Introducing cybernetics

Copyright 2016 Graham Berrisford. One of about 300 papers at Last updated 04/03/2018 16:57


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

It is presumed you have read Introducing system ideas.


The systems movement 1

Deterministic systems. 1

Weiner: cybernetics. 3

Ashby: cybernetics. 4

Forrester and Meadows: System Dynamics. 5

Conclusions and remarks. 6


The systems movement

Bertalanffy’s ideas were picked by others including William Ross Ashby, Anatol Rapoport and others.

In the 1950s, they looked for patterns and principles common to systems across all sciences.

 “The systems movement took hold in post-war America with the convergence of ideas drawn from biology, systems engineering, cybernetics and sociology.

·         Biology, with its evolutionary emphasis, contributed the ideas of emergence and hierarchy among and within living systems.

·         Systems engineering contributed the “hard systems” approach to problem solving that grew out of… trying to develop weapons and logistical systems during WWII.

·         Cybernetics introduced the idea of control through information (feedback) in operational systems.” Bausch (2001)


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.


“Cybernetics deals with all forms of behaviour in so far as they are regular, or determinate, or reproducible.” Ashby 1956

In short, cybernetics is about deterministic systems.

Deterministic systems

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 2017


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

Unless otherwise stated, quotes below are from Ashby’s Introduction to Cybernetics” (1956).

In Ashby’s view (as many dictionary definitions suggest) a system is orderly rather than disorderly

He wrote that the notion of a deterministic system was already more than century old.


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

A deterministic system, in a given state, will respond to a specific stimulus or eventby acting in a predictable way.

Paradoxically, as a result of processing a series of events, a system may change in an unpredictable, chaotic or non-linear fashion.

Read Determinism and hysteresis for more.


Ashby offered the following example of an orderly deterministic system.

“The male and female three-spined stickleback form… a determinate dynamic system.

Tinbergen (in his Study of Instinct) describes the system's successive states as follows” Ashby, 1956

The stickleback roles are shown columns of the table below (they could be shown as swim lanes in a process flow diagram).

The actors play those roles by communicating, by sending visual signals (information flows) to each other.


Stickleback mating system

The female’s role is to

The male’s role is to

present a swollen abdomen and special movements

present a red colour and a zigzag dance

swim towards the male

turn around and swim rapidly to the nest.

follow the male to the nest

point its head into the nest entrance

enter the nest

quiver in reaction to the female being in the nest

spawn fresh eggs in the nest

fertilise the eggs


The table above is an abstract system, a description of roles that typify actors.

Reading left to right, top to bottom, each visual signal (information flow) triggers the partner to act in response.

A concrete system would be any pair of sticklebacks that realises the two abstract roles.


Several other system theory concepts appear in this example.


System concept


Communication by the sending and receiving of information flows (here, visual signals)


Activities that advance the state of the system

Logical structure (role)

A list of activities an actor is expected to perform when playing a role

Physical structure (actor)

A concrete individual that cooperates with others by playing definable roles

Passive structure

An object that is acted in or on (here, nest and eggs).

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 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.

It records that information in memory.

The state information must model reality well enough, else the system will fail.

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


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.


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

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

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


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

Target system

Thermostat drawings

<draw>                      <realised in>

Engineers  <observe and envisage> Thermostats

<monitor>  Temperatures

<control>  Heating systems

Ashby: cybernetics

“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


In “Design for a Brain” (1952), Ashby presented the brain as a system.

He selected variables relevant to his interest in the brain as a control system,

Setting aside other things you might consider important to being human, such as consciousness.

The basic idea might be distilled into one sentence thus.


Generic system description

Ashby’s design for a brain

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.


Ashby’s 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.


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.

It is interpreted here as meaning:

·         A control system’s information state models only those variables in the target system’s concrete state that are monitored and controlled.

·         For a homeostatic system to be stable, the number of states of the control system must be at least equal to the number of states in the target system.

·         The more ways that a homeostatic system can deviate from its ideal state, the more control actions a control system will need.


Read Ashby’s ideas for a considerable elaboration of this law and its implications.


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

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.

Forrester and Meadows: System Dynamics

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

Discrete system state change means that a system’s state advances incrementally in response to discrete events.

Continuous system state change means that a system’s state advances continually in response to continuous forces or inputs

However, continuous systems can be modelled as discrete event-driven systems, as software tools that implement Forrester's System Dynamics.


System Dynamics is an agent-based (or agent-oriented) system simulation method.

The system of interest is modelled as a system of stocks related to each other by flows.

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 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.

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.


Some say complexity “emerges” at the macro level from simplicity at the micro level.

Individual actors, following simple behavioral rules, can generate “complex behavior” at a macro level.

However, if there is complexity, it is not in the behavior, it is in the trajectory followed by quantities over time.


Read System Dynamics for more.

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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