“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.” Bertalanffy

This and some quotes below are from “General System theory: Foundations, Development, Applications” (1968), Ludwig von Bertalanffy.

General system theory incorporates cybernetics, a movement that also grew in the 1950s.

Systems concepts include: system-environment boundary, input, output, process, state, hierarchy, goal-directedness, and information."

This and some other quotes below are from Principia Cybernetica Web.

In most modern system thinking, the system of interest is more than a collection of things; it is dynamic.

E.g. the solar system, an organism, a software system, a choir, a game of poker.

Meadows defined a system thus:

“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

A system is an island of orderly behaviors in the ever-unfolding process that is the universe.

The behaviors maintain or advance the system state and/or produce outputs.

System: an entity describable as actors interacting in activities to advance the system’s state and/or transform inputs into outputs.

· The actors are structures (in space) that perform activities - in roles and processes that are describable and testable.

· The activities are behaviors (over time) that change the state of the system or something in its environment - governed by rules that are describable and testable.

· The state is describable as a set of state variables - each with a range of values.

· An open system is connected to its wider environment - by inputs and outputs that are describable and testable.

These concepts can be seen in writings of Ashby, Forrester and Checkland.

In Ashby’s cybernetics, a system is modelled as processes that advance a set of state variables.

In Forrester’s system’s dynamics, a system is modelled as inter-stock flows that advance a set of stocks (variable populations).

In Checkland’s soft systems method, a system is modelled as actors who perform processes that transform inputs into outputs for customers.

Relevance to EA

Business systems evolved over millennia out of formalising social activity systems.

They feature business roles and processes that create and use information.

EA is concerned to define business roles, processes and the information they create and use in messages and memories.

Many general system theory concepts (below) are taken for granted in today’s enterprise and software architecture methods.

Natural and designed systems

A system can evolve naturally or be designed purposefully.

Natural system: a system that runs before it is envisaged as a system.

E.g. Solar systems and hurricanes evolve naturally.

A natural system emerges without intent, with no aims in mind.

The outcomes of its repeated behaviors are unintended consequences.

Designed system: a system that is envisaged before it runs.

E.g. Bicycles and card games and symphonies are designed.

A designed system is created by intent, with aims in mind - though its outcomes may diverge from those aims.

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

The outcomes of their repeated behaviors are intended.

Relevance to EA

EA is concerned with the design of human and computer activity systems and changes to them.

Abstract and concrete systems

Ackoff, Ashby, Checkland and others have emphasised that a system is a perspective of a reality.

“Different observers of the same [concrete] phenomena may conceptualise them into different [abstract] systems.” Ackoff 1971

Ashby urged us not confuse a concrete entity with an abstract system that the entity realises.

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

Our first impulse is to point at [some real entity or machine] and to say "the system is that thing there".

This method, however, has a fundamental disadvantage: every material object contains 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)

A concrete entity is a system only when and in so far as it realises a testable system description.

We do commonly abuse the term “system”.

We point to an entity (e.g. a business organisation or a biological organism) and casually call it "a system".

But with no explicit or implicit reference to a particular system description, this is to say nothing.

Idly calling an entity (or process, problem or situation) a system is meaningless, because one entity can realise countless systems.

Abstract system: is a description or model of how some part of the word behaves, or should behave.

E.g. the standard heart beat is a theory of, or insight into, how some part of the world works.

Concrete system: is a realisation by a real-world entity that conforms well enough to an abstract system.

E.g. your own heart beating is a real-world application, or empirical example, of such a theory.

Science requires us to show the latter conforms (well enough) to the former.

The basis of system theory

Abstract systems (descriptions)

System thinkers <observe and envisage > Concrete systems (realities)

These papers take this triangular, scientific, view of system theory as axiomatic.

Note that the relationship between physical entities and abstract systems is many-to-many.

One abstract system (e.g. the game of poker) may be realised by countless physical entities (countless card schools).

One physical entity (e.g. a card school) may realise countless abstract systems (e.g. poker, whist etc).

Relevance to EA?

You might hear it said that enterprise (say IBM) is a system.

However, IBM can realise many abstract system descriptions, in parallel, some of which are disconnected or in conflict.

So to say IBM is a system is meaningless unless you point to your chosen abstract description.

IBM

System descriptions

System describers <observe and envisage> The behaviors of IBM

To exercise change control over changes to systems, there must be description of those systems.

EA abstracts system descriptions from baseline systems (observed) and target systems (envisaged).

An “architecture definition” is collection of descriptive artefacts, often including graphical models in which diagrammatic symbols substitute for words.

The definition is detailed only so far as stakeholders need it to be.

Open and closed systems

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

Closed system: a system defined (as in a System Dynamics model) without reference to its wider environment.

E.g. a causal loop diagram represents some stocks connected by some flows.

It is a model of populations (stocks) that grow and shrink in response to inter-stock event streams (flows).

The model may be incomplete, because it omits stocks in the wider environment that have a significant effect on stocks in the model.

Open system: a system defined as consuming/delivering inputs/outputs from/to its wider environment.

fined without reference to its wider environment.

The inputs stimulate some processes (in the system) to change its internal state and/or produce outputs.

E.g. a model of a computer program.

The model can be complete in the sense that it models all possible inputs and outputs of the program.

However, it cannot model everything that happens when the program runs, down to the level of electron movements in the computer.

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

Many systems thinkers (e.g. Ashby and Checkland) have spoken of systems as black boxes that transform inputs into outputs.

E.g. A factory can be seen as “black box” whose primary function is to consume input supplies and deliver output products.

Inside the factory, human and mechanical actors interact to transform the inputs into outputs in a regular and repeatable way.

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 – encloses them in input-process-output “black box”.

If you expand the system boundary then external events become internal events, passing between subsystems.

If you contract the system boundary then internal events become external events, crossing that boundary.

System interface: a description of inputs and output 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.

Relevance to EA?

EA is concerned with business systems that are open - they consume input and produce outputs.

And with systems definable as a “black box”.

The encapsulation and specification of systems by their interfaces is a fundamental tool.

Bear in mind that the inputs and outputs to be described depend on the concerns of interest.

Inputs to IBM	Outputs from IBM
Oxygen	CO2
Electricity and food	Heat
Money from customers and investors	Salaries and dividends
Raw materials and components	Concrete products
Questions and requests	Answers and documents
Raw information	Integrated or advanced information

Coupling by flows between and within systems

Coupling: the relating of subsystems in a wider system by flows.

Flow: the conveyance of a force, matter, energy or information.

Force flows

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

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

And cyclists interact with bicycles by converting forces into motion.

However, enterprise architecture is not mechanical engineering.

Energy flows

“By importing complex molecules high in free energy, an organism can maintain its state, avoid increasing entropy" Bertalanffy

The second 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.

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

Matter flows

E.g. Animals interact with plants by exchanging gases, oxygen and carbon dioxide.

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

And 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.

Information flows

The ability of actors to communicate information is an amazing side effect of biological evolution.

Senders encode meaningful information in signals; receivers decode meaningful information from signals.

The types of things recognised by animals becomes evident when we match signals that make to situations they are in..

E.g. an animal sounds an alarm call to signal an instance of the type we call “danger”.

Read System coupling varieties for discussion of coupling varieties.

Relevance to EA?

EA is concerned with business systems that are coupled by information flows.

And concern to integrate those systems to the benefit of the enterprise.

Information

“connected with system theory is… communication. The general notion in communication theory is that of information.” Bertalanffy

Our main interest is in social and business systems in which animate and/or computer actors exchange information.

The Oxford English Dictionary lists more than half a million words.

Consider data, information, knowledge, wisdom, signal, symbol, description, representation, meaning and model.

Given those ten words, how many clearly distinct concepts are there?

This table distinguishes four concepts.

WKID	meaning
Wisdom	the ability to respond effectively to knowledge in new situations
Knowledge	information that is accurate enough to be useful
Information	any meaning created or found in a structure by an actor
Data	a structure of matter/energy in which information has been created or found (by a sender or receiver)

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

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

Information flow: the conveyance of information in a message from a sender to a receiver.

Information state: the information retained in a memory or store.

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

Feedback loop: the circular fashion in which output flows influence future input flows 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 efficiently or effectively enough, then it expires or is changed.

All communication utilises a structure

The medium for information storage or communication is a matter/energy structure of some kind.

To communicate, animals use sound waves (calls), smells, gestures, etc.

Humans use sound waves, written text, flags, etc.

Computers use electronic signals, radio waves, etc.

Every structure has information potential

There are infinite structures in the matter/energy of the universe.

Some equate structure with information.

Here, we say a structure has information potential to actors.

There is actual information when actors use some information potential to create or obtain a meaning.

There is information potential in the variable	There is actual information when
angle of the sun’s rays	a human reads the time from the shadow on a sundial. a sunflower perceives the position of the sun and turns to face it
nerve impulses (electrical charges)	an actor responds by removing its hand from a hot plate
bending of a bi-metal strip	a thermostat responds by switching a heater on or off.
movements of a honey bee	honey bees dance to communicate a location of pollen.
open or closed state of an office door	actors share a vocabulary in which an open door means “you have permission to enter”.
lengths of dots and dashes (in sound, light, braille…)	actors use Morse code to communicate.
quantity in a number	an actor says 20 in reply to a request for a fact (say, the speed of a bicycle in miles per hour).

Information is meaningful to its sender and/or receiver

Senders encode meanings in data structures, and receivers decode meanings from them.

The meanings include descriptions, directions, decisions and requests for them.

Descriptions are usually divided into facts (tasty, tall, scary) about things (say, food, friends and enemies) that actors perceive as discretely identifiable.

Information has at least one sender and/or receiver

A sender (a voice crying in the wilderness) may create information in a data structure that no receiver inspects.

A receiver may find some information in a data structure that was not intentionally sent.

E.g. The sun radiates a flow of light towards a rotating earth.

A sunflower finds a direction to turn its face to optimise its energy consumption.

Different actors can find different information in the same data structure

E.g. The sun radiates a flow of light towards a rotating earth.

A sunflower finds a direction to turn its face to optimise its energy consumption.

One man reads the shadow on a sundial as describing the hour of the day.

Another concludes that the sun rotates around the earth; another that the earth spins on its axis.

E.g. the data structure in a DNA molecule may be decoded by a biological cell as instructions for making proteins.

And decoded by a human reader of the genome as carrying a gene for some life-shortening condition.

Neither actor can read and act on the data structure as the other does.

To communicate requires sharing a data structure and a language

First, the data structure of a message must be preserved (a concern of Shannon’s theory).

Second, creators and users must share a language for encoding and decoding that data structure.

Two things can go wrong.

First, the data structure is distorted between sender and receiver.

E.g. Speaker says: “Send reinforcements we are going to advance.”

Listener hears: “Send three and four pence we are going to a dance.”

The intended signal is distorted at some point between sender and receiver.

Shannon’s information theory is about preserving the integrity of a data structure.

Second, creators and users use a different a language to encode and decode a data structure.

Or the ambiguity of natural language disables communication.

E.g. A speaker says: “He fed her cat food.”

Listener 1 hears: He fed her cat – food (He fed a woman’s cat some food).

Listener 2 hears: He fed her - cat food (He fed a woman some food that was intended for cats).

Listener 3 hears: He fed - her cat foods (He somehow fed the cat food that a woman owned).

Information is a subjective view of a data structure

The information in a data structure depends on senders and/or receivers and the languages they use.

E.g. I leave my office door open.

Case 1: I do it deliberately, to signal that I am open to visitors; you read the door as saying I am open to visitors, and enter my office.

Case 2: I do it by accident, but am open to visitors anyway; you misread the door as saying I am open to visitors, and enter my office.

Case 3: I do it by accident, but am not open to visitors; you misread the door as saying I am open to visitors, and enter my office.

Relevance to EA?

EA is concerned with input and output information flows, and with material flows accompanied by information.

It is concerned with information/meaning encoded in data structures by human and computer actors.

The usual presumption is that all actors will find the same information/meaning in any given data structure, because:

· data structures are preserved perfectly

· translations of data structures from one form of language to another are perfect

· senders and receivers apply the same language to writing and reading of messages and memories.

Holism and emergent properties

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

Holism: looking at a thing in terms of how its parts join up rather than dissecting each part.

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

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

A principle of system theory is to take a holistic view of a system.

To understand even a simple system, you must see beyond its components and understand their interactions.

And in doing that, you may disregard the internal complexity of the components.

E.g.

To understand a bicycle + rider system you must understand how they interact to produce the forward motion of both.

And in doing that, you can disregard the internal biological/psychological complexity of the rider.

So, having a holistic view of a thing does not mean you know the “whole”.

You do not know all there is to know about the thing or any part of it.

This point is repeated and laboured below.

A real-world thing is infinitely more complex than any system it realises.

E.g. You watch a group of people playing cards, and work out the rules of the game.

The group of people is infinitely more complex than the card game.

To understand or motivate people, you need more than "systems thinking".

Moreover, one thing may realise several systems, even ones in conflict with each other.

Every situation of interest to you is more than whatever systems you can abstract from it.

E.g. You may look at an elephant from different perspectives as different systems and still not to see the "whole" of it.

To get a sense of the whole elephant (if it were possible) you’d need more than "systems thinking".

Naming a system after a thing you observe does not tell us what system you have mind.

Naming a thing after a system you have in mind does not restrict that thing to behaving as that system.

Because your system is not the whole of a thing; it is an abstraction.

It joins up elements of the thing that you have mind - in a way of interest to stakeholders.

It is a perspective of a thing (e.g. the solar system, a human institution or a social network).

To see the whole of a thing is to see more than a system (e.g. see craters on the moon, or people who act outside of or obstruct a system's operation).

In short, to think holistically is not to see the whole of a thing, it is to see an abstraction of it.

It is to see how its parts (at the level of granularity you choose) join up in a "system of interest" - deliberately ignoring its full complexity.

Faced with a complex situation or issue, you start with situation/issue thinking.

Systems thinking helps only to the extent the situation/issue is describable or resolvable by one or more systems.

A principle of system theory is that the whole is more than the sum of its parts.

“that a whole machine should be built of parts of given behavior is not sufficient to determine its behavior as a whole:

only when the details of coupling are added does the whole's behavior become determinate. ” Ashby 1956

Emergence: the appearance of properties in a wider or higher-level system, from the coupling of lower-level subsystems.

The emergent properties of system are not deducible from studying its components in isolation.

E.g.

Emergent property	cannot be deduced from studying
The V shape of a flight of geese	one goose
The forward motion of a bicycle + rider system	a rider or a bicycle
The famous collapse of the Tahoma narrows bridge	the structure of the bridge or the wind

“You don't need something more to get something more. That's what emergence means.” – Murray Gell-Mann

This is misleading, since you do need something more than the components of a system, you need the interactions between them.

Emergent properties only become deducible when the inter-component interactions are understood.

Emergent does not mean unwanted or unexpected.

The requirements for any designed system include its emergent properties.

E.g. The forward motion of bicycle and rider was wanted and expected by the bicycle designer.

And much else said about emergence is questionable.

“The concept has been used to justify all sorts of nonsense.” Gerald Marsh.

Relevance to EA?

EA takes a holistic view; it looks to integrate business systems to the benefit of the wider enterprise.

It grew out of the need to reduce the cost and quality issues caused by “silo systems”.

EA is concerned to couple subsystems to produce emergent properties to the benefit of the business.

Read Holism and emergent properties for more discussion.

Hierarchy

With his background in biology, von Bertalanffy wrote of a concept he called organicism.

Organicism: the idea that systems are describable at multiple hierarchical levels.

E.g.

A body <is composed from> organs <that interact in processes to sustain> the body.

An organ <is composed from> cells <that interact in processes to sustain> the organ.

A cell <is composed from> organelles <that interact in processes to sustain> the cell.

Hierarchy: the successive decomposition of a system into smaller subsystems or a behaviour into shorter behaviors.

Any actor or component that performs activities within a system can be seen as a subsystem, and described as a system in its own right.

After a first (top level) division into subsystems, each subsystem may be decomposed - recursively - several times.

A system may be decomposable into subsystems, and/or composable (with others) into larger systems.

At every level, a system has cross-boundary input/output flows.

So, an event that is external to a smaller system is internal to a larger system

And the emergent properties of a small system are ordinary properties of any larger system it is a part of.

The hierarchy above is simple physical containment hierarchy

A system can be divided differently into parallel systems.

E.g. biologists see the body in terms of circulatory, respiratory, digestive, excretory, nervous, endocrine, immune, muscle and reproductive systems.

Relevance to EA?

Biologists like Bertalanffy presume all parts are necessary to maintain and advance the whole.

Evolutionary pressures shape the form and functions of an animal so they cooperate with minimal waste and inefficiency.

By contrast, many large businesses suffer the “diseconomies of scale”.

EA is said to view the enterprise as a system, or system of systems.

This does explain what EA strives for, though integration of all systems is impossible in practice.

An enterprise is not so much systems of systems as containers of systems, each relevant to a “bounded context.

Hierarchical structures are used extensively to help people understand and manage complex estates of systems.

Atomicity or “non-reducability”

Ultimately, every physical entity is reducible to sub-atomic particles; and every emotion is reducible to biochemical reactions.

At the bottom level of description (of interest to physicists and bio-chemists) everything we see and feel is well-nigh infinitely complex.

Does that mean every physical entity is a complex system?

E.g.

A bicycle + rider system can be described as a simple system composed of two coarse-grained components.

That same system is infinitely complex if you decompose those two components down to the level of subatomic particles.

The lesson here is that complexity is matter of perspective.

It depends on a) what your interest in a thing is and b) what you regard at the atomic components.

So, to compare the complexity of two systems, they must be described a) in the same style and b) at the same level of decomposition.

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

System describers choose how far to subdivide a system of interest.

E.g.

System	Atomic actors (active structures)	Atomic activities (behaviors)
Astronomy / The solar system	sun and planets	orbits
Biology / organism	organs	organic processes
Biology / cell	cell	a chemical or signal exchange
Biology / beehive	honey bee	deliver pollen, perform and observe wiggle dances
Biology / predator-prey system	wolves and sheep	eat sheep, eat grass
Economics / economy	trader (customer and/or supplier)	a trade or transaction
Sociology / society	person	a communication act
Sociology / symphony performance	orchestra player	musical notes
Sociology / business	employee	a one-person-one-place-one-time activity
Software / application	a module or object	an operation

In each case above, an atomic actor may be a complex entity, and some may play roles other systems.

What is the atomic or irreducible element?

Obviously, we cannot understand or explain society or business at the bottommost level of description (of interest to physicists and bio-chemists).

We explain these systems at much higher level of abstraction - with reference to actors and activities we regard as atomic, irreducible, elements.

Similarly, the complexity of communications, between social or software entities, depends on how far you unravel the levels of the communication stack.

The atomic activities in communication stack are units of micro-scale physical matter/energy.

E.g. Vibration patterns in sound waves, and electron movements in electrical circuits.

Relevance to EA?

The atomicity of the system elements is debatable.

People can be seen as atomic actors.

Data entities or data items can be seen as atomic data elements.

The extent to which processes and software systems are decomposed varies.

Dynamics (state change)

In cybernetics: “a system is any set of variables which he [the observer] selects” Ashby 1956.

A principle of system theory is that a system has a current state.

A system can be analyzed in terms of how its state variables change over time, often in response to inputs from outside the system.

Even the simplest system, say a pendulum or a thermostat, changes state dynamically.

A system can be homeostatic or not

Since the 19^th century, many authors have been particularly interested in homeostatic systems.

E.g. In “Design for a Brain” (1952), Ashby, discussed biological organisms as homeostatic systems.

He presented the brain as a regulator that maintains each of a body’s state variables in the range suited to life.

This table distils the general idea.

Generic system

Ashby’s design for a brain

Actors

interact in orderly activities to

maintain system state and/or

consume/deliver inputs/outputs

from/to the wider environment.

Brain cells

interact in processes to

maintain body state variables by

receiving/sending information

from/to bodily organs/sensors/motors.

However, homeostatic entities and processes are only a subset of systems in general.

In his more general work, “Introduction to Cybernetics” (1956), Ashby defined a system as a set of regular or repeatable behaviors.

Cybernetics is the science of how a system (be it biological or mechanical) can be controlled.

It addresses how a control system (via an input-output feedback loop) can control at least some activities in a target system.

System state: the current structure or variable values of a system, which change over time.

A concrete system’s property values realise property types or variables in its abstract system description.

E.g.

	Properties	Examples
Abstract description of system state	Variable types	Air temperature. Displayed colour.
Concrete realization of system state	Variable values	Air temperature = 80 degrees. Displayed colour = red

System state change: a change to the state of a system, which may be of several kinds.

· Update – progressively updating state variables to reflect state changes in the wider environment.

· Homeostatic adaptation – responding to events by maintaining internal state variables in acceptable ranges.

· Autopoiesis – the self-sustaining processes by which an organism maintains its structures from simple chemical inputs

· Decay – losing the ability to act according to a previously-given system description.

Any system may change state at different rates at different times.

It may stay in a stable state for a while, then be triggered by an input to move to an unstable state, or to a different stable state.

Stable states are sometimes called “attractors”.

Relevance to EA?

EA is much concerned with the data that records the state of things in a business and its environment.

And with the business roles and processes that create and update that data.

(Hoare logic underpins all methods for analysis of requirements and definition of business processes.

It describes how performing a process changes the state of a system.

The Hoare triple may be expressed as: {Precondition} Process {Post condition}.

The meaning of the triple is: IF the precondition is true AND the process proceeds to completion THEN the post condition will be true.

The post condition is a requirement; a result, goal or outcome of value to the business.)

Dynamics (processes)

“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.

A system is characterised by what does more than what it is made of.

What makes the solar system a system is not the substance of the planets, it is the regularity of the orbits they repeatedly perform.

What makes a game of poker a system is not the personalities of the players, it is the rules they follow in their roles as poker players.

A system is defined by its actions more than by its actors, since any actor can be replaced by another actor performing the same activities.

It is defined by some behavior(s) that are modelled with some particular interest in mind.

Behavior: a service or process that changes the state of a system or something in its environment.

Event: a discrete input that triggers a process.

Process: a sequence of activities that changes or reports a system’s state, or the logic that controls the sequence.

A process may be 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 that shows the control logic governing event-triggered activities that result in discrete state changes.

E.g. mathematics that describes continuous change in the position of a planet in its orbit.

E.g. this table shows a process that runs from top to bottom and left to right.

A simple end to end behavior
Customer activities	Supplier activities
Place order	Send invoice
Send payment	Send receipt

A system is describable in terms of roles and rules for actors and their activities.

The actors are structures or components that interact by performing the activities expected of their roles.

The activities are behaviors that change the state of the system or something in its environment.

E.g. consider our solar system; the actors are a star and several planets identified by astronomers.

The activities are planetary orbits that can be described, and shown by testing to match that description.

Relevance to EA?

The US government’s EA Framework declared its aims as: “common data and business processes”, “information sharing” and “new and improved processes”.

“An EA establishes the Agency-wide roadmap to achieve an Agency’s mission through optimal performance of its core business processes within an efficient IT environment.” (FEAF, 1999).

In the popular book “EA as Strategy”, Ross, Weill and Robertson said.

"Companies excel because they've [decided] which processes they must execute well, and have implemented the IT systems to digitise those processes." (“EA as Strategy” 2006).

In short, the purpose of EA has always been to optimise, integrate, improve and extend business processes – which produce results or outputs of value to some interested parties.

Behaviors can be specified in the form of “value streams”, “business scenarios”, “business processes” and “service contracts”.

Determinism (state/history-dependent processing)

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

A general principle is that processes – when changing the state of a system - are influenced by its current state.

And so, the current state is a result of all past processes.

E.g.

The current state of	Is a result of
The moon’s surface	past asteroid strikes, over millennia.
A human actor’s memory	past perceptions and thought processes, over a life time
A computer actor’s memory	past input message and computations.

Every decision and action that depends on the current state of a system is – indirectly - dependent on its past history.

An information system that does not maintain current state information can derive it from a log of past events (this is called hysteresis).

Deterministic system: a system that processes an input, with respect to its memory/state, to produce a result that is predictable.

Stochastic system: a system that processes an input, with respect to its memory/state, to produce a result that (due to some randomness) is predictable only with a degree of probability.

Ashby wrote in 1956 that the notion of a deterministic system was already more than century old.

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

E.g.

Domain	Actors	Behave	In response to inputs	And the current state of
Biology	Cells	act appropriately	electro/chemical inputs	their current electro/chemical state
Control systems	Controllers	direct controlled devices	messages received	controlled devices or environment variables
Discrete event models	Entities	change state	events	the attributes of entities
System dynamics	Stocks	increase or decrease	inter stock flows	stock quantities or population volumes
Sociology	People	act and communicate	messages received	their memories or “mental images” (Boulding 1956).

The response of a system to a one event may be to

· complete a process, simple or complex

· choose one of pre-determined actions using rules that make the response to predictable

· choose one of pre-determined actions using rules that are probabilistic or random – which makes responses predictable only statistically.

Relevance to EA?

EA is concerned with business processes that are regular, determinate or repeatable (as Ashby said of cybernetics in 1956).

Determinate means the responses of a business (to events and service requests) are determined by business rules applied to system state or memory.

To monitor and direct the state of the entities and activities it cares about, a business runs in a feedback loop with its environment.

1. It receives a message carrying new information about business entities and activities

2. It refers to its memory, holding the last-recorded state of those entities and activities

3. Depending on the state, it “chooses” whether to update its memory and/or send messages to direct external entities and processes.

Chaos

Chaotic: 1) disorderly, random, with no regular or repeated pattern. 2) unpredictable outcomes arising from variations in initial conditions. 3) non-linear state change trajectory.

Chaotic generally means disorderly, random, with no regular or repeated pattern.

Confusingly, some people equate chaos with complexity.

Counter-intuitive to some, complexity appears when order emerges from chaos.

E.g.

The complexity of a	Composed of	Emerges from orderly patterns imposed on
Scale-free network	Nodes	a chaotic random network with the same nodes.
Biological cell	Molecules	a chaotic soup of the same chemicals/molecules.
Social network	Actors	chaotic ad hoc interactions of the same actors

Chaos has two more specific meanings related to unpredictability and non-linearity.

Chaos 2 - unpredictable outcomes arising from variations in initial conditions

A simple system can change state in an unpredictable way.

When you turn on a tap, the stream of water may start by running in a smooth way.

As you continue to turn the tap, its state may switch from smooth to confused, and back again.

A system can be predictable in the short term but not the long term.

A deterministic system responds to external events and internal condition changes in an orderly way.

The rules mean you can predict what will happen when the next event or condition occurs.

Or given stochastic rules, you can predict what will most likely happen.

However, this does not mean you can predict the longer-term trajectory of system state changes.

Forrester’s System Dynamics taught us that even simple systems can be unpredictable in that way.

“Chaos: When the present determines the future, but the approximate present does not approximately determine the future.” –– Edward Lorenz

Chaos theory applies to deterministic systems in which the future is predictable from the initial conditions of the system.

In practice, prediction (for example of the weather) is often frustrated by two obstacles:

· small differences between initial conditions can lead to dramatically different futures

· the observations and computations needed are beyond what can practically be made

Chaos 3 - non-linear state change trajectory

Linear system state change has straight line trajectory - directly proportional to time, or to an input event stream.

Non-linear state change has a curved, jagged or chaotic trajectory.

Non-linear state change can be the outcome of a simple system with simple rules.

E.g.

A simple predator-prey system can have a chaotically non-linear state change trajectory.

A system composed of wolves and sheep interacting according to simple rules can change state chaotically.

The behavior of an individual actor (e.g. a wolf) in response to an event may be deterministic and predictable from its current state.

Yet at a macro level, the volumes of populations (wolf packs and sheep flocks) can fluctuate in what seems a random or chaotic manner.

Populations may remain stable for a while, then boom or bust.

Relevance to EA?

Some speak of disorderly situations as complex; consider for example a war zone

Surely disorder is chaotic rather than complex?

EA is about orderly processes, not chaotic ones.

It is rarely concerned with the long-term trajectory of state changes to business state variables.

E.g. the value of a revenue-per-month variable might be stable, increase steadily or change chaotically.

Unpredictability

The Plexus Institute glossary says:

"complexity is found in systems when there are unpredictable interactions of multiple participants and components across many levels of the system."

(The glossary contains no definition of "system", or what the "levels" of a system are.)

The definition equates unpredictability with complexity.

However, as indicated above, the next action or state of a simple system may be unpredictable because

· its current state is unknown

· its rules include a random or probabilistic choice between actions.

· interactions between actors at a micro-level lead to unpredictable state change effects at the macro-level.

And in the simplest human social system, how an actor responds to information received is unpredictable.

Because humans, having free will, can choose their response – either within the bounds of a system, or contrary to the rules of that system.

Exception: what happens when actors do not complete actions expected of their roles.

This is common in human activity systems, and happens also in mechanical systems when their components fail.

Relevance to EA?

EA is concerned to address exceptions to the main, happy, or straight-thru path of a business process.

Especially in processes in which human actors play roles.

The need to design systems with exception handling is a common source of complexity.

Adaptation

Adaptation: 1) system state change. 2) system mutation.

System state change

A system can adapt to change in its environment by changing state.

E.g. consider a thermostat-controlled heating system.

This simple homeostatic system can withstand perturbations and restore its original state after a large perturbation.

The term adaptation is used with various meanings, including

· Homeostatic adaptation through state restoration

· Psychological adaptation through biological development and learning from experience

· Sociological adaptation through social communication and learnng from education

· Species evolution through genetic variation and natural selection.

Relevance to EA?

EA is much about business roles and processes that create and maintain persistent state data.

It is much concerned with the data that records the state of things in a business and its environment.

System mutation

"Von Bertalanffy.emphasized that real systems… can acquire qualitatively new properties through emergence, resulting in continual evolution.” Principia Cybernetica Web

This is perhaps Bertalanffy’s most questionable idea, because systems continual evolution implies a system that can never be described or tested.

Evolution: a progression of inter-generational system mutations that change the nature of a system Evolution can be organic (a virus) or designed (a software upgrade).

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

It is generally helpful to distinguish state change within a generation from mutation between generations.

However, beware that long-lived coarse-grained systems often depend on shorter-lived subsystems.

And to maintain the state of a long-lived coarse-grained system may require mutations at the level of its atomic actors.

E.g. consider a termite colony as a system that must restore the state of its mound after damage.

No mutation is needed, since termites are innately pre-programmed to make this homeostatic state change.

E.g. consider a football team as a system of players that aims to win football matches.

Player mutations (when the manager replaces one player by another) are necessary to maintain the state of the whole team.

E.g. consider your immune system as a colony of cells that must recognise and dispose of pathogens.

The immune system doesn’t know what invaders it might meet, so it makes millions of different cells, each to recognise a different pattern.

Cell mutations are necessary to maintain the healthy state of the whole organism

E.g. consider the biosphere as a system of organisms that recovers from mass extinctions over millions or billions of years

Organism mutations are necessary to maintain the state of the whole biosphere.

The evolution of biological organisms depends on chance mutations proving beneficial to survival.

The evolution of business organisations depends primarily on designed mutations proving beneficial to customers.

Evolution favours whatever helps organisms and organisations perform better than rivals in competition for limited resources.

Typically increasing efficiency can involve simplification, and increasing effectiveness can involve complexification.

A biological species evolves when individual organisms are replaced by new ones better fitted to their roles.

“Since offspring tend to vary slightly from their parents, mutations which make an organism better adapted to its environment will be encouraged and developed by the pressures of natural selection, leading to the evolution of new species [eventually] differing widely from one another and from their common ancestors.” http://www.oxforddictionaries.com/definition/english

A football team evolves when individual members are replaced by members better fitted to their roles.

A business evolves when individual business systems are replaced by ones better able to support and enable the business.

It may help to set out three change principles:

1) The state of an activity system may change.

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

3) Changing the roles, rules or variables makes a new system version, or a new system.

Relevance to EA?

EA is very much concerned with system mutations made under change control.

It designs, plans and governs the migration from baseline systems to target systems.

See the next section.

Self-organisation

Some system theorists have said the concept of a self-organising system makes no sense.

However, the term is widely used, and has been interpreted in an extraordinarily wide variety of ways.

Self-organisation = absence of a design or pattern?

This means there is no law, rule or definition of how a system forms or changes.

However, one may say there is a blueprint for much so-called “self-organisation”.

The blueprint for self-organisation in a solar system is found in the laws of physics

The blueprint for self-organisation in a molecule is found in the chemists’ periodic table

The blueprint for self-organisation in a biological organism is found in its DNA.

The blueprint for self-organisation in a social system is found in the minds or documents of its actors.

In the first three examples, the self-organisation is predetermined and predictable in theory if not in practice.

Self-organisation = decentralised control?

Decentralisation means there is no central control - no overarching controller of system processes.

Rather, the processes of the system are distributed between atomic components or agents.

In sociology: this may be called anarchy or a participatory democracy (apparently the vision of many social systems thinkers).

In computing: this corresponds to the “choreography” design pattern rather than the “orchestration” pattern.

The latter is found in very simple software systems; does not imply “self-organisation”.

Self-organisation = growth by accretion?

Accretion means growth or increase by the gradual accumulation of additional layers or matter.

The accretion of a crystal growing in a super-saturated liquid is a simple process that has been called self-organisation.

The accretion of a city growing as it attracts more people and money has also been called self-organisation.

But neither is what people usually think of by “self-organisation”.

Self-organisation = flocking behavior?

Flocking means to move or go together in a crowd.

The shoaling behavior of fish has been called “self-organisation”.

The behavior of a flock of starlings wheeling in the sky has also been called “self-organisation”.

In both examples, many simple interactions between many adjacent actors produces a complicated moving shape.

However, the complexity of these state change (visual) effects is more in the eye of the observer than in the system itself.

Self-organisation = morphogenesis?

By any measure, the morphogenesis of an organism is a complex process.

The process, predetermined by DNA, inexorably builds an adult organism from an egg.

As the process proceeds, new kinds of component and interaction emerge, increasing the complexity of the organism.

Self-organisation = business reorganisation?

The morphogenesis of a biological organism leads inexorably to pre-determined outcomes.

The morphogenesis of business organisation is far from inexorable or pre-determined.

Organisation changes are stimulated by a variety of internal and external forces

Ultimately, external forces (political, economic, social, legislative and environmental) dominate the internal ones.

Self-organisation = the application of learning?

Some social systems thinkers (after Peter Senge) speak of a “learning organisation”.

Discussion of that is out of scope here.

Relevance to EA?

Ashby and Maturana have said the concept of a self-organising system makes no sense.

They rejected the idea that a system can change itself by creating new variables or rules.

They said a system can only be re-organised from outside the system, by a higher process or meta system.

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

E.g. in nature, the processes of sexual reproduction define a new organic system generation.

E.g. in business, enterprise architects design ad plan new business system generations.

EA is not about processes actors that are ad hoc, or performed without regard to business data.

EA is about regular business roles and processes that create and use business data.

It is a meta system to those business systems that it observes and envisages.

It helps an enterprise to change those business systems in response to external and internal forces.

Goal seeking

“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.” Bertalanffy

The notions of goal seeking and goal directedness mask a confusing variety of ideas.

Ashby presumed goals were a given.

Throughout this book it is assumed that outside considerations have already determined what is to be the goal, i.e. what are the acceptable states.

Our concern, is solely with the problem of how to achieve the goal in spite of disturbances and difficulties. ” Ashby 1956

Some use the phrase “Purpose Of System Is What It Does” as a way of thinking and approaching systems.

Meadows equated the results of behaviors with their goals.

However, to define goals in retrospect rather than in advance is to confuse goals with outcomes.

Remember the distinction between natural and designed (accidental and purposive) systems.

A designed system may be described in terms of aims (motivations), activities (behaviors), actors and passive objects or structures.

E.g. a country’s world cup campaign can be described thus.

System concepts	Example	Meaning
Aims (motivations)	win the world cup	target outcomes that give an entity a reason or logic to perform and choose between actions.
Activities (behaviors)	compete in world cup matches	behaviors or processes than run over time with intermediate outcomes and a final aim or ideal.
Actors (active structures)	players in a national football team	entities that interact by perform activities in system behaviors.
Passive structures	pitches, footballs	objects acted upon during behaviors.

At the end of the campaign, its outcomes can be tested against the declared goals.

Ackoff spoke of purposive (actors defined activities to meet given goals) and purposeful systems (actors define goals as well as activities).

Which is to confuse social networks with social systems – see the next section.

Relevance to EA?

EA is concerned with designing and changing systems to meet business goals.

Complexity

“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.” Bertalanffy

Complex is a term with scores of different definitions.

A system that is complex in one way may be simple in another.

It may be complex internally and/or appear complex externally

Internally, there may be a wide variety in its variables, its roles or its rules.

Externally, there may be complex convolutions in the observable trajectories of state variable changes.

Read Complexity for more detailed discussion of the topic.

Relevance to EA?

EA is concerned with software systems, arguably the most complex system designed by mankind.

And with roles played by human actors, who are arguably the most complex systems in nature.

And with business organisations that are very complex entities, if not systems.

On social network and social systems

This section is an exception from the others in this paper.

It discusses a distinction not drawn in the early development of general system theory and cybernetics.

A principle of system theory is that all the elements of a system are related directly or indirectly.

Else there would be two or more distinct systems.

This principle can be applied to networks as well as systems.

Defining the network that connects components

Each component may interact with one, a few or many other components.

Wherever two components interact, a structural relationship may be drawn between them.

Connecting all the components by these relationships reveals a communication structure, sometimes a hierarchy, but more commonly a network.

To assess the complexity of this network structure, you must know its nodes and their inter-connecting relationships.

Two ideas about network structures

Real world networks have been studied extensively.

E.g. the world-wide-web, the internet, energy landscapes, biological (cell-to-cell and protein-to-protein) networks and social networks.

Some have proposed such diverse networks can “self-organize” themselves.

Small world network

One idea is that as a network grows, it tends to minimises the number of steps from one node to another.

Mathematically speaking, in such a “small-world'' network, the number of steps grows in proportion to the logarithm of the number of nodes.

Scale-free network

Another idea is that as a network grows, the number of major “hubs” with many connections increases, along with minor hubs between the major hubs.

The complexity of a scale-free network lies in the orderly patterns it imposes on the chaos of a random network with the same number of nodes.

These ideas may be useful to designers of large systems in which actors or subsystems must communicate.

However, it turns out that scale-free networks are rare in nature and society.

“The universality of scale-free networks remains controversial.

Across [nearly 1,000] networks, we find robust evidence that strongly scale-free structure is empirically rare.

Furthermore, social networks are at best weakly scale free, while a handful of technological and biological networks appear strongly scale free.” Scale-free networks are rare Nature, 2019

The system of interest is not simply a network structure (discussed above), it is an activity system.

In accord with the “primacy of behavior” principle, it is defined by its behaviors or dynamics.

The system is composed of interactions between its constituent components.

Abstracting system interactions from network relationships

Any two components, connected by a relationship in network structure, may interact in several different processes.

So, the complexity of a system in which components interact is a different from the complexity of the network structure.

A complex network of connections (or acquaintances) may be used for simple interactions.

Conversely, a simple network of connections may be used for complex interactions.

Note, moreover, that actors in one communications network may interact in several different (complementary, cooperating, competing or contrary) systems.

E.g. the people in one city may vote in both local and national government elections - and vote for different parties in each.

Abstracting system interactions from the communication stack

Every communication act between human or computer actors depends on the use of communication stack between them.

When co-located humans converse, they use neurons > vocal chords > sound waves > ear drums > neurons.

When remotely distributed humans communicate via a telecommunications network, the lower levels of the communication stack are hidden from them.

The depth and complexity of the tele-communication stack is irrelevant to most systems of interest that actors cooperate in.

Social networks and social systems are different things

General system theory and cybernetics can be applied to the roles and rules of a social system.

Note however that a social network (a collection of communicating actors) is a different concept.

Social network: a group of actors who communicate with each other directly or indirectly.

Social system: a system that is realised by a social network.

One social network can realise several distinct social systems.

And one social system can be realised by several social networks.

Relevance to EA?

EA is concerned with businesses that connect their human employees in a social network.

EA works alongside others concerned with the management and motivation of those actors.

Much social systems thinking is specific to human situations and sociology.

Read EA and social systems thinking for discussion of social systems thinking.

Footnotes: two principles

General principle: a concrete entity is a system only when and in so far as it realises an abstract system description

As many system theorists have told us, a system is a way of looking at the world, or an entity in it.

The scope and content of the system is subjective - a choice made by one or more system describers.

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

Not only can one concrete entity realise several defined systems.

But one defined system may be realised by several concrete entities.

In too many “systems thinking” discussions, people merely point to a named entity (say IBM) and call it a system.

With no further description, that is vacuous to the point of being meaningless.

By looking at IBM in different ways you can find infinite different (even conflicting) systems.

The network of actors that are IBM employees may act in many different systems.

Some are complex, others simple; some are adaptable, others inflexible

Some are purely social, others socio-technical; some are cooperative, others in conflict.

Consider the performance of a symphony

The example may help to illustrate the ideas here.

An abstract or 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).

A concrete or 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.

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.

Consider the US government

A US government is infinitely more than its realisation of any abstract system description.

It is an ever changing entity in which stuff happens.

A US government can manifest, instantiate or realise several abstract system descriptions, successively or in parallel.

Different people will define the goals, inputs and outputs of the government differently, each expressing their own perspective of “the whole.”

So to say the US government is a system is meaningless unless you point to your chosen abstract description.

Indeed, every US government is testable as realising the abstract system description known as the US constitution.

US government

US constitution

US founding fathers <envisaged> US governments

The US constitution defines the roles of 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 most of what the federal government does day to day, or the roles or rules of its subordinate institutions.

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

Consider applying system theory as a science

An instance is something that exhibits (near enough to satisfy the observer) the properties of a type.

A scientific truth is what is measured (near enough to satisfy the observer) as matching what is predicted by a theory.

A concrete system behaves (near enough to satisfy the observer) as described in an abstract system description.

Viewpoint	Subject	Verb	Objects
Philosophy and maths	A type	helps people understand and describe	instances
Scientific method	A theory	helps people understand and predict	realities
System theory	An abstract system	helps people understand and make	concrete systems

In effect, to apply system theory is to apply the scientific method to a description of the world.

Applying system theory means forming abstract system description (a type or theory).

And testing that the behavior of a concrete system instantiates or realises that type.

Abstract system description	Concrete system realisation
Type	An instance
Theoretical system	An empirical system
General roles and rules	Some particular actors and activities
“Solar system” definition	Some planets orbiting a star
Laws of tennis	A tennis match
The score of a symphony	A performance of that symphony
The US constitution	A US government

There can be several performances of a role, as there can be several concrete realisations of an abstract system description.

An actor is infinitely more than their performance of one role, as concrete entity is infinitely more than its realisation of one abstract system description.

An actor can perform several roles, successively or in parallel, as a concrete entity can realise several abstract system descriptions, successively or in parallel.

Moreover

Strictly speaking, we ought to distinguish three concepts.

Abstract social system	A set of roles and rules (the logic or laws actors follow)
Concrete social system	Actors playing the roles and acting according to the rules
Social network	Actors who inter-communicate and act as they choose

By the way, a social cell is a social network in which actors find that realising the roles and rules of a particular social system is so attractive they resist any change to it.

Today, much written by social systems thinkers (e.g. Luhmann) has little to do with systems in the general system theory sense.

Their use of terms like "system", "autopoiesis" and "emergent properties" often appears pseudo-scientific.

Peter Senge explicitly promoted systems thinking as an alternative to general system theory.

Today, some don’t acknowledge there is a schism; others belittle general system theory, using terms like “engineering”, “linear” and “clockwork machine”.

Still, all modern society depends on large and complex engineered systems, which sometimes behave in magical or mysterious ways.

Q&A

The universe is an ever-changing entity in which stuff happens.

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

What if it stops doing that?

When an entity stops exhibiting the behaviors of a system, the system no longer exists.

E.g. When the organs of tiger stop working, the tiger dies.

When planets fall out of their orbits, there will be no solar system.

When the players in a tennis match go home, the tennis match is over.

What remains when an activity system stops running?

An abstract system description may persist long after the concrete system has disappeared.

But what remains of the concrete system?

In some cases, the system rests in the form of a structure containing parts that can be started up again.

E.g. An airplane’s mechanical parts are dedicated to their role in flying the plane

When an airplane rests in a hangar the parts stop playing their roles.

But they remain contained within the airplane’s carcass, and may resume their roles the next day.

When a bank closes for the night, its employees stop playing roles in it.

Overnight, those employees play other roles, in families, in bars, in part time jobs as football coaches.

Those people are not “parts” of the bank system in the sense they are dedicated to the bank, or the bank “contained” them.

Rather, they are actors hired to play roles in the bank system.

The “part” they play in the system is limited to the activities expected of those roles.

Outside of their roles, the participants in the bank are better viewed as a social network rather than as a system.

General principle: realisation differs from translation

Here, idealisation or conceptualisation means abstracting a description from a reality that is observed or envisaged.

Whereas translation means transforming one description of a reality into another description of the same reality.

Realisation = the operation of a concrete system that is testable against a system description
The US constitution is a document that conceptualises the structures and behaviors of the system that is a US government.

This public document was agreed by its authors and is understandable by all who share the authors' understanding of the words in it.

It has been realised in concrete / operational / run-time systems throughout the history of the US by successive government bodies.

Translation = transformation of one system description into another (more or less refined) description

The US constitution authors translated and collated their mental models into a documented model.
Readers of the published document translate it into their private mental models.

All documented and mental models are abstract system descriptions.

Obviously, documented models are more stable and shareable, which is why they are created.

Models are documented so that the behavior of real-world systems can be tested against the models.

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