Introducing System Theory and Cybernetics

Copyright 2016 Graham Berrisford. One of about 300 papers at Last updated 06/10/2017 12:51


This paper explains general system theory terms and concepts in the context of early sources.

All terms defined thus (Term: definition) are copied from this glossary.


Preface. 1

Other common ways to look at systems. 3

System description. 4

Abstract and concrete systems. 5

System boundary and information. 6

System structure and behavior 7

System change. 10

Natural systems and designed systems. 10

Social systems, social entities and social cells. 11

Cybernetics. 12

Discrete v continuous system dynamics (Forrester) 14

The idealism triangle. 16

More on von Bertalanffy. 17



In the 1950s, von Bertalanffy, Ashby and others looked for patterns and principles common to systems across all sciences.

General system theory (GST) is supposed to be applicable to systems in every field of research, at every level of nesting.

Bear in mind that systems stretch from the sciences to the humanities (maths > physics > chemistry > biology > psychology > sociology > politics).


Ludwig von Bertalanffy (1901-1972) was a biologist.

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.”

A later section questions some of Bertalanffy’s views of life, evolution, and the human condition.


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

“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.” Wikpedia in 2017

The remainder of this section is a minimal summary of what grew into a separate paper called Ashby’s ideas.

It introduces two ideas that are widely accepted and applied in business systems analysis and design.


To begin

Systems can be found everywhere, interrelated, nested and overlapping. 

There are natural systems and designed systems.

A system is bounded; it is separated from the environment outside the system.

But nothing in the universe is bounded until it is named and described.

So, nothing is a system until there is a description of it.



“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 in Introduction to Cybernetics (1956)


Ashby was keen we separate logical system descriptions from physical entities that realise them.

This triangle represents the idea more graphically.

System theory

System descriptions

<form>                        <idealise>

Systems thinkers <observe and envisage> Real world entities


A system is a view of a reality; or a role you can observe or envisage real world actors as playing.

To apply system theory is to form an abstract system description that hides the infinite complexity of real-world entities you observe or envisage.

You describe the state of a real-world system in terms of variables whose values can be measured (e.g. the positions of the planets).

You model whatever regular processes can change the values of those variables (e.g. the orbits of the planets).

You describe a process in a way that enables real world behaviors to be tested as matching your description of the process..


Cybernetics (control systems and target systems)

“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


Information flows are central to cybernetics.

E.g. Ashby saw the brain as a regulator that maintains a body’s state variables in the ranges suited to life

He presented the brain-body relationship as an information feedback loop.

A brain holds (or has access to) an abstract model of the body’s current state.

The brain receives information from sensors, and sends instructions to motors and organs.

The aim is homeostasis – to maintain the state of the body - and so help it achieve other desired outcomes.

Other common ways to look at systems

Obviously, a GST must be about properties shared by systems in different domains

A system is generally described in terms of aims (motivations), behaviors (processes) and structures (actors or components).





Win the world cup

Target outcomes, which give an actor a reason or logic to select and peform behaviors.


Compete in world cup matches

Processes, which run over time towards a final aim.

Active structures

Players in a national football team

Nodes (related in a hierarchical or network structure) that perform activities in behaviors.

Passive structures

Pitches, footballs

Objects and variables acted upon during behaviors.


System concepts include: system-environment boundary, input, output, process, state, hierarchy, goal-directedness, and information.” Principia Cybernetica

The table below maps different people’s ideas about systems to one generic structure.

Generic structure

Ashby’s design for a brain

Boulding’s social system

Checkland’s Soft System

An object-oriented software system

Active structure

A collection of brain cells that

A population of individuals that interact by

A coherent entity, a structure of components

A population of objects that interact by


interact in processes to

processing information in the light of

that interact in mechanisms that

processing information in the light of


maintain body state variables by

mental images they remember, and

maintain the integrity of the entity and

state data they remember, and

I/O Boundary

sending/receiving information

exchange messages to communicate meanings

send/receive inputs and outputs

exchange messages to communicate


to/from bodily sensors and motors

to each other and related populations

to/from each other and external entities

with other objects and system users


The principles of GST are applied all over the world, every day, to the description and design of successful systems.

They can be applied to things as diverse as tennis matches, brains, businesses and software.

System description

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

“Rather than reducing an entity to the properties of its parts or elements (e.g. organs or cells) systems theory focuses on the arrangement of and relations between the parts which connect them into a whole.” Principia Cybernetica


Reductionist view: identifying the parts of a whole, naming or describing parts without considering how the parts are related in the whole.

E.g. listing the organs and limbs of the body without relating them. Or analysing and describing the heart without reference to the lungs.


Holistic view: a description of how parts relate, interact or cooperate in a whole.

E.g. a description of how the muscles of the human heart interact.


Note: these definitions reflect what the biologist von Bertalanffy meant when he initiated the idea of a general system theory.

He deprecated reductionism (studying organs in isolation) and promoted holism (studying how organs cooperate to the benefit of the body).

However, the scope of the "whole" is a matter of choice; and so too is the granularity of a “part”.

And in practice, people flip between holistic and reductionist views of things.

So, the definitions must allow that systems can be nested and systems thinking can be recursive.


Organicism: the idea that systems are describable at multiple hierarchical levels (as von Bertalanffy named it).

E.g. Consider the decomposition of a human body through organ, cell, organelle and molecule to atom.

Or the decomposition of software application through component, class and operation to executable instruction.


Note: when you study a subsystem in isolation, it is the whole system of interest to you.

When you study how two systems are related, they become parts of a wider whole.

It is axiomatic that:

·         Systems can be hierarchically nested: one system can be a subsystem of another (and can overlap with another).

·         An event that is external to a smaller system is internal to a larger system

·         The emergent properties of a small system are ordinary properties of any larger system it is a part of.


Emergent property: a behaviour or structure of a whole that depends on interactions between its parts.

E.g. the forward motion of a cyclist on a bicycle, or the V shape of a flight of geese.

Typically, the property is not found in any one part, or predictable by studying a part in isolation from others.

The exception is systems with homogenous populations, where a subset of the whole may have the same properties as the whole.

(Note that emergent properties are the very purpose of system design.)


For more, read Holism and emergent properties.


Atomic element: an element that is not further divided in a description.

E.g. An organ is an atomic actor in a description of the human body.

A human is an atomic actor in a description of a society.

A note is an atomic activity in a musical score.

“Enter start and end stations” may be an atomic activity in a description of booking a train seat.


Note that atomic system actors (living or non-living) may be complex entities in their own right, and may play roles in other systems.

This system

has atomic actors

that perform behaviors

The solar system

sun and planets


An email system


executable instructions in source code

A human activity system


steps in work procedures

A beehive


deliver pollen, perform and observe wiggle dances

A predator-prey system

wolves and sheep

eat sheep, eat grass

The global ecology

animals and plants

transform oxygen into carbon dioxide, and vice-versa


Abstract and concrete systems

System: an overloaded term; a catch-all term used to label passive structures and activity systems

Also abstract (theoretical) system descriptions and concrete (empirical) systems.

Commonly used to mean a concrete activity system.


Abstract system description: a description or model of a concrete system.

E.g. a physical model, a narrative, a context diagram, a network diagram, a causal loop diagram, or a combination of such artifacts.

Abstract descriptions do take concrete forms (they are found in mental and documented models).

At the same time, they describe (model, conceptualise, idealise) a physical reality that can be envisaged or actually observed as matching the decription.

Abstract system description

“Solar system”

Laws of tennis

The score of a symphony

The roles in a radio play


Concrete system (aka System): a system that runs in reality.

A realization in physical matter and/or energy of an abstract system description.

A real world entity that can be tested as meeting an abstract system description – well enough.

(Note that one real world entity can realise many different abstract systems.)


Ashby said a real-world entity is not a system per se; it is only a system in so far as it performs the behaviors in an abstract system description.

There are two forms of system: a concrete system realises (or instantiates) an abstract system description (or type).

Abstract system description

Theoretical system

System description

Concrete system realisation

An empirical system

A system in operation


This table illustrates how a concrete entity realises (or instantiates) an abstract system description (or type).

Abstract system description

“Solar system”

Laws of tennis

The score of a symphony

The roles in a radio play

Concrete system realisation

Planets in orbits

A tennis match

A performance of that symphony

Actors playing those roles


Every real tennis match is unique in the immensely rich and complex detail of individual player’s actions.

Every tennis match is the same in so far as it conforms to the abstract description of a tennis match’s qualities in the laws of tennis.

Suppose a tennis player scratches his nose; that is an act in reality but not in the tennis match system.

The action is irrelevant to the target system controlled by the umpire.

The player’s breathing and biochemistry (though essential in reality) are also outside the described system.

System boundary and information

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

This means separating a system from is environment, and defining the system by its inputs and outputs.

Most systems analysis and design methods regard this as an essential step in system description.


System environment: the world outside the system of interest.

The environment of one system may be a wider system.

The environment of a business system is sometimes called its market.


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

It encapsulates the system’s internal structures and behaviors.


connected with system theory is… communication.” Bertalanffy


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

An interface defines the system as it is seen by external observers.

An interface may be defined in a contract defining services provided or required.

In social and software systems, the primary inputs and outputs are information flows.

For more read System boundary - encapsulation of structure and behavior.


“The general notion in communication theory is that of information.” Bertalanffy


Information: a meaning created or found by an actor in any physical form that acts as a signal.

Any description or direction that has been encoded in a signal or decoded from it by an actor.


Signal: any structure of matter or energy flow in which an actor creates or finds information.

In human communications, the physical forms include brain waves and sound waves.

In digital information systems, the physical form is a data structure in a binary code.


Information flow (aka message): physically, a signal passed from sender to receiver, logically, a communication.

Information state (aka memory): see “state”.

Information quality: an attribute of information flow or state, such as speed, throughput, availability, security, monetary value.


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


Information feedback loop: the circular fashion in which system inputs influence future outputs and vice-versa.

Brains and businesses can both be seen as information systems.

Both maintain a monitor-direct feedback loop with their environment:

·         They detect events and changes in their environment - via input information flows.

·         They remember the entities and events they monitor - in an internal model or information state.

·         They send messages to direct those entities and events.

If the system does not monitor and direct entities and events in its environment – well enough - then it expires or is changed.

For more, read Information feedback loops.

System structure and behavior

The structure/behaviour dichotomy:.structures exist in space; behaviors happen over time.

The table below has a natural language section, followed by a section adapted from the Unified Modelling Language (UML) standard.

Structure: passive structures and actors in space

Behavior: atomic actions and processes over time

Structures that are shaped or directed by behaviors.

Entities that are created, changed and destroyed by Events.

Stocks that are incremented and decremented by Flows.

Components that perform Processes and deliver Services.

Behaviors that are performed by active structures.

Events that create, change and destroy Entities.

Flows that increment and decrement Stocks.

Processes performed and Services delivered by Components.

Structure - along the lines defined in UML

Behavior - along the lines defined in UML

A structural entity may be an active actor (with a thread of control) or a passive object.

Actors respond to messages generated by actors performing communication actions.

All behavior is triggered by and composed of actions performed by actors.

An actor instantiates an entity type/role by performing the behaviors of that type/role.

An action is the atomic unit in the specification of behaviour.

An action converts a set of inputs and into a set of outputs.

Repeatable behaviors are often modelled as discrete event-driven processes.

The time between events can small enough to simulate continuous behaviors.


The primacy of behaviour: the principle that system theory is concerned with systems that display regular, repeated or repeatable behaviors.

“The principal heuristic innovation of the systems approach is what may be called ‘reduction to dynamics’ as contrasted with ‘reduction to components’ ” Laszlo and Krippner.

For example, in the context of Forrester’s “System Dynamics”, Meadows defined a system as follows.

“A set of elements or parts that is coherently organized and interconnected in a pattern or structure that produces a characteristic set of behaviors." Meadows

For more, read The primacy of behavior.


State: the current structure of a thing, as described in the current values of its variable properties.

An entity’s concrete state is directly observable in the values of its physical position, energy and material variables.

E.g. the location, temperature, colour, and matter state (solid/liquid/gas) of a thing you see in front of you.

Information state is found in the values of descriptive variables held in a memory or database of some kind.


Event: a discrete input that triggers a process that changes a system’s state, depending on the current state.

(Note: this is the basis of modelling a system using discrete event dynamics and system dynamics.)

(Note: information systems also consume enquiry events that report current state.)


Process: one or more state changes over time, or the logic that determines which state changes lead to which other state changes.

A process may be directly observed in changes to the state of the world.

E.g. an apple falls from a tree; a cash payment is handed by one actor to another.

In the abstract, a process is a description or specification of discrete or continuous state change.

E.g. a flow chart showing the control logic governing event-triggered activities that result in discrete state changes.

E.g. mathematics describing the continuous change to the position of a planet in its orbit.


Hysteresis: the process by which a system’s information state can be derived by replaying all events that have so far crossed the system boundary.


Deterministic: the quality of a system that means its next state is predictable from its current state and input event.

Stochastic: the quality of a system that means its next or future state is not predictable, and appears random.

(Note that what is describable as a deterministic system can appear stochastic over the long term.)


Linear change: progress or change over time that is represented in a graph as a straight line (or else a regular shape).

Non-linear change: progress or change over time that is represented in a graph as a curve (or else an irregular shape).

(Note what you model as deterministic can behave in a non-linear way over the long term.)


For more, read Determinism and hysteresis


Complex: a term that implies complicated in some way, but for which there is no agreed measure.

The term complex is sometimes used to mean a non-linear or stochastic system.

But simple deterministic systems can behave in non-linear or stochastic ways.

The only way to measure the complexity of a system is by reference to a description of it structures and behaviors.


Bertalanffy said system elements are discrete, can be classified into kinds, can be counted, and the relationships between them can be described.

“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

Suppose we were able to count element kinds and relationships in an abstract system description

Then count instances of those in a concrete system that realises that description.

How should we combine those numbers into an overall complexity measure?

There is no widespread agreement; scores of complexity measures have been proposed.

(Here, we might propose: complexity = the number of event/state combinations * the average procedural complexity of the rules applied to them.)


For more, read Complexity.

System change

This complex topic is explored elsewhere; this section is only a brief distillation of key terms and concepts.

System adaptation: a change to the state of a system, which changes the value of at least one variable.

System transformation: a change to the nature of a system, which changes the type of at least one variable or behavior.

“The distinction is fundamental and must on no account be slighted.” Ashby (1956)


Meta system: a system that defines a system or transforms it from one generation to the next.

Meta systems include biological evolution, which depends on the process of sexual reproduction and DNA to define and change an organic system.

And those human actors who perform processes (as in an analysis and design methodology) to define and change a designed system.


These two triangles may help to explain the relationship of a meta system to a system.

Meta system


System architect role

<define>               <idealises>

Methodologists  <observe and envisage> Architects

System roles

<define>                        <idealise>

Architects  <observe and envisage>  System actors


Read Stability and change in entities and systems for a longer discussion of system change and more system change varieties.


Natural systems and designed systems

If it ain’t described before it exists, then ain’t designed.


Natural system: a system that runs before it is described by man.

Its repeated behaviors evolved without externally-defined drivers or goals.

E.g. a solar system, weather system or biological organism.


Observation of a natural system in operation precedes its description

First, the natural system runs in reality as phenomena instances.

·         The Krebs cycle was a system in operation before it was described.

·         The solar system was in operation before it was described.


The solar system can be called a discrete entity – meaning it is separable from the rest of the universe.

Telling you that the orbiting planets form a solar system conveys an additional meaning.

It tells you that the system’s behavior can – conceivably - be tested against a description.


Orbiting planets as a system

“The solar system”

<define>       <abstracts features of>

Astronomers  <observe and envisage>  Planets in orbits


The correspondence of the real solar system to its description need not be perfect.

It only needs to be near enough to help people predict its behavior – well enough.


The solar system is composed of a few planets that display regular behaviors.

If/when those regular behaviors cease, that entity will stop being the system we understood it to be.


Designed system: a system described by man before it runs.

Its reproducible behaviors are defined in response to externally-defined drivers or goals.

E.g, a cuckoo clock, a motor car, an accounting system, a choir, a tennis match.


The description of a designed system precedes its operation in reality.

First, the designed system is described as a set of phenomena types.

·         Microsoft Word had to be described in coded types before it could be used to write a document.

·         A professional tennis match was described in laws before any matches of that type were played.


A tennis match can be called a discrete entity – meaning it is separable from the rest of the universe.

Telling you that it is a system conveys an additional meaning.

It tells you that the system’s behavior can – conceivably - be tested against a description.


Tennis match as a system

Laws of tennis

<write>   <abstract features of>

LTA  <observe and envisage>  Tennis matches


The correspondence of a real tennis match to its description need not be perfect.

It only needs to be near enough to help people predict and direct its behavior – well enough.


A tennis match is composed of players and officials that display regular behaviors.

Without those regular behaviors, there is nothing describable as tennis match.

Social systems, social entities and social cells

Social system: a system in which animate actors play roles in regular, repeatable processes.

E.g. bees collecting pollen for a beehive; an orchestra’s performance of a symphony.

The symphony score is a system description; every performance of that symphony instantiates that system description in reality.


Social entity: a group of actors who may chose their own behaviors, and may interact to reach agreed aims.

E.g. the group of actors hired to play in an orchestra, who may agree to hold a party after the performance.

Since the roles and rules of a social entity are adhoc and in flux, it cannot be described and tested as matching that description.


System theory is primarily about the roles in the symphony (the system).

Some systems thinking is more about the social entity - the actors in the orchestra, and their motivations.

A social entity may succeed in meeting goals, of its actors or sponsors, despite the roles and rules of a system in which the actors work.

But the ideal business is a social entity in which actors work happily in the roles of whatever system those actors are hired to work in.


Social cell: a social system whose roles reward the actors of a social entity sufficiently well to ensure the actors voluntarily perpetuate the system.

In other words, there is a symbiotic relationship between the roles of the social system and the actors of the social entity.

E.g. regular choir rehearsal meetings, a tennis club, and Japanese tea ceremonies.

Reward examples include hope, comfort, endorphins and money.

The very idea of a particular social cell may be so appealing that other actors, who hear of it, may replicate it (cf. Dawkin’s “meme”)


Read Social cells for discussion of a social cell as mutually beneficial, or insidious, or even as a parasite on society.


Cybernetics after Weiner

Norbert Wiener (1894-1964) founded cybernetics – about how control systems 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 has influenced systems thinking in general, and business system thinking in particular.

Nowadays it seems trite point out that a business system is connected to its wider environment by feedback loops.

It monitors and directs entities in its environment; and gathers, stores and produces information to do this..


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

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

Control system

Target system

Regulator specification

<forms>                         <realised by>

Control engineers  <observe and envisage>  Regulator

<monitor and control>  Regulated system


A basic principle of cybernetics is that a control system has to hold a description or model of the target system.

A brain has to form a mental model of what it perceives to be out there through its senses.

A business has to maintain documented models of entities and events it monitors and directs through information feedback loops.

These models contain only a selection of facts or variables abstracted from that what is monitored and directed.


Tennis match example

When tennis match umpires monitor events in a tennis match, they form mental models of the players’ actions

The match officials direct players’ actions according to the laws.

Control system

Target system

Laws of tennis

<write>           <realised by>

LTA  <observe and envisage>  Match officials

<monitor and control>  Tennis match


Suppose a tennis player scratches his nose; that is an act in reality but not in the tennis match system.

The action is irrelevant to the target system controlled by the umpire.

The player’s breathing and biochemistry (though essential in reality) are also outside the described system.


Heating system example

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.

Control system

Model of target system variables


Target system

Target system behavior

Heating system on/off


The thermostat switches the heating system on and off, according to its model of the temperature.

Control system

Target system

Thermostat drawings

<draw>                 <realised in>

Engineers  <observe and envisage>  Thermostats

<monitor>  Temperatures

<control>  Heating systems

Cybernetics after Ashby

See the introduction to this paper for discussion of Ashby’s ideas.

He eschewed discussion of consciousness; his basic idea might be distilled into one sentence thus.

A brain is collection of brain cells that interact to maintain body state variables by sending/receiving information to/from bodily sensors and motors.


Ashby picked out variables relevant to his interest in the brain as a control system, putting aside other things you might consider important to being human.

His Design for a Brain book holds an abstract description of a brain, which in turn holds an abstract description of a body’s physical variables.

Much as an engineer’s specification holds an abstract description of a control system, which in turn holds an abstract description of the physical variables it controls in a target system.


Cybernetics has influenced systems thinking in general, and business system thinking in particular.

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

It receives information about the state of entities and activities in its environment, and records that information in memory.

The state information it receives and stores must model reality well enough, else the system will fail.

It outputs information to inform and direct entities and activities as need be.


“Ashby formulated his Law of Requisite Variety stating that "variety absorbs variety, defines the minimum number of states necessary for a controller to control a system of a given number of states."

This law can be applied for example to the number of bits necessary in a digital computer to produce a required description or model.

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". Wikipedia 2017

Read Ashby’s ideas for further discussion of Ashby’s law.

Discrete v continuous system dynamics (Forrester)

Forrester (1971) promoted System Dynamics as tool for system observers to describe system behaviors in terms of stocks and flows.

A stock is a dynamic set of things – it has a number of members - a quantity - a stock level.

A flow between two stocks represents events that change stock levels over time.


The state of a narrow ecology can be described in terms of two variables: the quantity of sheep and the quantity of wolves.

Forrester’s System Dynamics helps to explain how these stocks interact.

It employs the idea of causal loop between what Engels might call opposites.

A growth in the stock of


will increase the stock of


A growth in the stock of


will deplete the stock of



The state of the wider world’s ecology can be described in terms of three variables: the mass of plants, the mass of animals and the mass of oxygen in the atmosphere.

We all depend on the fact that plants and animals balance the stock of oxygen.

Forrester’s System Dynamics helps to explain how these stocks interact so as to reach a balance.

A growth in the stock of


will increase the stock of


A growth in the stock of


will deplete the stock of



However, not all processes and feedback loops act to keep a system stable.

A system may deplete a stock to zero, or increase a stock continually; and this kind of instability may be desirable.

A system may exhaust a resource it needs: E.g. A moon rocket exhausts its fuel in a few minutes.

A system can continually expand or grow in some way: E.g. A business increases its revenue every year.


Note: the long-term impact of many interactions between two populations can be “chaotic” rather than homeostatic.

For more, read Modelling a continuously varying system using System Dynamics.

Discrete dynamics – system state advances incrementally in response to discrete events

The logic of system theory can be expressed in a model of discrete entities and events.

E.g. A business information system monitors and directs individual entities and events in its environment.

That system can be described in the form of a logical entity/event model

The model is translated into the software of a transaction processing system.

The coded system is first tested and then implemented as a live/production system.


In operation, the system runs in a feedback loop with its environment.

The system:

·         detects events and changes in the environment

·         maintains a model of the entities and events that it monitors

·         sends messages to direct entities and events in the real world.

If the system does not successfully monitor and direct entities and events in its environment, then it is changed or abandoned.

It supports and enables current behaviors, but does not predict their long-term outcomes.

Continuous dynamics - system state advances continually in response to continuous forces

How to model inter-related populations (say wolves and sheep) that are continuously changing?

A popular method, Forrester’s system dynamics, is distilled in the graphic below.

System dynamics

Model of Stocks and Flows

<create and run>                   <idealise>

System modellers     <observe and envisage>     Causal loops


The purpose of system dynamics is not to draw diagrams directing designers how to build a system.

It is to mimic or predict the outcomes of the built system - by running the system dynamics model as a simulation.


Causal loop diagrams and system dynamics models are high-level abstractions.

They model stocks of entities (wolf pack, sheep flock).

They don’t model individual entities (a wolf, a sheep), unless you count each “1” in a stock total as a model.

They model batches of events (number of sheep killed per time unit).

They don’t model individual events (a single predation), unless you count each “1” in a number of events as a model.


You can convert a discrete entity/event model into a system dynamics model by abstraction.

You cannot recover the discrete entity/event model from the system dynamics model.

Because the process of abstraction erases all qualitative attributes (names, addresses, descriptions, dates).

It also erases all instance-level entities, leaving only the quantitative volumes of entity types.

The idealism triangle

It is hard to overemphasis the importance of what Ashby said about abstraction.

As Ackoff and Ashby both said in their different ways.

A group of people doing things is not a system just because people call it a “system” or an “organisation”.

To be called a system, an entity must exhibit (manifest, instantiate, realise) the properties of a system.

Until those properties have been described and observed, the entity is just a named part of the universe.


In short, an entity is only a system in so far as it realises an abstract system description.

The mark of a good system description is that you can test how well it is realised in real-world phenomena.

This triangle separates system describers from system descriptions and the real world behaviors they observe.

Ashby’s cybernetics

System descriptions

<create and use>                  <realised by>

System describers <observe and envisage> Real world behaviors


Every living organism, every hamburger, every US government is unique in its infinitely rich and complex detail.

But every US government is testable as a realising the abstract system description known as the the US constitution.

This triangle separate the US founding fathers (and their successors) from the US constitution and its realisation by actual federal governments.

US government

US constitution

<created>                        <realised by>

US founding fathers  <envisaged>       US governments


The US constitution defines the roles and rules of the essential actors in the US federal government system.

The roles include the Congress (the legislative branch), the President, the court system (the judicial branch) and the States.

It also defines relations between actors playing those roles.

(It does not define the roles or rules of subordinate institutions created by federal governments.)


Different people may conceptualise the same named entity as different systems - or no system at all.

A particular (concrete) federal government is a system when its actors performs behaviors described in the generic (abstract) US constitution.

However, the same federal government may perform other behaviors, and may be conceptualised from a different perspective as a different system.


The US constitution also defines the meta system to be used (by system thinkers who succeed the founding fathers) to amend the constitution.

More on von Bertalanffy

Today’s general system theorist need not embrace all Bertalanffy wrote.

Some ideas von Bertalanffy supposed to be general don’t appear in this update of GST.



Hierarchy is not essential to all systems.

The designer of a human or computer activity system has to strike a balance between centralisation and distribution of process control.

Centralisation implies some kind of management hierarchy; and distribution implies its literal opposite – an anarchy, or a network.

For more, read Hierarchical and network organisations.


Homeostasis and thermodynamics?

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.

And read Introducing systems thinkers for why the general system theory discussed here ignores thermodynamics.


Inexorable progress?

Bertalanffy stretched his ideas into proposals about human psychology and the meaning of life.

“Life is not comfortable setting down in pre-ordained grooves of being; at its best, it is élan vital, inexorably driven towards higher forms of existence”.

It is possible Bertalanffy borrowed the idea of inexorable progress from Marxism.

The fact is, inexorable progress is not what one finds in nature.

Read Marxism, system theory and EA for a critique of this idea.


Goal directedness?

Any observer can ascribe goals to a system – be it designed or natural – be it a cuckoo clock, a choir or a beehive.

Different observers can ascribe different goals; and different stakeholders have different concerns.

Obviously, systems are designed with goals in mind – prioritising the goals of system sponsors and owners.


GST presumes the individual actors in a system perform actions in accord with the goals of the roles they play.

By contrast, social systems thinkers interpret goal-directedness as meaning that individual actors have their own goals.

Ackoff spoke of human institutions or organisations as “purposeful systems”, meaning each actor as their own purposes.


GST does not identify individual actors, so can say nothing about any goals individual actors have - outside their roles in a system.

The goals of individual actors may have to be addressed by business managers and a business change or HR team in parallel with enterprise architecture.

For more, read Goal-directedness.


What more of von Bertalanffy?

He eventually committed to this book (1968) in which he said GST brings us “nearer the goal of the unity of science”.

The quotes below are drawn from selected passages, which you can find on this web page.

“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.”

conceptions appear in contemporary science that are concerned with what is somewhat vaguely termed 'wholeness'.

I.e. problems of organization, phenomena not resolvable into local events, dynamic interactions manifest in difference of behavior of parts when isolated or in a higher configuration, etc.

In short, 'systems' of various order not understandable by investigation of their respective parts in isolation.”

“General System Theory… is a general science of 'wholeness'.”

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

“Information and Feedback: 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.”

“Causality and Teleology: You cannot conceive of a living organism, without taking into account what variously and rather loosely is called adaptiveness, purposiveness, goal-seeking and the like.”

“The System Concept: In dealing with complexes of 'elements', three different kinds of distinction may be made: (1) according to their number; (2)  according to their species; (3) according to the relations of elements.”


Other sources say:

“Systems theory is the interdisciplinary study of systems in general, with the goal of elucidating principles that can be applied to all types of systems at all nesting levels in all fields of research.

The term does not yet have a well-established, precise meaning.” Wikipedia

“Systems theorists seek to explain the behavior of complex, organized systems from thermostats to missile guidance computers, from amoebas to families.” “Systems Theory” Gail G. Whitchurch, Larry L. Constantine

“Some scholars consider general system theory to be broader than a theory, but rather an alternative Weltanschauung—a unique worldview” (Ruben & Kim, 1975).”


Not everybody accepted that GST is valuable.

The schools of systems thinking have different roots and perspectives; different schools hold sway in different regions.

“General system theory, like other innovative frameworks of thought, passed through phases of ridicule and neglect. (Laszlo and Krippner)


A respectable summary of GST is quoted below:

"von Bertalanffy.emphasized that real systems are open to, and interact with, their environments, and that they can acquire qualitatively new properties through emergence, resulting in continual evolution.

Rather than reducing an entity (e.g. the human body) to the properties of its parts or elements (e.g. organs or cells),

systems theory focuses on the arrangement of and relations between the parts which connect them into a whole (cf. holism).
This particular organization determines a system, which is independent of the concrete substance of the elements (e.g. particles, cells, transistors, people, etc).
Systems concepts include: system-environment boundary, input, output, process, state, hierarchy, goal-directedness, and information." Principia Cybernetica Web



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