System theory terms and concepts

Copyright 2016 Graham Berrisford. One of about 300 papers at Last updated 23/02/2018 12:15



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

So, you might assume their profession uses a controlled vocabulary of system concepts; but not so.

This paper picks up from where the introduction to general system theory left off.

It is presumed you understand Bertalanffy’s early ideas about systems.

This paper explains many more terms and concepts, elaborating in the vocabulary listed in this glossary.


The systems movement 1

System boundary. 2

Interactions between system actors. 2

Deterministic systems. 4

Cybernetics. 6

Abstract and concrete systems. 9

System structure and behavior 11

System stability and change. 12

Natural systems and designed systems. 13

System dynamics. 15

Social systems, social entities and social cells. 16

The idealism triangle. 17

Conclusions and remarks related to systems. 18


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)


Understanding systems involves drawing three distinctions.

There are forms and functions - structures and behaviors - within a system.

There are descriptions and realisations - abstract and concrete systems.

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

These and many other system concepts are explained below.

System boundary

“Systems concepts include: system-environment boundary, input, output, process, state….”   Principia Cybernetica


System environment: the world outside the system of interest.

Most system design methods separate a system from its wider environment

The environment of one system may be a wider system.


Closed system: a system that does not interact with anything in the wider environment.

All events of interest are internal to the system.

E.g. A System Dynamics model is a closed system of stocks and flows.


Open system: a system that interacts with entities and events in a wider environment.

Inputs can change the state of the system; outputs can change the state of entities in the environment.

Many system design methods scope a system by its inputs and outputs

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


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

It encapsulates the system’s internal structures and behaviors.

It defines the system’s input-process-output boundary.

Inputs and outputs can be flows of material, energy or information.


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.


Read System boundary for more.

Interactions between system actors 

It usually presumed that all actors in an activity system interact (directly or indirectly) else there would be two or more separate systems.

The actors in a system can interact by forces, matter, energy or information, the last of which is the main interest here.



Many physical, mechanical and biological systems involve an exchange of forces.

The planets in the solar system follow orbital paths dictated by gravity.

Cyclists interact with bicycles by converting forces into motion.

However, enterprise architecture is not mechanical engineering.



The 2nd Law of Thermodynamics says that across the universe, the total entropy (disorder) increases with time.

But locally, an entity can use energy to prevent an increase in entropy; and this is fundamental in thermodynamic systems.

E.g. a plant interacts with the sun by using its energy to build and maintain biomass.

Any organised entity must draw energy from its environment to maintain order and hold chaos at bay.

However: “In this discussion, questions of energy play almost no part; the energy is simply taken for granted.” Ashby.


Matter / materials

Organisms build and maintain their bodies from primitive chemicals consumed (this is called autopoiesis)

Animals interact with plants by exchanging oxygen and carbon dioxide.

Manufacturing and supply chain businesses transform and move materials.

However, enterprise architects are usually only interested in material flows that are associated with information flows.



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

Our main interest is in social and business systems in which actors exchange information.

The actors communicate and perform other activities according to messages received and memories retained.

The information exchanged includes descriptions, directions and decisions - and requests for them..


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.


Read Information feedback loops for more.

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


Ashby said an entity is only describable as a system when, and in so far as, it exhibits some regular or repeatable behaviors.

In his view (as many dictionary definitions suggest) a system is orderly rather than disorderly.


A concept important in systems thinking history is determinism.

In the 1950s, Ashby wrote that the notion of a deterministic system was already more than century old.

Sociologists, biologists, psychologists and control system engineers all describe deterministic systems.





In response to

In the light of



act appropriately

electro/chemical inputs

their current electro/chemical state

Control systems


direct controlled devices

messages received

the state of controlled devices or their environment

Discrete event models


change state


current entity state variable values.

System dynamics


increase or decrease

inter stock flows

current stock volumes



act and communicate

messages received

their memories or “mental images”  (Boulding 1956).


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 particular stimulus by 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


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

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

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

Several 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).


Generally speaking, an activity system is an island of orderly behavior, describable in terms of:

·         roles that describe actors - persistent entities – active structures – locatable in space

·         rules that describe activities – behaviors – which follow some logic or law over time..

E.g. A professional tennis match features actors who play defined roles and follow defined rules.

As long as the entity follows the laws of tennis, it is well-called a system.

If the laws of tennis change, it becomes a new system or system version.


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 in Forrester's System Dynamics works (to be discussed).


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.

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.

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 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.

Abstract and concrete systems

Ashby saw a system as an island of orderly behavior in the ever-unfolding process that is universe.

“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)

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


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.


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 description.


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.)


This triangle represents the idea more graphically.


System theory

System descriptions

<form>                        <idealise>

Systems thinkers <observe and envisage> Real world entities


This table separates abstract and concrete systems.


Abstract system description

Theoretical system

System description

Concrete system realisation

An empirical system

A system in operation


This table contains examples of concrete entity that realise (or instantiate) an abstract system description (or type).


Abstract system description

“Solar system”

Laws of tennis

The score of a symphony

The US constitution

Concrete system realisation

Planets in orbits

A tennis match

A performance of that symphony

A US government


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.


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

Many find it difficult to understand the implications of what was written in 1956 by Ross Ashby.


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

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

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

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

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


People point at a machine or a business and say "the system is that thing there".

The trouble is: every machine or business realizes countless describable systems.

We must select the facts relevant to some already-given interests or concerns.

The real-world entity is a concrete system only in so far as it performs the behaviors in an abstract system description.

In other words, it is an empirical system only in so far as it realises (or instantiates) a theoretical system description (or type).


A theoretical system describes roles played by actors and rules governing their activities.

E.g. A symphony score describes roles played by orchestra members (listed vertically), and rules governing their activities (scripted horizontally).


An empirical system is an entity in which actors play roles in performing described activities.

E.g. A symphony performance is an entity in which orchestra members perform the activities described in a symphony score.


An entity is only an empirical system to the extent that, verified by observation, it matches a theoretical system.

E.g. An orchestra delivers a symphony performance to the extent that, verified by observation, its actions in reality match those described a symphony score.


Actors are addressable in space, their activities are located in time.

E.g. Orchestra members are addressable in space, the sounds they make are located in time.


System activities change the state of the system (or the state of entities in the system's environment).

E.g. Playing a score advances the state of a performance (and the memories of audience members).


A theoretical system enables us to predict/measure/test state changes in an empirical system.

E.g. A symphony score enables us to predict/measure/test the progress of a symphony performance.


Note that the actors playing roles in one system can play roles in other systems at the same time.

For example, orchestra members are more than the parts they play in a symphony performance; they simultaneously play roles in other systems.

They are tax payers in a tax system - sons or daughters in a family – guests in a hotel system.

These disparate systems do not meaningfully add up to one system, unless and until the systems are related in one holistic system description.


Note also that Ashby, Checkland, and Ackoff all recognised that one discrete entity can be conceptualised as many different systems.

So, to agree an entity is "a system", we must share (in our minds or documentation) a description of its behaviors.

System structure and behavior

The structure/behaviour dichotomy:.the distinction between structures addressable space and behaviors that happen over time.

This table draws the distinction in reasonably natural language.


Structure: passive structures and actors in space

Behavior: atomic actions and processes over time

Structures -  shaped or directed by behaviors.

Entities -  created, changed and destroyed by Events.

Stocks -  incremented and decremented by Flows.

Components - perform Processes and deliver Services.

Behaviors - performed by active structures.

Events - create, change and destroy Entities.

Flows -  increment and decrement Stocks.

Processes performed and Services delivered by Components.


This table draws the same distinction as in the Unified Modelling Language (UML) standard.


Structure - along the lines defined in UML

Behavior - along the lines defined in UML

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.


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.

System stability and change

Again, a system is an entity whose behavior is organised, regular or repeatable.

Like every other discrete entity, such an activity system has a discrete life time, which can be long or short.



Life span



In our solar system

for eons

the planets

orbit around the sun, as determined by the law of gravity.

In a human body

for years

the organs

process chemicals, as determined by instructions encoded in DNA.

In a clock

for days

the pendulum

sways back and forth, converting a coiled spring's energy into clock hand movements.

In a tennis match

for hours

the players

behave in an orderly way, as determined by the laws of tennis.

In a symphony performance

for minutes

the orchestra members

make sounds, as scripted in a musical score.


Ashby was keen we differentiate inter-generational system mutation from system state change within the life span of a system.

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


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

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).


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


The following three notions are central to modelling activity systems.

1) The state of an activity system may change.

2) The roles and rules of a system are fixed for a system generation.

3) If the roles or rules of an entity change, then it realises a different system.


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


Read System stability and change for more.

Natural systems and designed systems

A natural system has evolved without any intent; its outcomes might be regarded as its aims.

A designed system was created by intent; so, it can be tested by comparing its outcomes with its aims.

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


Generic concept

Natural systems

Designed systems






Planets are


Humans and machines



bodies in space that

play roles in

play roles in

instantiate classes so as to


complete orbits and

performing processes and

performing processes and

transform and remember data and


exchange forces that

exchanging materials that

exchanging information to

exchange information to


maintain the solar system.

sustain an organism.

meet business goals

monitor and direct external entities


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.

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, or the world cup.

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


System concepts




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 acted upon during behaviors.


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.

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.

System dynamics

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


Discrete (or digital) system state change

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

The term digital is used to describe signals that are chunked into discrete units, as in a clock that displays the time as numbers. 

Most business systems are driven by discrete events and experience discrete state changes.

So they are modelled as what is called discrete event-driven systems (DEDS).


Continuous (or analogue) system state change

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

The term analogue is used to describe signals that vary continuously, as in a clock with revolving hands.

However, continuous systems can be modelled as discrete event-driven systems.

“Often a change occurs continuously, that is, by infinitesimal steps, as when the earth moves through space, or a sunbather's skin darkens under exposure.

The consideration of steps that are infinitesimal, however, raises a number of purely mathematical difficulties, so we shall avoid their consideration entirely.

Instead, we shall assume in all cases that the changes occur by finite steps in time and that any difference is also finite.

We shall assume that the change occurs by a measurable jump, as the money in a bank account changes by at least a penny.

… consideration of the case in which all differences are finite loses nothing; it gives a clear and simple foundation;

and it can always be converted to the continuous form if that is desired.” Ashby 1956

Forrester and Meadows: System Dynamics

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

The system of interest is modelled as a closed 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?

The long term outcome of a system (at the macro-level) is not predictable from knowledge of the system’s roles and rules.

It is sometimes possible to mimic or predict the outcome 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 happens to system state variable values (stock quantities) over long period of time.


Read Modelling using System Dynamics for more.

Social systems, social entities and social cells

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.


Social system: a social entity 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.


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.

The idealism triangle

As Ashby and Ackoff 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 realising the abstract system description known as the US constitution.


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.

The constitution also defines relations between actors playing those roles.

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

It does however defines the meta system to be used (by Congress or Constitutional Convention) to amend the constitution (change the system) itself.

Conclusions and remarks related to systems

This paper has attempted to restore order to the concept of a system - along the lines defined by Ashby.

An activity system is an island of orderly behavior in the ever-unfolding process that is universe.

It is describable in terms of:

·         roles that describe actors - persistent entities – active structures – locatable in space

·         rules that describe activities – behaviors – which follow some logic or law over time.


A theoretical system is a description of roles played by actors, and rules governing their activities.

The scope of the system is determined by its describers.

Give some aims or interests, describers focus on some activities performed by some actors

They describe instances of actors and activities as general types - the roles and rules of the system.


An empirical system is an entity in which actors play roles in performing described activities.

This entity is only an empirical system when, verified by observation, it matches a theoretical system.


Ashby's idea of a system gives us insights into a wide variety of disciplines.

Together with Darwin's theory of evolution, it leads us towards theories of information and communication.

It leads us towards answers to philosophical questions about description and reality.

And helps us to make sense of what enterprise architecture is about.




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