Introducing systems thinkers and their ideas

And distinguishing activity systems from social entities

https://bit.ly/2w5XKNK

Copyright 2017 Graham Berrisford. One of several hundred articles at http://avancier.website. Last updated 15/07/2020 14:45

 

This article contains and analysis and critique of nearly two centuries of systems thinking.

It introduces two new ideas:

1)     In place of the classic semiotic triangle - the more instructive "epistemological triangle"

2)     In place of second order cybernetics - the notion of separating meta system M from system S – allowing one actor to play a role in each.

The second gives us a way to reconcile activity system theory with self-organization and social entity thinking.

                                                  

“A very interesting potted history of the evolution of social entity thinking and the different strands of thoughts that have evolved.”

“Thank you for sharing.”

“I cannot overstate the sense of clarity that I get whenever I read your articles. It is very much appreciated.”

 

Contents

Preface. 1

PART ONE: The beginnings of systems thinking. 1

The importance of organization, activity and abstraction. 1

A paradigm clash. 1

General system theory. 1

PART TWO: 20th century activity system theory. 1

System Dynamics. 1

“Thinking in systems”. 1

Cybernetics. 1

Soft Systems. 1

Conclusions to part two. 1

PART THREE: Ambiguities and distinctions. 1

Distinguishing activity systems from physical entities. 1

Distinguishing two kinds of activity system change. 1

Distinguishing two kinds of self-organization. 1

Conclusions to part three. 1

PART FOUR: More thoughts. 1

About complex systems. 1

About entities rather than systems. 1

About the sociological perspective. 1

About systems in enterprise architecture. 1

 

Preface

Enterprise architects may both stimulate and contribute to business planning.

But their primary responsibility is business system planning.

 

It has been said that “enterprise architecture views the enterprise as a system, or system of systems”.

In short, the systems of interest:

·       are activity systems; meaning they comprise actors performing regular activities to meet given aims

·       are designed: meaning their elements are ordered and related to achieve desired outcomes

·       may be integrated with other business systems by the exchange of information

·       may be changed under change control.

 

There is much more to know about systems; this article returns to enterprise architecture only at the very end.

PART ONE: The beginnings of systems thinking

 

Many ideas that prefigure systems thinking discussion today can be traced to earlier sources.

Notable authors included.

·       Adam Smith (1723 to 1790) specialization of and competition between enterprises.

·       Charles Darwin (1809 to 1882) system mutation by reproduction with modification.

·       Claude Bernard (1813 to 1878) homeostatic feedback loops.

·       Willard Gibbs (1839 – 1903) the development of chemistry into a science.

·       Vilfredo Pareto (1848 to 1923) the Pareto principle.

 

Gibbs defined a system as: “a portion of the ... universe which we choose to separate in thought from the rest of the universe.”

However, modern system theorists separate the concept of a system from Gibb’s discrete entity.

If every discernible thing, every nameable discrete entity, is a system, the term is useless.

 

Today, in short, a system is definable as a particular set of regular or repeatable interactions.

Most systems of interest here feature actors (or components) that interact in the performance of activities.

 

To paraphrase Meadows: "Is anything not a system? Yes, a passive structure, like the Dewey decimal system, or any other hierarchical or tabular structure.

Also, an informal social entity, a group of actors who do not interact in the particular ways that characterize a system."

 

Early social systems thinking

The first sociological thinkers included:

·       Herbert Spencer (1820 to 1903) social systems as organic systems.

·       Emile Durkheim (1858 to 1917) collective consciousness and culture.

·       Gabriel Tarde (1843 to 1904) social systems emerge from the actions of individual actors.

·       Max Weber (1864 to 1920) a bureaucratic model – hierarchy, roles and rules

·       Kurt Lewin (1890 to 1947) group dynamics.

·       Lawrence Joseph Henderson (1878 to 1942) meaning in communication

 

Some thinkers likened business organizations to biological organisms, and some presumed that social or business system is homeostatic.

Though these ideas have influenced systems thinkers for 150 years, they are at least somewhat misleading.

 

Read thinkers who foreshadowed system theory for ideas attributed to the thinkers listed in this section.

20th century activity system theory

When system theory became established as a topic in its own right is debatable.

Some suggest it is a branch of sociology.

“Systems theory, also called social systems theory... https://www.britannica.com/topic/systems-theory

Others suggest the reverse, that social system thinking is branch of general system theory.

 

The general concept of an activity system became a focus of attention after second world war.

And there was a burst of theoretical development in the period 1940 to 1980.

Influential bodies and groups have included these three.

 

1941 to 1960: The Macy Conferences - cross-disciplinary meetings in New York.

On Cybernetics, with a leaning to The Macy Foundation’s mandate to aid medical research.

Topics included connective tissues, metabolism, the blood, the liver and renal function.

Also, infancy, childhood, aging, nerve impulses, and consciousness.

 

1949 to 1958: The Ratio Club - a cross-disciplinary group in the UK.

It was founded by neurologist John Bates to discuss cybernetics.

Many members went on to become prominent scientists - neurobiologists, engineers, mathematicians and physicists.

Members included psychologists (Ashby) and mathematicians (Turing).

 

1955 to date: The International Society for the Systems Sciences (ISSS).

This was conceived in 1954 by Bertalanffy, Rappaport and Boulding.

 

Read part two below for ideas attributed to the thinkers listed above.

First, we should explore a few ideas that will prove important in the later discussion,

The importance of order, activity and abstraction

A system theory (as defined by Bertalanffy, Ashby, Forrester, Checkland or other) must identify the features that characterize a system.

In the 19th century Gibbs spoke of “a portion of the universe, which we separate in thought from the rest of the universe.”

Let us use the word “entity” for Gibb’s observable or conceivable part of the world.

There is no limit to what we can think about - name and describe - as a discrete entity.

E.g. An atom, a planet, a tree, a rain forest, a tennis match, a brick, a church.

Also, a group of people, a hurricane, a performance of a symphony, or a socio-technical entity such as IBM.

If every discernible thing, every discrete entity, is a system, the term brings no useful meaning.

Here, a system is orderly, it is active, and it is an abstraction from the real world.

 

Order

Every observable entity or situation can be divided into parts or elements, but that does not make it a system.

To be a system of interest, the parts must be organized – there must be some order or regularity in how they are related.

 

Activity

The term system is sometimes applied to passive structures like the Linnean classification of species, the Dewey decimal system or the periodic table in Chemistry.

Those structures are highly organized, but they are passive.

The systems of interest here are dynamic, meaning they display behavior and change state over time.

E.g. Consider a tennis match, whose current state is displayed on the score board.

 

Abstraction

System theory distinguishes abstract system types and physical instances of them.

Think of Beethoven’s 5th Symphony as an activity system.

1.     The system architect, Beethoven, conceived and organised the musical notes he wanted an orchestra to play in the symphony.

2.     His symphony score is a model - an abstract system type – a record/expression of the notes he conceived and how they relate to each other.

3.     Symphony performances are physical system instances, which each exhibit the selected notes as sounds in a real-world venue.

4.     Orchestras are social entities that employ musicians to play given roles in physical system instances.

 

Think of any particular business activity system:

1.     The system architects conceive and organise some activities they want business actors to perform, to meet some aims of system sponsors.

2.     Their system architecture definition is a model - an abstract system type – a record/expression of the activity types architects conceived and how they relate to each other.

3.     Business systems in operation are physical system instances, which each exhibit the selected types as activity instances in the real world.

4.     Enterprises are social entities that employ actors to play given roles in physical system instances.

 

That a system is an observer’s perspective or description of an entity or situation is deeply embedded in the history of systems thinking.

Bertalanffy, Ashby, Forrester, Checkland and others all defined a system as a model, an abstraction, a perspective of a discrete entity.

Ashby noted that people use the term “system” in at least two ways.

·       An entity = a real-world thing (e.g. all the people, processes, materials and equipment used in a tennis match) regardless of which observer looks at it.

·       A system = an observer’s view of some regular or repeatable activities that advance some variables/quantities.

 

Ashby noted that the second is the practical view.

"Though the first sounds more imposing… the practical worker inevitably finds second more important."

"Since different systems may be abstracted from the same real thing, a statement true of one may be false of another."

“There can be no such thing as the unique behavior of a [real-world entity], apart from a given observer.”

"There can be as many systems as observers... some so different as to be incompatible.”

“[Therefore] studying real-world entities] by studying only carefully selected aspects of them is simply what is always done in practice.” (Ashby 1956).

 

In his introduction to cybernetics, Ashby wrote:

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

Our first impulse is to point at [some real-world entity] 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).

 

Ashby’s student Krippendorff wrote:

"Ashby defined a system not as something that exists in nature.

A system consisted of a set of variables chosen for attention and relationships between these variables, established by observation, experimentation, or design."

 

In other words, a real-world entity is only a "physical system" when, where and in so far as it realises what Russell Ackoff called an "abstract system".

The abstract system is a model (mental or documented) that represents the particular features that the entity displays when realizing the system that is modelled.

 

This article employs a new device - this epistemological triangle – to relate describers, descriptions and realities

 

Systen theory

Abstract systems

<create and use>              <represent>

System describers <observe and envisage> Physical systems

 

For a detailed explanation of this triangle, read “A philosophy of systems”.

Later in this article, the triangle is edited to reflect the system theories of Bertalanffy, Checkland, Ackoff, Forrester and Ashby.

A paradigm clash

There is probably little dispute that there are:

·       abstract and physical systems - system descriptions and realizations of them by real-world entities

·       natural and designed systems - accidental and purposive systems

·       actors and activities - forms and functions – within a system.

 

The systems of interest contain actors who perform activities.

You may naively think of actors and activities and nouns and verbs, but linguistic philosophy is a dead end here.

Think rather of actors as what exists in space, and activities as what happens over time.

Actors are structures that perform activities; they can be organisms, organs, computers, software components, or other machines.

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

 

A system contains two or more interacting parts or actors.

Consider the organs of a body, or the parts of a steam engine

These actors may interact in physical ways – by exchanging flows of materials, push-pull forces or energy.

Consider the bees in a beehive, or the customer and suppliers in an economy.

These actors may interact by logical communication - by exchanging flows of information – giving descriptions, directions and decisions to each other.

 

The distinguishing feature of social systems is communication of information between actors.

Having said that, even in sociology, the concept of the system is ambiguous.

“The first decision is what to treat as the basic elements of the social system.

The sociological tradition suggests two alternatives: either persons or actions." Seidl 2001

 

In other words, you may approach thinking about a system as:

·       a set of inter-related activities, or

·       a set of inter-related actors.

This article names these two approaches as follows.

 

Activity system theory is about regular activities, performed by actors.

It embraces Ashby's cybernetics, Forrester's system dynamics, and some “soft systems” techniques.

It surfaces in enterprise, business and software architecture models, such as business activity models, process flow charts and data flow diagrams.

And in social systems definable as activities performable by different actors in different social entities.

 

Social entity thinking is about actors, who perform activities.

It is about groups, networks or organizations of actors who interact by communicating, by exchanging information.

Some see organization structures as power structures in which actors exchange information in the form of directions and reports.

More generally, human actors often choose how they act, balancing given or shared purposes with their individual ones.

 

Activity system theory and social entity thinking are contrasted in the rest of this long article (and in this shorter article).

Some slip from one to the other without noticing.

An aim in what follows is to distinguish the two schools of thought and point to how they can be reconciled.

General system theory

The 1954 meeting of the American Association for the Advancement of Science in California was notable.

Some people at that meeting conceived a society for the development of General System Theory (the ISSS mentioned above).

They included:

·       Ludwig von Bertalanffy (1901-1972) the cross-science notion of a system

·       Anatol Rapoport (1911 to 2007) wrote on game theory and social network analysis.

·       Kenneth Boulding (1910-1993) applying general system theory to “management science”.

 

Ludwig von Bertalanffy was a biologist who introduced the idea of a cross-science general system theory in the 1940s.

“There exist models, principles, and laws that apply to generalized systems or their subclasses, irrespective of their particular kind.”

His aim was to discover and elucidate what is common to systems in every scientific discipline, at every level of nesting.

He looked for patterns and principles applicable to several disciplines or domains of knowledge rather than to one.

This section introduces several of his concepts, accompanied by some definition of terms.

System structure and behavior

“A similar hierarchy is found both in "structures" and in "functions."”

In the last resort, [structures and behaviors] may be the very same thing: in the physical world matter dissolves into a play of energies.” Bertalanffy 1968

 

Part: a structure, be it active (actor) or passive (material or information).

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

State: the current material or information structure (variable values) of a system, which changes over time.

System boundary

“Every living organism is essentially an open system. It maintains itself in a continuous inflow and outflow…” Bertalanffy 1968

 

System environment: the world outside the system boundary.

System interface: a description of inputs and output that cross the system boundary.

System boundary: a line separating a system from its environment.

The boundary is arbitrary, a choice made by its observers or designers.

A closed system is not connected to or influenced by anything outside its boundary.

An open system is connected to its wider environment by inputs and outputs – which are describable and testable.

The way that an open system’s activities are driven by inputs is characteristic of that system.

Inter-system flows and feedback

“Another development which is closely connected with system theory is that of… communication.

The general notion in communication theory is that of information.

A second central concept of the theory of communication and control is that of feedback.” Bertalanffy 1968

 

Interaction: an activity involving two or more actors or subsystems.

They may interact by exchanging physical flows of energy (e.g. electromagnetic radiation) or force (e.g. gravity).

Or interact by exchanging logical information, either directly by sending/receiving messages, or indirectly by writing/reading some shared memory.

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

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

Feedback loop: the circular fashion in which output flows influence future input flows, and vice-versa.

 

The interest here is particularly in social activity systems in which actors interact by communicating - by exchanging information - such as descriptions, directions and decisions.

Actors play roles that involve creating and using information in messages and memories.

They respond to information in messages, often in ways determined by some information in memory.

Remembered information represents the last known state of entities or events of importance to the business at hand.

Input messages can update the information state.

 

Some position information in a hierarchy of Wisdom, Knowledge, Information and Data (WKID).

Here is a way to make sense of that hierarchy.

 

·       Data = a structure of matter/energy in which information has been created (encoded) or found (decoded).

·       Information = meaning created or found in data by an actor.

·       Knowledge = information that is accurate enough to be useful.

·       Wisdom = the ability to apply knowledge in new situations.

 

Information is only created or found by an actor when performing a data coding/decoding process, and with reference to a language.

And to succeed in communicating, the communicating actors must share the same language.

(By the way, with respect to these definitions, Shannon's "information theory" is about maintaining the integrity of data structures rather than information.)

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 1968

 

Holism means considering how actors (selected parts of a whole) interact to do things they cannot do on their own.

E.g. consider how the smooth forward motion of a rider on a bicycle emerges from their interaction.

Or how the properties of a higher-level system (e.g. consciousness) emerge from the interactions of lower-level subsystems (e.g. neurons).

 

Holism: looking at a whole in terms of how its parts interact, rather than studying and dissecting each part on its own.

(Holism does not mean considering the whole of a discrete entity, every aspect of it, all at once; we can never do that.)

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

Reductionism: studying or describing parts on their own - often deprecated by systems thinkers.

 

The difficulty with holism/reductionism distinction is that is the boundary of the whole is an arbitrary choice of an observer.

As you zoom in or zoom out, a holistic view becomes reductionistic or vice-versa.

 

E.g. Consider the beating of the human heart.

An observer describes the regular beat as an emergent property of parts (muscles) interacting in a whole (the heart).

You describe it as an ordinary/assumed property of one part in a wider whole (a body).

 

E.g. Consider the flexing of the Tahoma Narrows bridge.

An observer wrongly describes the flexing as an emergent property of the whole thing (the bridge).

You realise it is really an emergent property of a wider whole in which some part(s) of the bridge interact with some part(s) of its environment (the wind).

 

Organicism or hierarchical composition

“We presently "see" the universe as a tremendous hierarchy, from elementary particles to atomic nuclei… to cells, organisms and beyond to supra-individual organizations.” Bertalanffy 1968

To describe and understand any large and complex reality, observers tend to create a hierarchical description.

By zooming in and out, observers can decompose a system into subsystems, and compose subsystems into a system.

 

By the way, the decomposition of a system is not usually fractal, since the system at one level is very different from the next higher or lower system.

Abstraction

“One of the important aspects of the modern changes in scientific thought is that there is no unique and all-embracing "world system."

All scientific constructs are models representing certain aspects or perspectives of reality.” von Bertalanffy’s p94 1968

 

In other words, a system is selective perspective of a reality.

Moreover, one reality may be represented by different scientific models - as light may be modelled as waves or particles.

We can represent the separation of models from reality using our epistemological triangle.

 

General system theory

Models

<create and use>          <represent>

Observers <observe and envisage> Realities

 

By the way, the oft-drawn analogy between business organizations and biological organisms is misleading.

As a biologist, Bertalanffy saw an organism as a system (he may have disregarded failing or diseased parts).

Each biological entity, each plant and animal, does grows as a coherent system.

Its existence as a whole depends on its cells cooperating systematically.

Each cell has the same DNA, and is born to play its particular role in the whole entity; it has no role outside of that.

 

A business organization is a very different kind of entity.

Each human and computer actor employed in a business was born or created outside it.

Each may play various roles, both inside and outside a business, and possibly in conflict with each other.

The “parts” of a business activity system are the roles played by actors, rather than the actors themselves.

 

For more, read Introducing general system theory

Bertalanffy didn’t like some directions in “the System Movement”, but saw the movement as “a fertile chaos” that generated many insights and inspirations.

An aside on two other theorists                        

 

Anatol Rapoport was a mathematical psychologist and biomathematician who made many contributions.

He pioneered the modelling of parasitism and symbiosis, researching cybernetic theory.

This gave a conceptual basis for his work on conflict and cooperation in social groups.

 

When actors interact in a system, they may cooperate, as within a football team or a business system.

But they don’t necessarily help each other; they may compete, as in a game of poker, or a market; or hurt each other, as in a boxing match or a war.

Cooperation, conflict and conflict resolution is a focus of bio-mathematics and game theory.

 

Game theory: cooperation and conflict resolution

In the 1980s, Rapoport won a computer tournament designed to further understanding of the ways in which cooperation could emerge through evolution.

He was recognized for his contribution to world peace through nuclear conflict restraint via his game theoretic models of psychological conflict resolution

 

Social network analysis

Rapoport showed that one can measure flows through large networks.

This enables learning about the speed of the distribution of resources, including information through a society, and what speeds or impedes these flows.

 

Kenneth Boulding was among the first to explore the application of general system theory ideas to “management science”.

He speculated whether the elements of a social system are the human actors, or the roles they play.

What is a role? It is the set of activities an actor is expected to perform.

Thus, Boulding hinted at the paradigm clash between thinking of

·       a social entity – characterised by the actors in it

·       a social activity system – characterised by the roles actors are expected to play

 

Boulding is probably best known today for this hierarchical classification of system types.

1.     Static structures

2.     Clock works

3.     Control mechanisms

4.     Open systems

5.     Lower organisms

6.     Animals

7.     Man

8.     Socio-cultural systems

9.     Symbolic systems

 

“[Boulding’s hierarchy] is impressionistic and intuitive with no claim for logical rigor.” Bertalanffy 1968

It mixes up several scales, including:

·       steps in complexity (simple to complex)

·       steps in composition (small to large)

·       steps in biological evolution,

 

The hierarchy stretches from static structures (1) through robotic organisms and self-aware organisms to their logical products (9), which are static structures (1).

Clockwork mechanisms (2) are open (4) in that they acquire energy from a winder, and give that energy to some movable entity of interest.

The interactions between cells in a lower organism (5) can be much more complex than interactions between animals in a socio-cultural system (8).

(The complexity of a system can only be measured with respect to given a system description, at a given level of abstraction.)

PART TWO: 20th century activity system theory

 

Which parts of general system theory are most directly applicable to business systems in which humans and computers perform activities?

What is here called “activity system theory” generalises concepts from cybernetics and system dynamics, discussed later in this part.

 

When and where is a system called an activity system?

Answer: when and where its parts interact in regular ways, where there is a pattern of behavior.

Where some actors interact in regular activities that advance the state of the system.

This table contains some examples.

 

System

System kind

Actors (active structures)

Activities (behaviors)

State (facts of interest)

A solar system

physical

planets and star

orbit the star

positions of the planets

A windmill

physical

sails, shafts, cogs, millstones

rotate to transform wind energy and corn into flour

wind speed, quantities of corn and flour

A digestive system

biological

teeth, intestines, liver, pancreas etc.

transform food into nutrients and waste

quantities of nutrients and waste in the system

A termite nest

biological

termites

disperse pheromone, deposit material at pheromone peaks

the structure of the nest

A prey-predator system

ecological

e.g. wolves and sheep

births, deaths and predations

e.g. wolf and sheep populations

A tennis match

social

tennis players

motions of the ball and the players

game, set and match scores

A church

social

people

play roles in the church’s organization and services

many and various attributes of roles and services

A circle property calculator

software

software component

calculate perimeter, calculate area

radius, the value of pi (invariant)

An information system

socio-technical

humans and/or computers

messaging and message processing

facts in memories

 

Activity system theory is useful whenever we seek to:

·       understand how some outcome arises from some regular behavior.

·       predict how some outcome will arise from some regular behavior.

·       design a system to behave in a way that produces a desired outcome.

·       intervene in a situation to change some system(s) for some reason.

Core ideas

This section below generalizes how Meadows characterized systems (later in this part).

 

What characterises an activity system?

Actors (people, planets, cells, molecules…) interact to perform the characteristic activities of system.

An actor is a structure (or continuant) that is able to perform activities.

An activity is a behavior (or occurrent) that changes or makes something.

 

What are the aims or purposes of a system?

Actors occupy space, and may be visible or tangible; activities, which run over time, are harder to see, and aims are even harder.

A system’s aims may be perceived or expressed:

·       as intentions or goals - by observers inside or outside the system

·       as what the system does – its effects by way of outputs and/or a line of behavior, showing how the system changes state over time.

These different kinds of aim may be related or distinct.

 

Is every social entity an activity system?

A system’s activities in response to inputs are characteristic of the system.

A system isn’t just any old collection of actors

It is a collection of actors organized to perform the system’s characteristic activities.

 

Is there anything that is not an activity system?

Yes, a passive structure (Linnean classification of species, Dewey decimal system, periodic table in chemistry).

And a collection of actors that do not interact in the particular ways that characterise a system.

 

How to know you are looking at a system?

And not just a bunch of stuff that exists or happens?

a)     Can you identify roles played by actors in interactions?

b)     Do actors cooperate in those roles to produce effects (change the state of the system and/or produce outputs)?

c)     Do those effects differ from the effects of Actors on their own?

d)     Are the activities regular and repeatable?

(Again, activity system theory focuses on roles - the activities an actor is expected to perform.)

 

Which of actors, activities and aims are most important?

All are essential to what a system does, and interdependent.

What matters most are usually its aims and the effects of activities.

The actors, the most tangible and visible elements, are often the least important.

(Again, activity system theory focuses on roles - the activities an actor is expected to perform.)

 

What does it mean to change a system?

Changing actors usually has the least effect on a system; change all the players on a football team, and it is still a football team.

But if a systems’ activities change, then it mutates into a new system generation, or a different system.

Changing a desired aim usually implies changing the activities, which sometimes implies changing the actors.

Definition of terms

 

Actors

Actors are active structures of any kind (people, planets, molecules, machines or whatever).

In a designed system, actors are made, bought, hired or employed to perform the characteristic activities of the system.

 

Activities

Activities are regular behaviors, performed by actors, that advance the state of the system.

Activities create, use and change structures, both passive structures (material or information) and active ones (actors)

Activities advance the internal state of the system, and repetition of activities produces a line of behavior over time.

Activities can also produce outputs, which change the state of the external environment.

 

Aims (purposes or goals)

What is the aim of the solar system?

There are different views of what aims a system meets or is supposed to meet.

Different people may perceive and express the aims different ways, related or distinct.

 

For some, aims are simply what a system does by way of advancing system state and/or producing outputs.

How the system state advances over time may be shown on graphically as lines of behavior.

Meadows said you deduce a system’s purposes from its behavior over time, not from rhetorical declarations of goals.

Beer coined the phrase “the purpose of a system is what it does” (POSIWID).

 

For others, aims are intentions held or declared by observers of a system or actors in it.

Aims may be given to a system by its observers, sponsors, designers or other stakeholders.

The actors who play roles in a system may share those given aims and/or have different aims.

 

On the interplay between personal and shared aims

Peter Senge recommends building a shared vision from personal visions through interaction, give and take.

You may have to suppress some personal visions that others do not share, at least in the same social network/group.

However, you do not (as some seem to presume) belong to only one social group; you belong to many.

As different groups develop different visions. you may choose to spend more of your time with those whose shared vision is closer to your own.

The internet helps you do the reverse of what Peter suggests, which is to discover groups that already share your personal vision. 

Clicking on "accept" so often (as you do) means that the internet will direct you to groups that already share your personal vision. 

 

System state

Most systems of interest to us are stateful, rather than stateless.

State is the current status of a system's physical materials and/or logical information/memory.

This means the system’s structures persist over time, and between discrete activities.

Actors advance the state of the system, which sometimes means recording information for future use.

 

(Again, activity system theory focuses on roles - the activities an actor is expected to perform.

Actors may rest between activities, or do something irrelevant to, even in conflict with, a system they play a role in.)

 

In Ashby’s cybernetics, a system's state is the values that a particular set of state variables have.

“A variable is a measurable quantity that has a value.”

“The state of the system is the set of values that the variables have.”

 

Changes to the information state variables of a system may reflect changes to the material state of the real-world entity that is modelled.

On the other hand, the entity may experience material state changes not reflected information state changes.

 

System

Information state

Material state

A prey-predator system

wolf and sheep populations

the current physical condition of each wolf and sheep.

A tennis match

game, set and match scores

the current condition of the court, the balls and the players.

 

Modelling the dynamics of an activity system

A dynamic system is one defined by a set of state variables, whose values can change over time and/or in response to events.

There are three ways to model dynamics:

 

Continuous-variable dynamics presumes a system's state changes continuously.

The rates of change of state variables can be defined differential equations.

This approach is rarely used in modelling a business or software system.

And if time is divided into small enough intervals, the next approach is approximately the same.

 

Fixed-increment time progression is the basis for software tools that help people use Forrester's system dynamics.

Time is divided into time slices that can each contain several events.

The system state is updated according to the set of events that happened in the preceding time slice.

 

Next-event time progression is the basis for most business and software system modelling, and Ashby's cybernetics.

In a discrete event driven system, the state advances over time in response to discrete input events.

Each event occurs at a particular instant in time.

Between events, no change in the system occurs.

 

Line of behavior

In system's state is the quantities of a particular set of stocks, populations or qualitative attributes.

The trajectory of a system’s state change over time, shown on a graph, might be oscillating, linear, curved or jagged.

Whatever its shape, this “line of behavior” is an inexorable result of the system following its rules.

 

Particular activity system theories

The authors of activity system theories distinguished abstract systems from physical entities or situations.

·       von Bertalanffy saw a “system model” as a selective perspective of an entity in reality.

·       Forrester saw a system as a mathematical model of flows that increase and decrease stocks.

·       Ashby’s saw a system as “an observer’s digest” of some regular behavior performed by an entity

·       Ackoff’ distinguished “abstract systems” from “concrete system”.

·       Checkland’s “soft system” is a perspective of a real-world business.

 

The relationship between physical entities and abstract systems is many-to-many.

One abstract system may be realized by countless physical entities; one physical entity may realise countless abstract systems.

This section outlines some of the activity system theories mentioned above, notably:

·       In Forrester's system dynamics - variables represent populations or resources that are related by cause-effect relationships.

·       In Ashby's cybernetics - a system maintains one or more interrelated state variables; control systems are connected to target systems by feedback loops

·       In Checkland's soft system methodology - actors interact in a network of activities to transform inputs into outputs.

System Dynamics

System Dynamics was founded and promoted by:

·       Jay Forrester (1918 to 2016) every system is a set of quantities that are related to each other.

·       Donella H. Meadows (1941 to 2001) resource use, environmental conservation and sustainability.

 

Jay Forrester (a professor at the MIT Sloan School of Management) was the founder of System Dynamics.

In Forrester’s system dynamics (1950s) a system is a mathematical model of inter-stock flows that increase and decrease stocks.

 

A stock is a variable number representing quantity of a population, stock or resource of any kind.

E.g. populations of wolves and sheep; or happiness level and sunlight level; or the many variables that affect climate change.

 

A flow between two stocks represents how increasing or decreasing one stock increases or decreases another stock.

A change to the quantity of each variable acts to change the quantity of one or more other variables.

 

A causal loop connects two or more stock by flows that form a circular feedback loop.

Suppose variables A and B are connected in a loop by flows in both directions.

·       If increasing A increases B and vice-versa, that is an amplifying loop that leads to a population or resource explosion.

·       If both flows have decreasing effect, that leads to the extinction or exhaustion of a population or resource.

·       If increasing A increases B, but increasing B decreases A, that is a dampening loop that might maintain the two variables in a stable or homeostatic state.

 

A system can be modelled in a causal loop diagram, supported by rules for flows that modify stock quantities.

If you give the state variables some initial values and set the system in motion, its state will change, and the trajectory of each variable can be shown on a graph as a line showing its quantity over time.

That line is a state change trajectory or line of behavior.

 

Forrester was concerned in the first place with entities that can be modelled as having continuous dynamics.

Mathematically, the model is a set of coupled, nonlinear, first-order differential (or integral) equations.  

But when system dynamics is simulated using software tools, the continuous dynamics is converted into discrete event-driven dynamics.

A software tool divides time into discrete intervals, it steps the model through one interval at a time, and reports how stocks, populations or resources change over time.

 

System Dynamics

Mathematical models of causal loops

<create and animate>                          <represent>

System modellers <observe and envisage> Inter-related quantities of things

 

Such a model is most useful when it accurately represents how real-world stocks, populations or resources change over time.

Beware: it is rarely possible to identify resources and flows in a way that captures enough of reality to predict outcomes with certainty.

 

For more on system dynamics, read System Dynamics.

Today, akin to system dynamics, there are agent-based approaches to the analysis of systems.

“Thinking in systems”

Donella Meadows (1941 –2001) was an environmental scientist, teacher, and writer.

She was much concerned with resource use, environmental conservation and sustainability

She is surely best known as lead author of the popular and influential book “Thinking in Systems: a Primer.”

The first half of the book is clearly about system dynamics as Forrester might define it.

In the second half, there is some scope creep, where Meadows discusses human institutions.

 

In general activity system theory, actors interact in activities to advance system state or meet aims.

Meadows terminology may reasonably be aligned with those terms as in this table.

 

Meadows’ system dynamics

General activity system theory

Function or purpose

Element

Behavior

Interconnection

Pattern of behavior over time.

Aim

Actor

Activity

Interaction

Line of behavior (state change trajectory)

 

Meadows relates a system’s function to the trajectory of a system’s state change over time, also called its line of behavior.

Given the quantity of a resource or population, a line of behavior might increase it, maintain it, or exhaust it.

Remember, Meadows was much concerned with resource use, environmental conservation and sustainability

Meadow’s basics ideas about a system

Meadow’s ideas are quoted below, following the pattern used to introduce general activity system theory above.

All quotes are from the Introduction to and Chapter one of Meadows book “Thinking in Systems: a Primer.”

 

What characterises a system?

“A system is a set of things [elements] people, cells, molecules, or whatever interconnected in such a way that they produce their own pattern of behavior over time.”

“The system may be buffeted, constricted, triggered, or driven by outside forces. But the system’s response to these forces is characteristic of itself.

The behavior of a system cannot be known just by knowing the elements of which the system is made.”

 

What are the aims of a system?

“The word function is generally used for a nonhuman system, the word purpose for a human one, but the distinction is not absolute, since so many systems have both human and nonhuman elements.”

“If information-based relationships are hard to see, functions or purposes are even harder.

A system’s function or purpose is not necessarily spoken, written, or expressed explicitly, except through the operation of the system.

The best way to deduce the system’s purpose is to watch for a while to see how the system behaves.

Purposes are deduced from behavior, not from rhetoric or stated goals.”

 

Is every social entity a system?

“A system isn’t just any old collection of things.

A system is an interconnected set of elements that is coherently organized in a way that achieves something.”

 

Is there anything that is not a system?

“Yes—a conglomeration [of elements] without any particular interconnections or function.”

 

How to know you are looking at a system?

“How to know whether you are looking at a system or just a bunch of stuff:

a)      Can you identify parts? . . . and

b)      Do the parts affect each other? . . . and

c)      Do the parts together produce an effect that is different from the effect of each part on its own? and perhaps

d)      Does the effect, the behavior over time, persist in a variety of circumstances?”

(Again, activity system theory focuses on roles played by parts in particular behaviors.)

 

Which of elements, interconnections, or purposes are most important?

“To ask whether elements, interconnections, or purposes are most important is to ask an unsystemic question.

All are essential. All interact. All have their roles.

But the least obvious part of the system, its function or purpose, is often the most crucial determinant of the system’s behavior.

Interconnections are also critically important.”

(Again, activity system theory focuses on roles played by elements in particular interconnections.)

 

What does it mean to change a system?

“Changing relationships usually changes system behavior.

The elements, the parts of systems we are most likely to notice, are often (not always) least important in defining the unique characteristics of the system.”

(Again, the focus is on roles played by elements in particular interconnections.)

 

In chapter 1, Meadows wrote:

“Changing elements usually has the least effect on the system.

If you change all the players on a football team, it is still recognizably a football team.

A system generally goes on being itself, changing only slowly if at all, even with complete substitutions of its elements —as long as its interconnections and purposes remain intact.

If the interconnections change, the system may be greatly altered. It may even become unrecognizable.

Changes in function or purpose also can be drastic.”

Which is to say that changing the purpose of a system is to change the system itself.

Ambiguities in Meadow’s thinking

Chapter one of Meadows' book starts thus:

“A system isn’t just any old collection of things.

A system is an interconnected set of elements that is coherently organized in a way that achieves something.

If you look at that definition closely for a minute, you can see that a system must consist of three kinds of things: elements, interconnections, and a function or purpose.

 

In this definition, Meadows makes a canny generalization, since readers may interpret each kind of thing in different ways.

 

Elements: may be read as

·        active structures (actors),

·        passive material structures (resources), or

·        passive data structures (state variables, quantities of a population, resource or quality).

 

Interconnections: may be read as

·       physical material flows, or

·       logical data flows (signals or messages), or even,

·       social relationships.

 

Functions or purposes: may be read as

·       state changes over time (lines of behavior), or

·       motivations (goals or intentions, individual or communal).

 

Did Meadows intend all these alternative interpretations to be made or allowed?

People aren’t always clear which interpretation they have made, and sometimes slip from one to another.

Meadows didn’t entirely avoid slipping between meanings; however, she was more specific about what her three basic terms mean.

 

Elements (structures)

Meadows wrote of elements as structures – be they active or passive – physical materials or logical data.

 “For example, the elements of your digestive system include teeth, enzymes, stomach, and intestines.” [active and passive material structures]

“A football team is a system with elements such as players, coach, field, and ball” [active and passive material structures.]

“The elements of a system are often the easiest parts to notice, because many of them are visible, tangible things.” [because they are material structures.]

“The elements that make up a tree are roots, trunk, branches, and leaves.” [active material structures]

[Elements include also intangibles such as] “school pride and academic prowess” [quantitative variable attributes possessed by a structure.]

 

Interconnections (flows or behaviors)

Meadows wrote of interconnections as flows or behaviors – physical materials or logical data.

“Some interconnections in systems are actual physical flows, such as the water in the tree’s trunk or the students progressing through a university.

Many interconnections are flows of information—signals that go to decision points or action points within a system.”

These flows represent activities or causal relationships that are definitive of the system.

 

Functions and purposes

Meadows was less clear about the concept of a function or purpose.

“The word function may be used for system with non-human actors; the word purpose for a human one.

But the distinction is blurred, and many systems have both human and non-human actors.”

“A system’s function or purpose is not necessarily spoken, written, or expressed explicitly, except through the operation of the system.

The best way to deduce the system’s purpose is to watch for a while to see how the system behaves.”

 

Meadows was also unclear about what it means to change a system.

"Change the rules from those of football to those of basketball, and you’ve got, as they say, a whole new ball game.” Meadows

OK, but what if the rules change in some less noticeable way?

If we cannot say when a system is no longer the same system, then our idea of a system is fuzzy and debatable.

 

In chapter 5 Meadows wrote:

“Back in Chapter One, I said that one of the most powerful ways to influence the behavior of a system is through its purpose or goal.”

Given that this article separates activity systems from entities, she might have put it differently.

“One of the most powerful ways to influence the behavior of a social entity is through its purpose or goal,

By changing its aim(s), you can change the social activity system that a social entity realizes.”

 

In business, the term organization usually refers to a management structure.

Meadows doesn't clearly distinguish this kind of organization from her core idea of looking at a real-world entity as a causal loop network.

A result is assertions that casual readers may read as they like, but studious readers may find difficult to interpret.

 

Did Meadows distinguish

·       activity systems from physical entities?

·       social activity systems from social entities?

·       system state change from system mutation?

We’ll return to these questions in part three below.

Cybernetics

Cybernetics is the science of how a physical, biological or social machine can be controlled.

It emerged out of efforts in the 1940s to understand the role of information in mechanical system control.

Thinkers in this domain include:

·       Norbert Wiener (1894-1964) the science of system control.

·       W. Ross Ashby (1903-1972) the law of requisite variety.

·       Alan Turing (1912 –1954) finite state machines and artificial intelligence.

 

Wiener discussed how a controller directs a target system to maintain its state variables in a desired range.

E.g. Consider how a thermostat (control system) directs the actions of a heating system (target system).

The control system receives signals or messages that describe the state of a target system

The control system responds by sending signals messages to direct activities in the target system.                                                                                                         

 

A concept of importance to cybernetics is information feedback.

Information feedback loops are found in both organic, mechanical, business and software systems:

·       A missile guidance system senses spatial information, and sends messages to direct the missile.

·       A brain holds a model of things in its environment, which an organism uses to manipulate those things.

·       A business database holds a model of business entities and events, which people use to monitor and direct those entities and events.

·       A software system holds a model of entities and events that it monitors and directs in its environment.

 

Ashby popularized the usage of the term 'cybernetics' to refer to self-regulating (rather than self-organizing) systems.

For decades, many thinkers had been particularly interested in homeostatic systems.

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 activity 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.

 

Homeostatic entities and processes are only a subset of systems in general.

Ashby was interested in all dynamic systems, which display behavior and change state over time, either continually or in discrete steps.

His system is a theory of how an entity (mechanical, biological or social) behaves, or should behave.

He sometimes referred to the entity as a “real machine”, as in the triangle below.

 

Ashby’s cybernetics

Abstract systems

<create and use>                   <represent>

Observers <observe and envisage> Real machines

 

An abstract system is a model or type; a real machine is an instance of that type.

A real machine is the realization, in the physical world, of an abstract system description.

E.g. A real-world hurricane is a realization in the atmosphere of an abstract weather system described by meteorologists.

E.g. Your heart beats in accord with an abstract system known to medical science.

 

An abstract system describes roles for actors, and rules for activities, and state variables that represent system state.

The abstract system does not have to be a perfect model of an entity’s behavior; only accurate enough to be useful.

We can test that an entity realises an abstract system to the degree of accuracy we need for practical use.

 

Our epistemological triangle can be used to indicate the abstract to physical relationship.

 

How systems may be described

Roles, Rules, Variables

<create and use>                       <represent>

System describers <observe and envisage> Actors, Activities, State

 

Ashby’s core ideas are distilled below.

·       On dynamics: continuous dynamics can be simulated using discrete dynamics

·       On abstraction: many physical entities can realize one activity system; many activity systems can be realized by one physical entity

·       On regularity: an activity system applies a set of rules to a set of state variables.

·       On adaptation: system state change differs from system mutation or rule change.

·       On self-organization: growth differs from improvement.

·       The law of requisite variety: controllers recognize the variety they seek to control.

 

For more, read Ashby’s ideas

Soft Systems

Observers can use various techniques to model the actors, activities and state of a system.

Using Ashby’s cybernetics, observers model a system as a set of state variables advanced by processes.

Using Forrester’s system dynamics, observers model a system as a set of stocks (variable quantities) increased and decreased by inter-stock flows.

Using Checkland’s soft systems method, observers model a system as actors playing roles in activities that transform inputs from the environment into outputs for customers.

 

The term “soft system” emerged in the 1970s; however, the distinction between hard and soft systems is debatable.

Remember that all system theorists discussed above consider a system to be “soft” in the sense that it is a perspective of the real world.

All three gurus below mixed some activity system theory with some sociology about the human “organization”.

 

Churchman, one of the first soft systems thinkers, said "a thing is what it does".

He outlined these considerations for designing a system managed by people:

·       “The total system objectives and performance measures;

·       the system’s environment: the fixed constraints;

·       the resources of the system;

·       the components of the system, their activities, goals and measures of performance; and,

·       the management of the system.”

 

Like other soft systems thinkers, Churchman sought to integrate activity system theory into “management science”.

The trouble is that in replacing the word “business” by “system” he tended to confuse a social entity with a social activity system.

 

Somewhat better-known soft systems thinkers include:

·       Russell L Ackoff (1919-2009) human organizations as purposeful systems.

·       Peter Checkland (born 1930) the Soft Systems Methodology.

·       Stafford Beer (1926- 2002) management cybernetics and the Viable System Model.

 

Ackoff wrote many works on systems.

In Ackoff’s vocabulary for systems (1971), an “abstract system” represents a “concrete system”.

To paraphrase his second and third definitions of terms:

·       Abstract system: a system in which the elements are concepts.

·       Concrete system: a system that has two or more objects.

 

Ackoff’s abstract system is a description or model of how an entity behaves, or should behave.

His concrete system is any entity that realises to an abstract system.

He wrote that: “Different observers of the same phenomena [the same discernible entity] may conceptualise them into different systems and environments.”

 

Ackoff’s system theory

Abstract systems

<create and use>                        <represent>

Systems thinkers <observe and envisage> Concrete systems

 

Ackoff wrote widely and wisely about the management of human institutions or organizations.

He did somewhat lose the system plot when speaking of human organizations as systems - regardless of any model or perspective.

Like Churchman, when he replaced the word “organization” by “system” he confused a structured social entity with a social activity system.

 

Checkland promoted a “soft systems methodology” for the analysis and design of business systems.

He regarded a business system as an input-to-output transformation.

A network of activities (a business activity model) that transform inputs into outputs

Different observers may perceive different systems of that kind, possibly in conflict, in one organization.

He called each perspective (each a “soft system” if you like) a “weltenshauung” or world view.

 

Checkland’s Soft systems methodology

World views

<create and use>                        <represent>

Observers <observe and envisage> Human organizations

 

Checkland said the term “soft” was intended to characterize his methodology or approach.

He noted that the distinction between hard and soft system approaches is slippery; people get it one day, and lose it the next.

 

Today, soft system methods typically involve:

·       Considering the bigger picture

·       Studying the vision/problems/objectives

·       Identifying owners, customers, suppliers and other stakeholders

·       Identifying stakeholder concerns and assumptions

·       Unfolding multiple views, promoting mutual understanding

·       Analysis, visual modelling, experimentation or prototyping

·       Considering the cultural attitude to change and risk

·       Prioritizing requirements.

 

The challenge for people using soft systems is not to understand what an activity system is.

The challenges lie managing a change process, gathering different views, reconciling them and making a successful change.

 

For more, read

·       Checkland’s ideas

·       Ackoff’s ideas

·       Beer’s ideas

Conclusions to part two

Some slip from activity system theory to social entity thinking without noticing.

An aim here is to distinguish the two schools of thought and point to how they can be reconciled.

 

In activity system theory, a system is a particular way of looking at a real-world entity or situation.

In short, the entity must behave systematically.

Activity system theory is useful whenever we seek to:

·       understand how some outcome arises from some regular behavior.

·       predict how some outcome will arise from some regular behavior.

·       design a system to behave in a way that produces a desired outcome.

·       intervene in a situation to change some system(s) for some reason.

 

This slide show shows how cybernetics and general activity theory inform enterprise architecture models and meta models.

 

An abiding sin of some “systems thinkers” is over generalization

They take a word from one domain and use it with a different meaning in a different (often social or business) domain.

This does not produce a more general system theory - it merely draws a superficial analogy between what can be very different concepts.

This phenomenon, sometimes called "overloading", introduces ambiguity into discussions, and analogies that can be misleading.

 

Much systems thinking discussion is confused by three particular ambiguities Ashby identified.

To progress a general activity system theory, we must acknowledge and address these ambiguities.

 

We need to distinguish activity systems from entities

1 An entity (material object, real-world thing or organization)

2 An activity system realized by an entity    

 

We need to distinguish two kinds of system change

1 System state change (be it homeostatic or progressive)

2 System mutation

 

We need to distinguish two kinds of self-organization

1 Rule-bound self-assembly (of parts into a whole)

2 Rule-changing improvement (system mutation)

 

These needs are explored a little further below, and a lot further in later articles.

 

This article introduces two new ideas:

3)     In place of the classic semiotic triangle - the more instructive "epistemological triangle"

4)     In place of second order cybernetics - the notion of separating meta system M from system S – allowing one actor to play a role in each.

The second gives us a way to reconcile activity system theory with self-organization and social entity thinking.

PART THREE: Ambiguities and distinctions

 

Again, to progress a general activity system theory, we must expose, acknowledge and address ambiguities.

Remember this epistemological triangle.

 

Systen theory

Abstract systems

<create and use>              <represent>

System describers <observe and envisage> Physical systems

 

An abstract system is named and described in terms of aims, roles for actors and rules for activities (e.g. the rules of poker).

The physical system in the triangle may be seen from two perspectives.

·       Seen as a physical entity - the actors and other resources needed to realise an abstract system (e.g. a card school with a pack of cards).

·       Seen as an activity system in operation – as a realization in the real world of an abstract system by a physical entity (e.g. a game of poker).

 

One physical entity may realise several systems at the same time.

Different observers, watching the members of a card school play cards, may see different systems.

·       A card player sees they are realizing the "poker" system - playing cards according to the roles and rules of that game.

·       An economist sees they are realizing a system to transfer money from the less skilled to the more skilled. 

·       A psychologist sees each player is realizing a system in which they are conditioned by occasional and near random rewards to repeat a behavior. 

·       A sociologist sees the players are realizing a system in which the card game is a front for exchanging anecdotes and reinforcing social bonds and/or a dominance hierarchy. 

·       A heating engineer sees the players as realizing a system that generates heat and so reduces the heating bill of the host.

 

Each is system abstraction from the same real-world entity, made by an observer, given the interests they bring to their observation.

 

Does reality exist at all?

To pre-empt a philosophical debate that could run on for years, presumptions here include:

·       we can observe an entity in the real world, describe it, and prove it exists to others’ satisfaction

·       we can observe an entity's behavior, describe it, and test how well it conforms to a particular system description

·       we can also envisage entities and systems that do not yet exist, and then make them

·       we can do all above accurately enough to be useful.

Distinguishing activity systems from physical entities

When introducing cybernetics, Ashby noted that the term “system” is ambiguous in discussions.

Because systems thinkers use the term in at least two ways.

·       An entity = a complete real-world thing (e.g. all the people, processes and technologies of a business) regardless of which observer looks at it.

·       A system = an observer’s view of some regular or repeatable behaviors that advance some variables/quantities.

 

Krippendorff, a student of Ashby, wrote:

"It is important to stress Ashby defined a system not as something that exists in nature.”

“What we know of a system always is an ‘observer’s digest’.”

 

In Ashby’s “Introduction to Cybernetics”, a system is an abstraction from a real-world entity (be it observed or envisaged).

3/11 “At this point we must be clear about how a "system" is to be defined.

Our first impulse is to point at [some entity in the real world] 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)

 

The relationship between physical entities and abstract systems is many-to-many.

One abstract system <may be realized> by many physical entities.

 

One abstract system (type)

may be instantiated (in a physical system)

by many real-world entities

“Carbon capture”

may be realized (by photosynthesis processes)

by countless rain forests

“Poker”

may be followed (in a game)

by many card schools

One musical score

may be performed (in a performance)

by many orchestras

One program

may be executed (in an execution)

by many computers

 

One physical entity <may realise> many abstract systems.

 

One real-world entity

may instantiate many different abstract systems

One rain forest

may capture carbon in tree trunks, and sustain biodiversity

One card school

may play many different games (poker, whist and pizza sharing)

One orchestra

may perform many different musical scores

One computer

may execute many different programs

 

Distinguishing social activity systems from social entities

“Management scientists” are concerned with the management of socio-technical entities that employ human actors.

Many speak of a business, such as IBM, as a system – but speak contrary to Ashby and Meadows concept of a system.

Because they speak without reference to any particular "characteristic set of behaviors" or "pattern of behavior over time".

And presume the purposes of the business are declared as goals by managers or other actors.

 

To rescue the system concept, we need to distinguish social entities from social activity systems, as this table indicates.

 

A social entity

A social activity system

A set of actors who communicate as they choose.

A set of activities performed by actors.

A physical entity in the real world.

A performance of abstract roles and rules by actors in social entity

Ever-changing at the whim of the actors

Described and changed under change control

 

A social entity is a group of people who inter-communicate.

It is an entity, it is a bounded whole, but is it a system?

The actors in a business often behave in ad hoc, impromptu and disorderly ways.

In fact, every business depends on people doing this, making decisions and acting in creative and innovative ways.

Which is fine; there is no reason to presume business - as a whole - is one coherent activity system as Ashby or Meadows would see it.

 

A social activity system can be seen as a game in which actors play roles and follow rules.

You rely on countless human activity systems; for example, you wouldn't want to:

·       stand trial in a court that didn’t follow court procedures

·       board a train or airplane operated by people who didn’t follow the rules.

·       invest in a company that didn’t repay its loans as promised

·       play poker with people who ignore the laws of the game.

 

How the two concepts are related

The relationship between the two concepts is many to many

One social entity <can realise> several social activity systems.

One social activity system <can be realized by> several social entities.

 

The two concepts are realised at the same time, alongside each other, in any real-world business.

When and where the actors creatively invent how they interact, that social entity does not behave as an activity system.

These ad hoc activities lie outside of any activity system employed or deployed by that business.

The business is an activity system only when and in so far as its actors interact in regular ways – where there are describable roles or rules.

 

Moreover, every human actor can belong to many social entities and play roles in many systems.

 

Did Meadows distinguish activity systems from physical entities?

At the start of Meadows’s book, the following entities are given as examples of a system.

"A school, a city, a factory, a corporation, a national economy, an animal, a tree, the earth, the solar system, a galaxy."

Whether by accident or design, Meadows seems here to deny the possibility of abstracting different systems from one real-world entity.

 

More generally, a system is only one perspective of a real word entity (be it a school, a corporation, or planet earth).

And in practice, many systems can be abstracted from observation of such an entity.

Each system each be accurate and useful for some purpose, yet two such systems be unrelatable or incompatible.

E.g. physicists may model a stream of flight as waves or particles.

 

Did Meadows distinguish social activity systems from social entities?

In the second half of the book, Meadows sometimes refers to a human organization as though it is a system.

And says some things that seem contrary to system dynamics and/or conventional business management practices.

 

“[A system’s] purposes are deduced from its behavior over time, not from rhetoric or stated goals.”

This is a very particular view of “purpose”, not the usual one in management science.

 

“Hierarchical systems evolve from the bottom up.”

This may apply to biological organisms, but business organizations are often shaped and reshaped from the top down by their directors.

 

“The purpose of the upper layers of the hierarchy is to serve the purposes of the lower layers.”

This may be desirable in a human society, but is a subjective view, and it is possible to take the opposite view.

 

In business, the term organization usually refers to a management structure.

Meadows doesn't clearly distinguish this kind of organization from her core idea of looking at a real-world entity as a causal loop network.

A result is some statements that casual readers may read as they like, but studious readers may find difficult to interpret.

Distinguishing two kinds of activity system change

Continuous change is the nature of our universe.

However, an activity system is (by definition) an island of regularity in the ever-unfolding process that is the universe.

 

When introducing cybernetics, Ashby distinguished two kinds of system change or adaptation.

“5/7. the word 'adaptation' is commonly used in two senses which refer to different processes.”

When introducing system dynamics, Meadows was less clear than Ashby about the distinction, but did imply it.

 

System state change

Ashby’s system can change state, either under its own internal drive, or in response to changes in its environment.

E.g. A word processor changes state when you select the font you want to use when typing.

 

The first half of Meadows’ book discusses systems as in Forrester’s system dynamics.

A system contains a set of resources, each of which increased and/or decreased by repeated flows or interactions.

“A system generally goes on being itself, changing slowly if ever, even with complete substitutions of its actors —as long as its interactions and purposes remain intact.” Meadows

 

Typically, a line of business manager oversees actors employed in the regular operations of a business system.

The state of a system, S, is continually advanced by business activities, so it continually changes in that way

 

System mutation

Ashby’s system (its roles, rules and variables) can change or be changed, creating a new and different system.

E.g. A word processor mutates when the vendor releases a new version.

 

Similarly, to change a systems dynamics model (add or remove resources, or change the interactions) is to define a new system.

That new system may be regarded as the next generation of the same system, or a different system altogether.

"Change the rules from those of football to those of basketball, and you’ve got, as they say, a whole new ball game.” Meadows

 

Project managers, enterprise architects oversee changes to the roles, rules and variables of business systems

They act in a meta system, M, to system S, as discussed below.

Distinguishing two kinds of self-organization

Ashby distinguished two kinds of self-organization.

·       “Changes from parts separated to parts joined” “Self-connecting” “Perfectly straightforward”

·       “Changing from a bad way of behaving to a good.”

 

The two kinds might be named and differentiated as:

·       rule-bound self-assembly (as when autonomous geese join in a flight of geese)

·       rule-changing improvement (as when a machine reconfigures itself to behave in a different way).

 

Ashby and Maturana rejected self-improvement as undermining the concept of a system.

Ashby said: “No machine can be self-organizing in this sense.”

And that to re-organize a system, it must be coupled to another “higher” system

In effect, Ashby argued that second order cybernetics (see part four below) confuses:

·       the acts of actors in an activity system with

·       the acts of actors in a social entity that can define or change the activity system.

 

The epistemological triangle, being recursive, presents us with an alternative view to second order cybernetics.

To change a system (S) from one generation to the next you need a higher-level process or meta system (M).

One actor can play a role in both systems, as shown in these triangles.

 

Actors role in the meta system (M)

Actors role inside the system (S)

The roles and rules of S

<create and use>          <represent>

Actors <observe and envisage> S in operation

State variables

<create and use>          <represent>

Actors <observe and envisage> Environment of S

 

Note that while drawing a model of S is difficult enough, drawing it for M is an even bigger challenge.

And for discussion of complex adaptive systems, see part four below.

Conclusions to part three

An aim in this and related articles is to expose an clarify ambiguities in systems thinking discussion.

We shall consider the impacts of resolving these ambiguities on systems thinking and on “complexity science”.

And point to how the actors in a social entity may act to change the activity system(s) they perform.

PART FOUR: More thoughts

About complex systems

Much "complexity science" is about a system whose properties emerge from interactions between autonomous actors.

A system is said to be “complex” if it displays some kind of “self-organizing” behavior, such as:

 

Emergence - where a systems' properties emerge from the actions of autonomous actors following rules.

E.g. the shimmering of a shoal of fish, or the V shape of flight of geese.

 

Non-linear state change - where state variable values may fluctuate or change unpredictably.

E.g. unit prices in a stock market, or the number of people infected by a virus).

 

Self-assembly - where an entity grows incrementally by adding more elements or actors to its body.

E.g. the growth of a crystal in a liquid, or a plague of locusts.

 

Homeostatic adaptation – where a system is drawn to an "attractor" state and resists being moved from that state.

As in biological and electro-mechanical control systems.

 

In each example above, the system activity is regular or rule-bound.

While results of the activity system may be unexpected or complicated, the rules may be simple.

Many so-called "complex systems" are indeed simple by any common sense definition of the term.

You can read more about complexity science here: http://grahamberrisford.com/AM%204%20System%20theory/What%20is%20a%20complex%20system.htm

 

Complex adaptive system?

A focus of a social entity thinking is often on system mutation by self-organization.

Some use the scientific-sounding term "complex adaptive system" to describe a business.

But we cannot, and are never expected to, describe the whole entity as one coherent system.

And continuous adaptation or reorganization undermines the concept of a system – which is order or regularity.

 

Ashby and others have emphasised that every system of interest to us is an abstraction.

3/11 “Our first impulse is to point at [an entity, say IBM] and to say "the system is that thing there".

This method, however, has a fundamental disadvantage: every [such entity] 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)

 

Every large and complex entity contains far more than any system we can abstract from it

It includes activities outside of the particular system of interest, perhaps even contrary to it.

Moreover, different observers can abstract different systems from an entity such as IBM.

IBM can realise countless different abstract systems in parallel, some of which may be in conflict, and most of which are changed over time.

Therefore, it is meaningless to point to IBM and call it a system without reference to a particular system of interest.

That system of interest must be defined somewhere, whether in a mental or documented model.

 

For sure, IBM is complex socio-technical entity, and it may continually adapt to changes in the environment.

So, IBM might reasonably be called a complex adaptive entity.

If we don't distinguish an ever-evolving social entity from the various activity systems it may realise, the concept of a system evaporates.

For more on that distinction, read on, and read second order cybernetics.

About entities rather than systems

 

Physical entities

Though our universe is said to be a space-time continuum, every intelligent animal makes sense of it by carving it up into mentally-separable chunks.

We perceive and describe the world around us in terms of discrete entities.

Some entities have a physical boundary; some are solid bodies (e.g. the moon, a tree, a person) in space.

Some have solid boundaries (e.g. a farm or a factory).

 

Other entities have only the logical boundary given by a describer or observer (e.g. a rainbow, a symphony, the democratic party, a corporation).

In management science, a business or “organization” encapsulates some actors and activities that must be managed or monitored.

However, the boundary is logical rather than physical, and different people may draw different boundaries

 

Biological entities

A living thing is a large and complex entity; its boundary is determined by its DNA, which appears in every cell.

The DNA stores information - the rues for an organism’s development and function.

                                                                                                   

Life on earth is an even larger and more complex entity.

The biosphere can only survive as a whole if it has balancing (rather than amplifying) loops, such as a CO2-Oxygen balancing system.

And its actors (via sexual reproduction) can adapt to permanent changes in those gas levels – by evolution.

 

Can we call an organism or the biosphere a system? Loosely yes.

But we cannot describe and test every aspect of that system in one place and time.

“What we know of a system always is an ‘observer’s digest’.”

The largest and most complex system we could ever discuss will only ever model one small and simple aspect of a biological entity

And the same applies to any model of a socio-technical system like IBM.

 

Social and business entities

Many social entity thinkers have adopted the terms of more general system theory

Some have adopted the words but use them with different meanings.

And some describe activity system theory in ways they regard as criticisms of it.

 

·       Some criticise it as linear; yet it is not linear in any way I understand the term to mean.

·       Some criticise it as systematic rather than systemic; yet it is as holistic and systemic as it can be.

·       Some criticise it as reductionist; yet social entity thinking often focuses attention on how individual human actors behave.

·       Some criticise it as mechanistic; yet social entity thinkers tend to be fans of Meadows, whose systems are mechanical.

 

Meadows' “Primer in systems thinking” is primarily about systems that can be modelled in a casual loop diagram.

However, denotic causal flows (telling actors what to do) may have effects contrary to the regular causal flows in such a diagram.

And in chapter 5, Meadows notes that manager-set goals can lead to unintended consequences and counter-productive results in activity systems.

 

When Meadows equates a human or business organization to a system, there is an incongruity.

Different observers may see countless different casual loop networks in one managed human institution.

Each network is only one of the many, possibly conflicting, perspectives.

 

So, sorry Donella, it is meaningless to call IBM a system with no reference to your particular perspective or model, be it mental or documented.

Having said that, in chapter 7, Meadows does discuss the importance of verifying system models against realities.

“Expose Your Mental Models to the Light of Day... making them as rigorous as possible, testing them against the evidence,

and being willing to scuttle them if they are no longer supported is nothing more than practicing the scientific method

—something that is done too seldom even in science, and is done hardly at all in social science or management or government or everyday life."

About the sociological perspective

“Though it grew out of organismic biology, general system theory soon branched into most of the humanities.” Laszlo and Krippner.

The idea of social networks is as tricky as identity politics.

Sure, sociologists study how groups of people behave.

But you no longer belong only to one family and small tribe.

In the modern "information age", the concept of the social network begs questions.

·       How do you join or leave a network?

·       Who determines whether you belong?

·       Must you be a member of a network all the time - or only part time?

·       Must you be a full member – or can you be a partial member?

·       How many networks can you belong to?

·       If you align your personal goals with the goals of one, what does that imply for your membership of others?

You can identify with any or group or network you like; but also

·       celebrate you belong to *infinite* nameable subgroups of humanity,

·       don't let yourself be defined by any of them, and

·       don't get sucked into "identity politics" of the kind that treats people labelled differently as enemies, or blames descendants for what their ancestors did.

 

General system theory doesn’t start from or depend on sociology, or analysis of human behavior.

However, it stimulated people to look afresh at social systems in general and business systems in particular.

 

Four ways actors may be organized

Some draw the equation: one human organization = one system.

What does organization mean here?

·       Organization 1: a hierarchy in which actors are told what to do?

·       Organization 2: an anarchy in which individuals determine their own activities? and even their own aims?

·       Organization 3: a causal loop structure in which actors are triggered by flows to perform activities?

·       Organization 4: a social network in which actors communicate as they choose?

All four of these things can be found at once in a real-world business, like IBM.

 

Three ways actors may interact

The opening sentence of chapter one in Meadow’s book is often quoted but variously interpreted.

“A system is an interconnected set of elements that is coherently organized in a way that achieves something.” 

That definition is so generalized that it embraces readings contrary to assertions Meadows makes about systems.

Consider the three different kinds of flow that appears in the three different kinds of organization above.

 

1: Causal flows

Flows in causal loop structure(s) - in which employees are triggered by flows to perform regular activities

A causal loop structure organizes some quantifiable stocks, populations or resources in a structure of causal relationships.

Here, Meadows coherently organized may be read as saying system elements are inter-related as in a causal loop diagram (not a management structure).

And to achieve something may be read as producing "a pattern of behavior over time" (not meeting goals set by people).

 

The flows in a causal loop structure are causal relationships, which trigger actors to perform regular activities.

(Most of Meadows’ book is about systems of this kind, as in system dynamics.

She takes a side-swipe against event-driven models, as are used in most business system modelling.)

 

It is impossible, and never necessary in practice, to describe every causal flow in a business.

And impossible to address all conflicts that may arise between them.

“There can be no such thing as the unique behavior of [IBM], apart from a given observer.”

"There can be as many systems as observers... some so different as to be incompatible.”

“[Therefore] studying [IBM] by studying only carefully selected aspects of [it] is simply what is always done in practice.” (Ashby 1956).

 

2: Denotic causal flows

Flows in management structure – which direct employees in what to do or achieve

A human institution typically organizes people in structure of authority/reporting relationships.

Here, Meadows coherently organized may be read as saying people are related in such a management structure.

And to achieve something may be read as performing given activities to meet given aims.

 

Causal flows trigger actors to perform defined activities.

By contrast, denotic causal flows define the activities to be performed (or tell actors enough to define the activities for themselves?).

In a denotic relationship, a manager gives goals, duties and obligations to an employee.

 

3: Ad hoc information flows

Communications in social networks

A social network is a structure in which actors create connections by communicating with each other.

Much of the inter-actor communication in corporations is ad hoc and impromptu.

Though much of it is essential to a business, the behavior of this network is irregular and outside any definable system.

 

Surely, every real-world business features all three kinds of flow above.

Some of today’s social entity thinking discussion is generic - about how groups of people work effectively together.

It is not about particular business operations; it is instead about how people shape and steer those operations.

It is about a higher process or meta system (M) that defines the workings of regular business operations (S).

 

Second-order Cybernetics (Von Föerster, Bateson, Mead)

“Second-order cybernetics” was developed in the early 1970s.

It was pursued by thinkers including Heinz von Foerster, Gregory Bateson and Margaret Mead.

Read Sociological Systems Thinkers for discussion.

 

Decision theory - or theory of choice

Ackoff noted that the actors in a system, when described as per classical cybernetics, act according roles and rules.

By contrast, the actors in a human society have free will and can act as they choose.

This prompts the question as to how people do, or should, make choices.

A biologist might look to instinct or homeostasis as the basis for making decisions.

A psychologist might look to emotions or Maslow’s hierarchy of needs as the basis for making decisions.

A sociologist or mathematician may take a different perspective.

Read Sociological Systems Thinkers for discussion.

 

Social entities as organisms

In the theory of evolution by natural selection, can a social group be treated as an organism?

Can selection between groups (favoring cooperation) successfully oppose selection within a group (by competition)?

Thinkers who addressed this include:

·       Lynn Margulis – the evolution of cells, organisms and societies

·       Boehm – the evolution of hunter-gatherer groups

·       Elinor Ostrom – the formation of cooperatives.

 

Read Sociological Systems Thinkers for discussion.

 

Luhmann: autopoietic social activity systems

Read Sociological Systems Thinkers for discussion.

 

Habernas: universal pragmatics

Read Sociological Systems Thinkers for discussion.

 

Social entity thinking babble and “systemantics”

Read Sociological Systems Thinkers for discussion.

About systems in enterprise architecture

This slide show shows how cybernetics and general activity theory inform enterprise architecture models and meta models.

 

Business activity systems and software systems are similar in important ways.

The system of interest is composed of actors and activities that are organized in a coherent whole to complete transformations or services of value to external actors in meeting their goals.

 

A simplistic activity system design method is to define:

1.     Aims of external actors and system sponsors

2.     Input/output transformations to meet 1

3.     Activities to complete 2

4.     Actors, messages, memories and other resources needed to perform 3.

 

For ease of management, actors are grouped into lower-level subsystems, which are grouped into higher level subsystems and so on.

At each level, the actors and subsystems connect in a network.

There are design patterns for the structure of a network, and for the way end-to-end behaviors are performed, and trade-offs between design patterns.

 

Defining business data

Remember there is no meaning in data alone.

Meaning appears only in those moments when actors create and use data structures or symbols.

And with reference to a language/system in which that data is mapped to a meaning.

 

In unregulated human society, the meaning of a word like "policy" is ambiguous.

It may be interpreted by each actor as they see fit, and may be changed by one actor regardless of how other actors interpret it.

Successful communication in business systems requires communicating actors to share a controlled vocabulary.

The meaning of (say) "policy number" is registered as the identifier of a particular "policy", which has an agreed set of attributes.

 

Business architects may acquire or define a domain-specific vocabulary in some kind of data dictionary or canonical data model.

This "meta data" defines the meanings created/used by actors when performing a coding/decoding process.

Business architects usually assume that the meaning of a data item is shared by its creators and users.

 

Changing a designed business activity system

Think again about a game of poker.

To design such an activity system is to define roles for its actors and rules for its activities.

To change those roles or rules is to design a new system, or system generation.

Where card players act in ad hoc ways, those activities lie outside the designed system.

 

An business activity system is stable for a generation, but changes from generation to generation - under change control.

Business architects presume activity systems are designed and changed in discrete and testable steps, from one generation to the next.

It actors cannot continually change how they individually interpret messages and memories, since this undermines the very concept of a system.

 

Applying social entity thinking to a business

Many activities in a business are irregular; people make ad hoc decisions about what to do.

Business architects do not model informal social networks and ad hoc inter-actor communications.

Nevertheless, both are essential to the success of a business.

 

Much "systems thinking" is about a social group or network in which actors can act in ad hoc ways.

Social entity thinking concepts and techniques may be useful, for example in discussion of stakeholder's concerns and in business change management.

 

In short

EAs apply "activity system theory" rather than "social entity thinking".

 

Further reading

Principles and concepts of business architecture

 

 

All free-to-read materials at http://avancier.website are paid for out of income from Avancier’s training courses and methods licences.

If you find the web site helpful, please spread the word and link to http://avancier.website in whichever social media you use.