Copyright 2017 Graham Berrisford. One of about 300 papers at http://avancier.website. Last updated 23/02/2018 00:28
The role of enterprise architects is to observe baseline systems, envisage target systems, and describe both.
So, you might assume it is universally agreed what a "system" is; but this is far from the case.
The work introduced here sets out to provide a stronger theoretical foundation for enterprise architecture.
It turns out that, to provide that foundation, we have to explore wider questions and theories:
· what it means to call something (a hurricane, a human being, a society, a business, a radio) a system
· how "general system theory" and "systems thinking" differ, and can sometimes be contrary to each other
Later papers analyse the notions of system theory with reference to:
· theories of description, types, communication and information
· philosophical questions about description and reality - how they differ and relate.
The analysis leads towards a coherent and consistent understanding of these matters.
The findings and conclusions can help both authors and users of enterprise architecture standards.
They can help readers detect and resolve ambiguities and contradictions in "systems thinking".
They could help scientists in different disciplines to use the term "system" more consistently.
Moreover, they may provide food for thought for philosophers about the description/reality distinction.
Systems can be found in many domains of knowledge, in many different ’ologies.
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.
The solar system features well-known actors; the sun and the planets.
The planets repeat orbits that can be described, tested, and shown to match their description.
The entity is orderly and well-called a system, defined by its structures and their behaviors
An animal can be seen as a realisation of the system defined in its DNA.
We see that system as a collection of subsystems that interact in processes.
E.g. there are circulatory, respiratory, digestive, excretory, nervous, endocrine, immune, muscle and reproductive systems.
The whole is a system of systems (though the last above is not needed for the sustenance or survival of the whole).
A social entity is a collection of actors who exchange information.
The actors interact according to information in messages received and memories retained.
The information can include descriptions, directions and decisions - and requests for them.
Here, a social system is a social entity in which actors interact in regular or repeatable behaviors.
In a hunter-gatherer society, the roles and rules of a hunting party might be describable as a social system.
Human societies have evolved much more complex legal, religious and political systems.
Sociologists debate the best design for a political system.
Centralised totalitarianism, a participatory democracy, or anarchy?
Business systems have evolved over millennia by formalising social systems.
They formalise actors’ roles by listing activities expected of them.
A simple business system
A shoe factory contains people and machines that interact in processes to achieve business goals.
A shoe factory can be seen as “black box” whose primary function is to consume input supplies and deliver output products.
Within the factory, actors act to transform the inputs into outputs in a regular and repeatable way.
Actors may come and go, but their roles remain; the entity is orderly and well-called a system.
Business architects debate the best design for a business system.
A top-down management hierarchy, distribution of control to local managers, or delegation to small teams?
The “Information Age” arrived when humans started to digitise business systems using software.
As described by a software architect, a software system is an abstract system description.
It is composed of components, modules or objects that communicate and perform other activities according to messages received and memories retained.
Software architects debate the best design: centralisation of control (orchestration) or distribution of control (choreography)?
Natural 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.
Designed systems are often described in terms of aims (motivations), behaviors (activities) and structures (actors and obiects).
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.
Players in a national football team
Nodes (related in a hierarchical or network structure) that perform activities in behaviors.
Objects acted upon during behaviors.
Atomic system elements
System structures and behaviors are composable and decomposable.
An atomic element is an element that is not further divided in a description.
E.g. An organ in the human body, a human in a society, a note in a musical score.
Or any one-person-one-place-one time activity in a human activity system
Atomic system actors may be complex entities in their own right, and may play roles in other systems.
There are general archetypes or design patterns for how system elements interact.
The table below identifies some contrasting design patterns.
Designers choose between alternative patterns by trading off their pros and cons on the light of given requirements.
Centralisation of control
Distribution of control
in one place or component.
between places or components.
Anarchy or Network
Hub and Spoke
Point-to-Point or Mesh
Fork or Orchestration
Chain or Choreography
The first three are structural patterns, the last is a behavioral pattern.
Shorter processes take place within components; longer processes connect and coordinate components.
A cross-component process works either by orchestrating components, or choreography between components.
To some, the term "system" means no more than "an entity that contains things interrelated in some way or another".
This definition applies to passive structures, like a garden fence, a telephone directory, a necklace, or the Dewey Decimal System.
However, most general system theorists use the term in a richer, more demonstrably useful way.
They focus on activity systems in which structures/parts interact in behaviors/processes.
Ludwig von Bertalanffy (1901-1972) was a biologist who promoted the idea of a general system theory in the middle of the 20th century.
His aim was to discover patterns and elucidate principles common to systems in every discipline, at every level of nesting.
He looked for concepts and principles applicable broadly, rather than to one discipline or domain of knowledge.
Bertalanffy wrote of concepts such as organicism. holism and emergent properties.
He related system theory to communication of information between the parts of a system and across its boundary.
“Systems concepts include: system-environment boundary, input, output, process, state….” Principia Cybernetica
Early system theorists were especially interest self-regulating systems such as the physiological systems of the body.
A homeostatic system (be it natural or designed) maintains itself in a stable or viable state through input/output feedback loops.
By contrast, end-to-end behaviors run from start to end, yielding a result or output.
In business systems, these behaviors are usually called value streams, scenarios or processes.
The trigger that starts an activity (or sometimes the activity itself) is often called an 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.
Abstract and concrete systems
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.
Theorists like 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.
Like every other discrete entity, an activity system has a discrete life time, which can be long or short.
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.
For more, read the first paper under GENERAL SYSTEM THEORY on the "System Theory" page at avancier.website.
The culture of an enterprise has a huge impact on what systems can be changed or introduced at an operational level.
Culture also has a huge impact on the ability of enterprise architects to propose change in the first place.
Enterprise architects must be sensitive to cultures at both operational and strategic levels, and are influenced by them.
That does not mean that enterprise architects are employed to propose or design cultural change.
The social impacts of changes to activity systems are usually addressed in parallel, by a business change team.
The system theory presumed in enterprise architecture frameworks is a relatively scientific approach to describing systems.
It is done to describe operational systems in a way that can be tested and implemented.
Much social system thinking is different.
A social entity is a group of inter-communicating actors.
The actors interact by exchanging information (descriptions, directions, decisions and requests for them).
Primitive social entities (e.g. bees in a beehive) can be regarded as deterministic systems.
The actors play pre-defined roles and communicate pre-defined types of information.
Classical system theory can be applied to such an animate social entity.
Classical system theory, when applied to a human society, is sometimes called “structuralism”.
Structuralists analyse aspects of human society in terms of relationships between elements in a conceptual or theoretical system.
Science presumes we can test a real-world or empirical system against a theoretical system.
However, many systems thinkers are concerned with societies that are less systematic.
Their focus is on social groups in which the actors are self-aware and regarded as having free will.
If need be, human actors can behave in a deterministic way; and communicate using pre-defined types of information.
And a deterministic system can accommodate out-of-ordinary cases as “exception paths”.
However, humans also create new information, communicate it to others, and respond in ad hoc ways.
The question arises: if little or no behaviour of the group can be tested against a description, in what useful sense is it a system?
What does it mean to call a church, a government or IBM “a system”?
Does it mean merely that it contains things that are interrelated in some way or another, which may be said of the entire universe?
Does it mean more specifically that it is a social entity in which people receive some directions from above and communicate with each other?
System thinkers speak of social and business entities as complex adaptive systems.
Obviously, IBM is an entity in which countless systems may be found.
And at any one time, some of those systems will be changing.
So, one might glibly call IBM a complex adaptive system of systems.
How to measure IBM’s complexity, or its adaptations?
In what meaningful, testable or useful sense is IBM a system?
Like philosophers, socio-cultural essayists like comparing, contrasting and criticising past approaches.
They invent classifications and neologisms like “stratified open system”.
They borrow words from general system theory like “emergent properties” but use them differently.
They present classifications as though they are scientific theories and make grand-sounding propositions
“The role of theory in social science therefore is to interpret empirical phenomenon in terms of how observed events are the contingent outcomes of the interaction of unobservable processes.”
Meaning, we cannot see or define the processes of a system, or predict their outcomes.
So, all we can do is explain history in terms of a classification or theory we made up? As Marxists do?
In short, much systems thinking is the stuff of a socio-cultural essayist rather than a scientist.
For more, read the first paper under "SYSTEMS THINKING" on the "System Theory" page at avancier.website.
Classical system theory gives us insights into a wide variety of disciplines.
It leads towards theories of information and communication.
It leads towards answers to philosophical questions about description and reality.
And helps us to make sense of what enterprise architecture is about.
Enterprise architecture is about:
· Open (rather than closed) systems, which deliver services to consumers.
· Designed (rather than natural) systems, which need analysis and design effort.
· Purposive (rather than accidental) systems, which have measurable objectives.
· Information (rather than material) processing systems, which formalise messages and memories.
Formalisation of messages and memories
Enterprises are social entities in which actors exchange information.
Business activity systems can be seen as formalised social systems.
The actors perform activities according to messages received and memories retained.
The contents of memories and messages are defined as data structures composed of business data types.
A meta system is one that observes, envisages or describes the roles and rules of another system.
An enterprise architecture function is a meta system for changing an enterprise's business systems – under change control.
The social impacts of changes are usually addressed in parallel, by a business change team.
An enterprise architecture function takes a holistic view of business activities that create and use business data.
Architects take a cross-organisational and strategic view of the enterprise’s business systems.
They strive to standardise and integrate discrete business systems.
However, seeing the whole enterprise as one system is a vision rather than a reality.
Partly because different business functions/capabilities have their own domain-specific languages.
And partly because large businesses suffer the diseconomies of scale.
Generally, an enterprise is as many different activity systems as you are able to describe and test
Calling an enterprise a system without reference to a particular system description means next to nothing.
Not only can an enterprise be conceptualised as countless different systems.
But also, those systems may be nested, overlapping, disparate, cooperative or antagonistic.
Architects identify, design, plan and govern changes to business activity systems - under change control.
They describe a system in a way that can be used to test an operational system, and analyse the impact of changes.
They describe a system from several viewpoints:
· External and internal views
· Structural and behaviour views
· Logical and physical views.
For more, read the first paper under "ENTERPRISES AS SYSTEMS" on the "System Theory" page at avancier.website.
A system is a subdivision of the universe we can describe as a system.
A Darwinian explanation of description starts long before words.
It starts from the notion that organisms can recognise family resemblances between things.
And so recognise when a new thing is of a kind important to survival, or resembles a previously remembered thing.
Eventually, evolution led to humans, verbal language and our sophisticated creation and use of “types”.
We describe system structures and behaviors by typifying them.
For more, read the first paper under DESCRIPTION AND REALITY on the "System Theory" page at avancier.website.
Philosophers have looked at description and reality in many ways – both overlapping and contrary.
Many people’s instinct is to divide the universe into mental and physical worlds.
The view taken today in cognitive science and psychology is that the mind has a physical biological basis.
First, one has to shake off:
· the mental/physical dichotomy presumed in Cartesian Dualism (after Descartes)
· a human-centric view of the universe
· a language-centric view of philosophy.
Then acknowledge that:
· describers are actors who have some intelligence about their environment
· the ability of actors to describe the world is a side effect of biological evolution
· the first description was a biological model of some kind, and brains evolved to retain descriptive mental models of perceptions
· in natural intelligence, the mental world is physical – though the bio-chemistry of that is deeply mysterious
· in artificial intelligence, the most notable ability is the ability to abstract descriptive types from observations of similar things.
And finally, acknowledge that:
· descriptions include all signs, all models, all encodings of perceptions, private and public
· mental, spoken, written, audio, visual and physical models are all descriptions
· humans and their machines can translate a description of any kind into a description of another kind
· describers and descriptions are themselves part of reality - and can themselves be described (if need be).
The philosophy here can be expressed in a triangle.
The nub of our philosophy
Descriptions (private & public)
<create and use> <idealise>
Describers <observe & envisage> Realities
The process of envisaging reality starts in dream-like or consciously-directed brain activity.
Both processes result in the creation of descriptive/mental models.
Describers translate descriptions from private to public forms, and back again.
Today, there is little debate about the existence of material realities.
Most presume that there is physical matter/energy out there.
The questions to be answered are rather the ones listed below.
· What is the nature of description?
· What is the role of a describer?
· How to test things match their descriptions?
· How accurately do describers describe or measure things?
· What does it mean to exist?
For answers, read the papers under "PHILOSOPHY" on the "System Theory" page at avancier.website.
1906 Space-time continuum (Hermann Minkowski)
1948 “Cybernetics or Control and Communication in the Animal and the Machine” (Norbert Weiner)
1952 “Design for a Brain” (W Ross Ashby)
1956 “General Systems Theory” (von Bertalanffy)
1956 “Introduction to cybernetics” (W Ross Ashby)
1956 “General System Theory – The Skeleton of Science” Kenneth Boulding
1971-72 System Dynamics (Forrester; Meadows et al.)
1971 “System of System Concepts” (Russell Ackoff).
1972 Second order cybernetics (Bateson)
1972 Soft systems methodology (Peter Checkland)
1979 “Ecological System theory” (Bronfenbremner)
1997 “A Realist Theory Of Science” London: Verso. (Bhaskar, R.)
2001 “Luhman’s autopoietic social system” David Seidl
???? Charles Handy
2011 “The positive and the negative” Justin Cruickshank
2014 “Types and tokens” Stanford Encyclopedia of Philosophy
2018 “Splitting Chairs” in Philosophy Now, Jan 2018
All free-to-read materials on the http://avancier,web site are paid for out of income from Avancier’s training courses and methods licences.
If you find them helpful, please spread the word and link to the site in whichever social media you use.