Introducing cybernetics

Copyright 2016 Graham Berrisford. One of several hundred papers at Last updated 14/10/2018 21:59


This paper introduces some of the terms and concepts of cybernetics.


Preface. 1

Weiner 1

Ashby. 3

Alan Turing. 4

Further reading. 5



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

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


A general idea is 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.

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

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


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

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

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


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

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

It members included Ross Ashby and Alan Turing – two of the three thinkers discussed below.


Norbert Wiener (1894-1964) founded cybernetics.

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

He coined the term Cybernetics for the concept of natural and artificial organisms steering themselves using feedback.

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

He designed self-steering anti-aircraft firing system using radar feedback.

The phrase “the animal and the machine” suggest the principles apply to both animate and inanimate systems or machines.


A simple homeostatic system is composed of two components:

·         a control (or regulatory) system

·         a target system (or “real machine”).

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

The control system monitors a selection of variable facts observable in a target system.


Heating system example

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

Variables monitored

Variables controlled


Heating system on/off



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

Since then, cybernetic ideas have succeeded brilliantly in other spheres and applications.


Tennis match example

Tennis match umpires monitor events in tennis matches and direct players’ actions according to the laws.

The laws are defined by the Lawn Tennis Association.


Control system

Laws of Tennis


Target system

Variables monitored

Variables controlled

Player and ball behavior

Player behavior and score


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.


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.”

“Ashby popularised the usage of the term 'cybernetics' to refer to self-regulating systems” Wikipedia 2017


Ashby’s ideas include:


·         Systems are abstractions

·         Systems are deterministic

·         Cybernetics is behavioristic

·         System generation change differs from system state change

·         Cybernetics is about information flows rather than energy flows

·         The brain can be modelled as a control system

·         Variety is a measure of complexity

·         Variety absorbs variety - the law of requisite variety.


Some of these ideas are applied today in enterprise, business and software architecture methods and modelling languages.

Two of the ideas are outlined below, because they are relevant to what follows.


Systems are abstractions

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

Ashby recognized the importance of distinguishing a system from a machine (natural or designed) that realizes it.

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

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

This method, however, has a fundamental disadvantage: every [machine] 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


Ashby’s v iew

Abstract system descriptions

<create and use>                   <idealise>

Systems thinkers <observe & envisage> Concrete entities


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

You observe or envisage a system in the real world – an empirical system – an instance.

You model it in an abstract system description - a theoretical system – a type.


You model the state as variables whose values can be measured (e.g. the positions of the planets).

You model the behavior as processes (e.g. the orbits of the planets) that maintain or advance variable values

The model is a type; it hides the infinite complexity of real-world actors and activities that instantiate (or realise) the model

If and when the concrete system runs in reality, you can test that real-world entity against what the abstract system predicts.


Variety absorbs variety - the law of requisite variety

Ashby’s 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".



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.


System change

Ashby insisted we distinguish two kinds of system change:

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

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


Alan Turing (1912 –1954) was computer scientist, mathematician, logician, cryptanalyst, philosopher, and theoretical biologist.

The members of The Ratio Club were interested in understanding biological brains and developing artificial intelligence.

“Turing led three different talks.

In 1950, Turing introduced the Turing Test and focused on how intelligent machines might be developed.

Turing suggested using adaptive machines that could learn over their lifetime.

In 1952, Turing described his then unpublished work on reaction-diffusion models of morphogenesis.

This launched him into new directions of theoretical biology and was incredibly influential in the field of computer modelling.

In a letter to William Ross Ashby, he said: "In working on the ACE [Automated Computing Engine] I am more interested in the possibility of producing models of the action of the brain than in the practical applications of computing."



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

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

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

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



Roles, Rules & Variables

<create and use>                   <idealise>

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

Further reading

This paper has introduced some of the terms and concepts of cybernetics.

Understanding Ashby’s ideas helps you to understand much else in the field of systems thinking.

If you want to read this work in sequence, move on to System Dynamics.

You can find a list of cyberneticians at



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