making a decision that way and making a more intuitive or experience-based decision.

Second, mathematics-based decision making cannot be extended to that large set of business decisions where

·         the time or cost of the analysis is too high

·         one can make only wild guesses about the raw data/numbers

That last is the category of decision that top level managers are often faced with, and paid to make.


The Wikipedia entry on Decision Theory will give you other links to follow.

Two more sociological systems thinkers

Social systems thinking continued alongside the post-war system theory movement, sometimes in touch with it, sometimes far apart from it.

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.

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 or actions.”

Luhmann chose neither, he proposed the basic elements of a social system are communicative events about a code that lead to decisions that sustain the system.

Each social system is centred on one code, which is a concept such as “justice” or “sheep shearing”.

He endorsed the “hermeneutic principle” that the hearer alone determines the meaning of a communicative event.

Read Luhmann’s ideas for more.



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

It is well-nigh diametrically opposed to that of classical cybernetics.

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

And the hermeneutic principle is contrary to common sense and to biology.

Since communication requires a receiver to decode the same meaning from a message that a sender intentionally encoded in that message.

Luhmann’s whole scheme (like that of Parsons before him) seems more metaphysical than scientific.

However, the idea of system based on a code might been seen as having a counterpart in more general system theory

That is a “domain-specific language” for communication of information about entities and events related to one body of knowledge.

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.



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.


Read Systems thinking approaches for more on sociological systems thinking.

Second-order cybernetics – undermining the system concept

Returning from sociology to the harder science of systems that do have a persistent state.

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 should on no account confuse two kinds of change.




For example

System state change

the value of at least one state variable

homeostatic regulation of values to stay within a desired range

System mutation

the type of at least one variable or behavior.

re-organization changing the variables or the rules that update their values.


Classical cybernetics is about maintaining the values of defined state variables.

Ashby used the term adaptation in the context of homeostatic state change.

Systems thinkers often use the term adaptive in the second – mutation – sense

And moreover, they mean the system is self-organising.


Second-order cybernetics was developed around 1970 by Margaret Mead, Heinz von Foerster and others.

It is about self-organising systems; it is the recursive application of cybernetics to itself.

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

It allows actors in a system to study the system and change it.

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


What if actors may change a system continually, rather than incrementally, generation by generation?

Read the following papers for how second-order cybernetics and “complexity science” undermined the concept of a system.


There is probably little dispute about these basic ideas about systems.

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

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

·         There are descriptions and realisations - abstract and concrete - systems.


However, much systems thinking discussion has conflated two or more of the following four ideas.

·         People doing their own thing.

·         People communicating with other people.

·         People playing roles in systems.

·         People playing roles in meta systems that administer and manage those systems.


The following papers unscramble ways that these ideas have been confused.

Read Systems thinking approaches for more.



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