1b Five new ideas for systems thinkers https://bit.ly/2w5XKNK

Copyright 2017 Graham Berrisford. Now a chapter in “the book” at https://bit.ly/2yXGImr. Last updated 12/02/2021 14:48

 

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

 

The first version of chapter 1 distils centuries of systems thinking. It brings some new clarity to the field by dividing it into broad two schools, not hard and soft, but activity-oriented and actor-oriented. This second version of chapter 1 further differentiates the two schools, begins to reconcile them and relate them to Enterprise Architecture (EA). It outlines five new ideas for systems thinkers, which inform later chapters.

Contents

Three ideas from system thinking history. 1

Part one: a review of general system theory. 4

Part two: generalizing activity system theory. 7

Part three: social entity thinking. 12

Part four: classifying system change types. 13

Conclusions and remarks. 16

References and recommended reading. 17

 

Three ideas from system thinking history

Are you reasonably familiar with the terms used in this chapter? If not, you might want to read the previous chapter first. For some, reading this book requires something of a paradigm shift. This first section contains three ideas to remember

One phenomenon can be described in several ways

We use a map (mental or documented) to help us understand a territory. The map relates selected features of a territory to each other. The triangle below relates mappers to maps and territories. Read it left to right thus: Mappers <create and use> Maps <represent> Territories.

 

Cartography

Maps

<create and use>          <represent>

Mappers  <observe and envisage> Territories

 

Different maps, showing different features, may be drawn of one territory. You may use different maps for country walks, planning journeys, driving, studying geographic features, studying demographic features. And you may need to find or buy a new map now and then.

 

The more general triangle below relates describers to descriptions and phenomena.

 

Episteomology

Descriptions

<create and use>              <represent>

Describers <observe and envisage> Phenomena

 

This triangle is edited later to represent enterprise or business architecture. Bear in mind, a description in the mind is at the top, not the left.

Systems are abstractions from physical phenomena

A territory is infinitely complex; a map cannot be a complete or perfect representation of it. A map is an abstraction that shows only enough that we can find or learn what we need to.

 

The universe is an infinitely complex ever-unfolding process. A system is a regular pattern in that process, or rather, a model of it. The systems thinkers below defined a system as an abstraction.

·       von Bertalanffy’s “system model” is a selective perspective of a physical entity.

·       Forrester’s system is a mathematical model of flows that increase and decrease stocks.

·       Ashby’s system is “an observer’s digest” of some regular behavior performed by an entity.

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

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

 

Some go as far as to say systems don’t exist in the real world, which is misleading. Ashby offered the following example (after Tinbergen) of an abstract system that is realized in nature. The columns of this table show stickleback roles. The two sticklebacks communicate by sending information in the form visual signals to each other. The rows show the successive states of the system.

 

Stickleback mating system

The female’s role is to

The male’s role is to

present a swollen abdomen and special movements

present a red colour and a zigzag dance

swim towards the male

turn around and swim rapidly to the nest.

follow the male to the nest

point its head into the nest entrance

enter the nest

quiver in reaction to the female being in the nest

spawn fresh eggs in the nest

fertilise the eggs

 

The abstract system above features active structures (sticklebacks) and passive structures (nest and eggs). It defines roles in which each actor sends information (in visual signals) that trigger the other actor to respond.

 

In Ashby’s cybernetics, there are three distinguishable concepts. 1) an abstract system: a description of some regular behavior. 2) a physical system: a performance, instantiation or exhibition of the abstract system. And 3) a physical entity that realizes the physical system.

 

Stickleback mating ritual

Abstract system (roles, rules and variable types)

The type, a model of the ritual

Physical system (actors and activities, variable values)

An instance of the ritual

Physical entities

A pair of sticklebacks, nest and eggs

 

The physical system is far from all that the two sticklebacks do. The same separation of abstract system, physical system and physical entities can be seen in the next two examples.

 

 

A symphony

A billing system

System architect

A composer conceives and organizes the musical notes an orchestra is to play in a symphony.

A system architect conceives and organises the activities business actors are to perform, to produce invoices and collect payments.

Abstract system

(role, rule and variable types)

The symphony score is a model -– a representation of musical notes conceived and how they relate to each other.

The architecture definition is a model – a representation of activity types architects conceived and how they relate to each other.

Physical system

(actor and activity instances, variable values)

Symphony performances each exhibit the selected notes as sounds in a real-world venue.

Business operations exhibit selected activity types as activity instances in the real world.

Physical entities

Orchestras employ musicians to play roles in symphony performances.

Enterprises employ actors to play roles in business operations.

 

Ashby deprecated referring to a physical entity as a system.

3/11 Our first impulse is to point at [some physical entity] and say "the system is that thing there". [However] every material object contains an infinity of variables and possible systems. Any suggestion we study "all" the facts is unrealistic, and never [attempted]. [In practice we] pick out and study the facts relevant to some main interest already given.” (Ashby 1956)

There is a dichotomy between actor and activity-oriented views of society

A big question has hung over social system discussions since the nineteenth century.

"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

 

 

The persons?

The actions?

 

Social entity thinking

Activity systems thinking

About

A network of actors, who perform activities

A network of regular activities, performed by actors.

Example

A card school.

A poker game.

How to scope it?

The boundary is drawn around an identifiable group of actors (a species, a tribe, a card school, the employees of a business).

Almost regardless of what activities they perform.

The boundary is drawn around those activities needed to maintain some variables. and/or produce some (emergent) effects or results.

Almost regardless of what actors perform the activities.

How do actors determine their actions?

Actors may be more or less free to invent how they act.

Bearing in mind given aims, shared aims and individual aims.

Actors select from the range of regular activities allowed by the system.

Expanding the range of actions, giving actors a higher degree of freedom, increases the system’s complexity.

 

Importantly, the relationship between activity systems and social entities in many to many.

·       One activity system can be realized by several social entities (the game of poker can be realized by many card schools).

·       One social entity can realize several activity systems (the card school can play whist, or share a pizza).

 

In an extremely rule-bound social entity (think of an ant colony) the actors do nothing but perform regular activities in recognizable systems. By contrast in a human society, the actors are free to invent aims and activities, and make ad hoc decisions. At the extreme - they continually innovate - there are no stable activities, no pattern of behavior, no observable regularity or repetition. So, there is no recognizable activity system in the social entity.

 

Part one below reviews general system theory ideas. Part two discusses activity system theory - about regular activities, performed by actors playing roles (e.g. a poker game). Part three discusses social entity thinking - about actors, who perform activities (e.g. a card school with a pack of cards).

 

Parts two and three present different ideas about what it means for a system to be defined, to exist, and to change or evolve. When people slip from one to the other, the result may be a poetic metaphor, but can also be misleading. So, an aim here is to distinguish the two schools of thought and point to how they can be reconciled. Drawing a clear distinction helps us recognize and resolve ambiguities and confusions in modern systems thinking.

Part one: a review of general system theory

Hierarchy and emergence

To briefly define some terms:

Emergence: the appearance of properties in a higher or wider thing that emerge from coupling lower or smaller things.

Holism: studying or describing how things interact.

Reductionism: studying one or more parts of a thing on their own.

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

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

 

This table (bottom to top) presents a history of the universe from the big bang to human civilisation. Note that, as the biologist and systems thinker Maturana, observed: “knowledge is a biological phenomenon”. In other words, before life, there was no knowledge, description or model of reality.

 

THINKING LEVEL

Elements or actors

Interact by

Knowledge acquisition

Human civilisation

Human organizations

Information encoded in writings

Science and enterprise

Human sociology

Humans in groups

Information encoded in speech

Teaching and logic

Social psychology

Animals in groups

Information encoded in signals

Parenting and copying

Psychology

Animals with memories

Sense, thought, response

Conditioning

Biology

Living organisms

Sense, response. Reproduction

Inheritance

Organic chemistry

Carbon-based molecules

Organic reactions

 

Inorganic chemistry

Molecules

Inorganic reactions

 

Physics

Matter and energy

Forces

 

 

In relation to the table above, we can identify three kinds of emergence, and three misconceptions. The three kinds of emergence are.

·       The emergence by evolution of higher levels over time

·       The everyday emergence of higher-level phenomena from lower-level phenomena.

·       The everyday emergence of effects from interactions between things at one level

 

The three misconceptions are.

·       Emergence does not require a system to have many actors.

·       Emergence does not mean a system behaves in a surprising or unpredictable way.

·       Emergence does not mean a system is complex in any normal sense of the term

 

For discussion and explanation of the above, read the previous chapter.

 

Taking a holistic view of a business does not mean you study the whole of the business, every conceivable element of it. It can mean you observe or envisage elements relevant to your motivation for understanding or describing the business. Then, study or design how those elements interact to produce outcomes they cannot produce on their own. Or else, it can mean you identify some unexplained effect or outcome of a business in operation, then look for what things must interact to produce it.

Encapsulating a system within an environment

To briefly define some terms:

System: a pattern, an island of regularity or repetition, in processes of the universe.

Closed system: a system not connected to anything outside its boundary.

Open system: a system connected to its wider environment by inputs and outputs.

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

System environment: the world outside the system boundary.

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

 

Traditionally, a system is seen as a set of regular activities that transform inputs into outputs. This is the presumption, for example, in Checkland's "soft systems methodology". The diagram below encapsulates the processes of a system between inputs and outputs.

 

Suppliers

Activity System

Customers

Inputs

Processes

Outputs

 

What this SIPOC diagram doesn't show is that the outputs of a business system (say invoices) can influence its future inputs (say, payments).

In other words, there are feedback loops between a business system and actors in its environment.

Information and communication

To briefly define some terms:

Communication: the exchange of information between senders and receivers.

Data: a structure of matter/energy in which information has been created/encoded or found/decoded

Information: a structure or behavior that represents something or phenomenon.

Feedback loop: the circular fashion in which output flows influence future input flows.

Memories and messages: holders of information.

 

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

The term is used in three concepts that overlap and are entangled.

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

·       Causal flow: the progress from a cause to an effect, or actor to acted-on.

·       Denotic flow: the conveyance of goals, duties or obligations.

 

Actors may interact by exchanging flows of any kind above. Data / information flows are especially important in cybernetics, society and business.

 

In natural language: the terms data, information and knowledge are often used interchangeably. To facilitate discussion of social systems, an informal classification is helpful. Several WKID hierarchies have been proposed and criticized. The version below seems the best fit to a system of communicating actors.

 

WKID

meaning

Wisdom

the ability to apply knowledge in new situations.

Knowledge

information that is accurate enough to be useful.

Information

meaning created/encoded or found/decoded in data by an actor.

Data

a structure of matter/energy in which information has been created/encoded or found/decoded

 

Any physical structure or motion, of matter or energy, can be used as a data structure. E.g. the biochemical structure of your brain, dance movements, sound, spoken words or written words.

 

To put it another way, any phenomenon that is variable, has a variety of values, can be used to store or convey information. A phenomenon is used as data structure when it is encoded to convey information/meaning, and when it is decoded as conveying information/meaning.

 

In any communication stack, the information/data distinction is recursive. The actors at level N+1 abstract the logical information of interest to them from the physical data structure at level N.

 

Feedback was defined earlier as the circular fashion in which output flows influence future input flows. Information feedback is a central concept in cybernetics (see part two), and central to the success of business operations.

 

Information was defined above as a structure or behavior that represents something or phenomenon. More scientifically: “A carries information about B if the state of A is correlated with the state of B.” Intelligent animals can hold information in memory. (cf. encapsulated state data).

 

Intelligent life

Information in memories

<create and use>          <represents>

Animals      <observe and envisage>    Phenomena

 

Social animals communicate. Communication was defined above as the exchange of information between senders and receivers. Communicating actors can create and use messages that represent phenomena of common interest.

 

Social life

Information in messages

<send & receive>             <represents>

Social animals  <observe and envisage>  Phenomena

 

Less obviously, social animals can communicate indirectly by writing/reading information stored in a shared memory that all can access. (cf. a database).

 

Memories, messages and languages (discussed further in chapter 6a)

Memories and messages were defined above as the holders of information. In biology, internal memories and external messages are of different kinds. Memories are neural patterns, whereas messages take the form of sounds, smells and gestures. By contrast, in software, the distinction between memories and messages is blurred.

 

There is no meaning in a brain’s memory on its own. Meaning is found in the processes of

·       encoding a perception (or conscious thought) into memory

·       decoding that memory into action (or conscious thought).

 

Similarly, in a society of communicating actors, there is no information or meaning the data structure of a message its own. Meaning is found in

·       the sender’s encoding of a data structure, with reference to a language.

·       a receiver’s decoding of that data structure, with reference to a language.

 

Like Ashby, we shall eschew direct discussion of consciousness, though it is implicit in social entity thinking. More importantly here, note that to succeed in communicating, actors must share the same code or language. Where mistakes cannot be allowed, in science for example, domain specific languages are defined. And in information system design, the meaning of a data structure is defined meta data.

Part two: generalizing activity system theory

To briefly define some terms:

Activity (or interaction): a regular behavior or process, performed by actors.

Actor (or element): an active structure of any kind (person, planet, cell, machine etc).

Aim (or purpose): a motivation or outcome.

Causal flow: a relationship between two stocks that represents how increasing or decreasing one increases or decreases the other.

Causal loop: a feedback loop that connects two or more stocks by flows in a circular fashion.

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

Line of behavior: the trajectory of a system’s state change over time, as shown on a graph

Process: a behavior, 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.

Stock: a variable number representing the quantity of a stock, population or resource.

System dynamics: a technique for modelling a system as a set of stocks increased and decreased by inter-stock flows.

 

It is common to classify systems thinking approaches into hard and soft systems thinking methods. And some present the latter as an advance on the latter; this is misleading. This table outlines the three schools of activity systems thinking.

 

Cybernetics

Weiner and Ashby |(reference 2)

System dynamics

Forrester and Meadows (reference 3)

Soft (business) systems

Churchman and Checkland

Regular activities maintain

variables that describe the state of

actors (organisms, machines, societies)

Regular flows increase/decrease

variable stocks that represent the state of

resources or populations of any kind

Regular activities transform

inputs into outputs wanted by

customers

Feedback loops connect

control systems to target systems or entities

a) a receptor senses changes in the state of the target

b) a control center directs responses, and

c) an effector changes the state of the target.

Feedback loops connect

stocks that respond to changes in each other.

The whole model represents a closed system or ecosystem.

Feedback loops connect

a business to its environment thus:

a) it detects changes in the state of its environment

b) it determines responses

c) it directs entities to perform activities.

Observers may observe the current state of a system, and

draw a graph to show how the system's state changes over time.

Observers may draw a diagram of flows between stocks,

and draw a graph of stock level changes over time.

Observers may draw a business activity model, and

read the current state of a system in its data store(s).

 

All three schools are about systems characterized by regular activities (physical, organic, social, economic or ecological). All presume several activity systems may be abstracted from one social entity or business. All accommodate systems that may display complex, non-linear, self-organizing or chaotic behavior.

 

Remember the distinction between hard and soft systems is debatable. All scientific system thinkers consider a system to be “soft” in the sense that it is a perspective or model of some physical phenomena. 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 world views, promoting mutual understanding

·       Analysis, visual modelling, experimentation or prototyping

·       Considering the cultural attitude to change and risk

·       Prioritizing requirements.

 

Most if not all of these ideas can be found in a “hard” business system methods like TOGAF.

A general activity system theory

What is an activity system? It is a regular or repeatable pattern of behavior. E.g.  the motion of a rider on bicycle, a game of poker, or a billing and payment system.

 

Activity systems thinking is applied every day, all over the world. Especially to business systems in which humans and computers perform activities. It 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 regular way that produces a desired outcome.

·       intervene in a situation to change some regular pattern of behavior.

Activity system concepts

Meadows’ “Thinking in systems: a primer” is discussed in chapter 5. It starts with three concepts, which are generalized here as follows.

 

Actor (or element): an active structure of any kind (person, planet, cell, machine etc). It occupies space. Business actors are made, bought, hired or employed to perform business activities.

 

Activity (or interaction): a regular behavior or process, performed by actors over time. Activities create, use and change structures, both passive (material or information) and active (actors). They advance the internal state of a system, and so produce a “line of behavior” over time. They can also produce outputs, which advance the state of the system’s external environment.

 

Aim (or purpose): a motivation or outcome. Aims are commonly perceived or expressed as motivations – goals ascribed by observers - inside or outside the system. However, Beer coined the phrase “the purpose of a system is what it does” (POSIWID). In other words, aims may instead be expressed as outcomes - results or effects – as may be shown in a line of behavior or state change trajectory.

 

This table contains some examples of systems in which actors interact in regular activities to advance the state of the system.

 

System

Actors (active structures)

Activities (behaviors)

State (facts of interest)

A solar system

Star and planets

Orbits

Planet positions

A windmill

Sails, shafts, cogs, millstones

Rotations that transform wind energy and corn into flour

Wind speed, corn and flour quantities

A digestive system

Teeth, intestines, liver, pancreas etc.

Transformation of food into nutrients and waste

Nutrient and waste quantities

A termite nest

Termites

Disperse pheromone. deposit material at pheromone peaks

The structure of the nest

A prey-predator system

Wolves and sheep

Births, deaths and predations

Wolf and sheep populations

A tennis match

Tennis players

Ball and player motions

Game, set and match scores

A church

People

Roles in the church’s organization and services

Various attributes of roles and services

A circle calculator

Computer module

Calculate perimeter, area, etc.

Radius, the value of pi (invariant)

A billing system

Customer and supplier

Order, invoice, payment

Product, unit price, order amount

Activity system FAQS

This table quotes Meadow’s questions and answers on system dynamics. It compares them with the concepts of cybernetics and more general activity system thinking.

 

 

General activity system thinking (after Ashby and Weiner’s cybernetics)

System Dynamics (after Forrester) quoted from Meadows’ “Thinking in Systems: a Primer”

What characterizes an activity system?

A system is characterized by a pattern of interrelated activities.

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

An actor is a structure (or continuant) that plays a role in performing activities.

“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 or purposes of a system?

Actors, which occupy space, may be visible or tangible.

Activities, which run over time, are harder to see.

Aims are even harder to see, and may be perceived or expressed as motivations or as outcomes.

“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 composite entity an activity system?

No, a system isn’t just any old collection of things or actors. It is a collection of actors organized to perform the system’s characteristic activities.

“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 an activity system?

Yes, a passive structure (Linnean classification of species, the Dewey decimal system, the periodic table in chemistry). And a collection of actors that do not interact in a recognizable pattern of activities.

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

How to know you are looking at a system? And not just some stuff that exists or happens?

a)     Are the activities regular and repeatable?

b)     Are the actors’ roles in those activities regular and repeatable?

c)     Do actors interact to produce effects (state changes and outputs) they cannot produce on their own?

“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?”

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.

“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.”

What does it mean to change a system?

Changing actors usually has the least effect on a system (change every player on a football team, 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.

“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.”

And in chapter 1.

“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 appears to say that changing the purpose of a system is to change the system itself.]

 

Despite the high degree of correspondence in the table above, there is some ambiguity in Meadow’s observations, explored in chapter 5.

Causality in activity systems thinking

An activity system is a recognized pattern of behavior – a machine - that we model. The model is a complex type that defines a set of interrelated activity types. We may build the model using the principles of cybernetics, system dynamics or a soft systems method.

 

When observing an activity system’s behavior, we can classify actors’ responses to stimuli into three kinds.

·       Deterministic: we may be able to predict exactly which action an actor will perform in response to an event.

·       Probabilistic: we may be able to predict how likely an actor will choose activity type A over activity type B.

·       Possibilistic: we can always predict the actor will choose from the range of activity types in our model.

Note that the behavior over time, even of a deterministic activity system, can be non-linear or chaotic. And that a Bayesian causal network may be used to represent probabilistic causal relationships.

 

You act predictably when removing your hand from a hot plate. By contrast, you try to act unpredictably when playing a game of poker. As autonomous agent, you can choose between the actions defined in the rules of the game.  However, you cannot innovate (change the rules of poker) while playing your hand of cards.

 

The responses an activity system can make are limited. They were determined when the system was defined, constructed or evolved, even if an observer is unaware of them.

Part three: social entity thinking

 

General system theory didn’t start from sociology, or analysis of human behavior. However, it stimulated people to look afresh at social systems and business systems. One result has been an abundance of questionable analogies and metaphors.

Views of social entities

The previous chapter briefly outlined eight views of society.

·       Society as organism

·       Society as tribe

·       Society as homeostat

·       Society as knowledge builder

·       Society as autopoietic (self-sustaining)

·       Society as network structure

·       Society as an ecology of interacting organizations

·       Society as an organization within a market

 

For further discussion of the eight views above, read chapter 2 of the book.

On causality and innovation in social entities

As before, stimulus-to-response or cause-to-effect relationships are classifiable into three kinds.

·       Deterministic means that given input A and/or current state B, the next action is C.

·       Probablistic means that given input A and/or current state B, the next action is C (probability X) or D (probability Y).

·       Possibilistic means that given input A and/or current state B, the next action is C or D, with no measurable probabilities attached.

 

However, in a human social entity, there is no prospect of defining the range of responses actors can make in response to events or situations. So, we have to think differently about causality. We have to allow that actors can have insight, and react innovatively, and outside of any activity system that could be modelled. Imagine innovation is taken to an extreme. and the actors in a social entity continually innovate. If there is no observable regularity or repetition, no pattern, then there is no recognizable activity system in the social entity.

 

Insightful innovation, a 4th kind of causality, helps a social entity to adapt or be adaptive. And the social entity may in turn, innovatively, adapt any activity system that it participates in. This makes human social entity thinking different from activity systems thinking.

 

For more on social entity thinking read chapter 2 of the book and reference 4.

Part four: classifying system change types

 

System change can be classified in three ways:

·       continuous or discrete

·       state change or mutation

·       natural/accidental or designed/planned.

 

Recognizing change as a three-dimensional phenomenon helps us to think more clearly about what it means to model change and design for it.

 

 

Continuous

Discrete

State change

Natural

Natural

Designed

Designed

Mutation

Natural

Natural

Designed

Designed

 

Continuous change v. discrete change

The universe is an ever-unfolding process of continual change. A social entity continuously and naturally mutates. Its members change, and it responds to environmental changes that were not predicted or anticipated.

 

By contrast, an activity system is an island of regularity in the universe. A system that continually changes its nature would be a contradiction in terms. If there is no stable pattern, no regularity, no repetition, then there is no system to describe. A system cannot possibly be designed to continually mutate into infinite different systems. Ashby and Maturana, separately, rejected continual mutation as undermining the concept of a system. However, continuous change can be simulated by dividing changes into steps frequent and small enough to appear continuous.

System state change v. system mutation

Ashby wrote.

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

Meadows was less clear than Ashby about the distinction, but did allude to it.

 

State change

There are four varieties of state change

1.     Continuous natural state change (e.g. the growth of a crystal in liquid)

2.     Discrete natural state change (e.g. asleep to awake, or day to night)

3.     Continuous designed state change (e.g. analogue light dimmer switch)

4.     Discrete designed state change (e.g. light on to light off

 

“A system generally goes on being itself… even with complete substitutions of its actors as long as its interactions and purposes remain intact.” Meadows

Meadows’ system changes state when  the stocks grow and shrink. But the stocks and the flows that relate them don’t change.

 

System mutation

There are four varieties of mutation,

1.     Continuous natural mutation (e.g. maturation of child into adult)

2.     Discrete natural mutation (e.g. generational change, parent to child)

3.     Continuous designed mutation (impossible)

4.     Discrete designed mutation (e.g. system version 1 to version 2)

 

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

If you change the stocks or the flows in a causal loop diagram, then you define a new system. It may be seen as the next generation of the same system, or a different system altogether.

Self-organization in classical cybernetics

Second order cybernetics is not a later version of classical cybernetics; it is different. It is usually said to be about systems that include the system describer in the system. And thereby enables the system to be self-organizing and creatively self-improving.

 

Ashby and Maturana rejected self-improvement as undermining the concept of a system. Ashby wrote "the term "self-organization" perpetuates a fundamentally confused way of thinking about how a system may evolve". And “No machine can be self-organizing in this sense.” He said that re-organize a system, it must be coupled to another system.

 

An entity that defines or changes a system is usually considered to be at a “higher” level than that system. It could be a social entity, or a process, or what is here called a “meta system”.

 

Ashby proposed the rules of a regular system can be modified by such a higher process or meta system.  His principle is that a system (call it S) cannot reorganize itself. But it can be reorganized by “higher” process or system (call it M). M can observe and change the behavior of S. When circumstances demand it, M changes the roles and rules of S. By doing this, M creates a new generation of S.

 

To change an entity that realizes S, M must:

1.     take as input the abstract system (S version N) realized by the entity

2.     transform that input into a new abstract system (S version N+1)

3.     trigger the entity to realize the new abstract system.

 

Where is the “self-organization” in scheme?

·       The lower system is a simple system, it does not self-organize.

·       The higher system is also a simple system, it does not self-organize.

·       The higher system can reorganize the lower one.

·       The whole system (the composite of higher and lower systems) is a somewhat more complex system, but does not self-organize.

 

Looked at this way term “self-organisation” seems inappropriate and unhelpful. The process of system mutation might better be called “rule-setting reorganization”.

Meta system thinking

Ashby’s idea was that a system can be re-organized by a higher process or meta system. Meta systems thinking adds one more idea, that one actor may play two different roles:

E.g.

·       a role as a tennis player in tennis matches

·       a role as a law maker in the Lawn Tennis Association.

 

It turns out that meta system thinking can be applied in a variety of domains. It can be applied to homeostatic machines, to biological evolution and in sociology.

 

In biology the higher process is discrete -step evolution by natural selection. In sociology, the higher process or meta system is discrete-step evolution by design. Typically, a committee or governing body determines the rules of a collective or other social entity. It designs changes to the roles of actors, and directs actors in the social entity to follow them. It also has some power to ensure compliance to the rules.

 

The separation of meta system from system helps us to reconcile activity system theory with social entity thinking. And goes some way to reconcile second order and classical cybernetics. It does however presume that a system evolves by inter-generational steps. And in the case of human activity systems, this implies some kind of change control.

 

EA is primarily about the design of activity systems that change state in discrete steps, and are changed under change control. For discussion of natural or accidental, and designed or planned change, read the next two chapters.

Conclusions and remarks

 

The preface discussed several reasons why much systems thinking discussion is confused or confusing. One of them is a question that has hung over social system discussions since the nineteenth century.

"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." David Seidl 2001

 

At the centre of your thinking, you may put either the actors or the activities. In this chapter, we distinguish two kinds of systems thinking. Social entity thinking is about a network of actors, who perform activities. Activity systems thinking is about a network of regular activities, performed by actors.

 

This chapter exposes and resolves ambiguities by using five new or newly presented ideas.

1.     Drawing a clear distinction between social entity thinking and activity systems thinking helps us recognize and resolve ambiguities and confusions in modern systems thinking.

2.     Classifying causality into four kinds helps us to characterise what makes social entity thinking different.

3.     Representing “change” as a three-dimensional phenomenon helps us to think more clearly about what it means to model change and design for it.

4.     Separating meta system from system – allowing one actor to play a role in each - helps us to reconcile activity system theory with “self-organization”, and gives us an alternative to second order cybernetics.

5.     Using an "epistemological triangle" to distinguish models from what they model, and relate information to the phenomena it corresponds to, gives us a practical and useful alternative to the classic semiotic triangle.

 

Relevance to EA

It has been said that enterprise architecture (EA) regards an enterprise as a "system of systems". Look at any business and you will see some actors, performing some activities, to meet some agreed aims. To a greater or lesser extent, the actors are organized and the activities are systemized.

 

A business activity system may reasonably be defined thus. It is orderly; it is dynamic; and characterized by regular activities. It is realized by a set of actors and resources that is coherently organized and interconnected so as to perform the required activities. Activities advance the state of things: people, processes, materials and machines of interest. The state can be remembered in the values of state variables and communicated in messages.

 

However, a business is more than the sum of activity systems we can define. It is also a social entity, in which actors are more or less free to act as they see fit. So, we say EA sees a business as a social entity that may realize any number of (possibly conflicting) activity systems, and strives to coordinate them. The advice in short is:

 

Is your interest what actors do to

Then use

meet aims however they choose?

Social entity thinking

 

do both above and below?

Social entity thinking

Activity systems thinking

play roles in regular processes?

 

Activity systems thinking

                                                                                                 

The next chapter looks at social entity thinking with regard to EA. The chapter after that looks at activity systems thinking with regard to EA.

 

Our triangle is edited below to represent enterprise or business architecture.

 

Enterprise architecture

Architectural descriptions

<create and use>                 <represent>

System architects <observe and envisage> Business operations

 

For more, read the chapter 3 of the book, and references 2 and 3.

References and recommended reading

 

Further reading

1.     The rest of “the book” at the top of this page https://bit.ly/2yXGImr

2.     Introduction to Cybernetics” (Ashby 1956/7)  Best read it alongside chapter 4 of “the book”.

3.     Principles of the self-organizing system” (Ashby 1962).

4.     "Thinking in systems: a primer" Donella Meadows. Best read it alongside chapter 5 of “the book”.

5.     Michael Jackson’s 2003 review of system thinking approaches.

 

Read on for issues to beware of in references 4 and 5.

Issues in Meadows primer in systems thinking

Reference 4 tends to confuse social entity thinking with activity thinking.

The need to abstract activity systems from physical entities

Remember that holism not = wholeism. When people speak of a system, they may speak of

·       an entity - the whole of a thing, regardless of how observers look at it.

·       a pattern of activity - regular or repeatable behavior observed or envisaged in a thing.

 

The second is the view taken in activity system theory

 

Ashby put it thus.

 6/14 “the system” [is] ambiguous. [It] may refer to…

·       the thing itself; or to

·       the variables with which some given observer is concerned.

"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).

 

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 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 unrealiztic, 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).

 

In Ashby’s cybernetics, we can identify four distinct concepts, as shown in the graphics below.

 

Ashby’s cybernetics

Poker

Abstract systems

<create and use>              <represent>

Observers  <observe and envisage> Physical systems

The rules of poker

<create and use>              <represent>

Gamers <observe and envisage> Poker games

<realized by>

Physical entities

<realized by>

Card schools

 

Different observers of the same physical entity, may see different systems. Consider how five different observers may see a card school.

A card player sees they are realizing the "poker game" system.

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 people exchange anecdotes and reinforce social bonds.

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

 

Each observer brings their own interests to the phenomena they are observing.

 

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

may be realized as 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

 

Conversely, one physical entity <may realize> 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

 

The same principles apply in sociology. One social entity <can realize> several social activity systems. And conversely, one social activity system <can be realized by> several social entities.

 

Did Meadows abstract activity systems from entities?

At the start of Meadows’ primer in systems thinking, she gives the following entities 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."

This seems to deny the possibility of abstracting different systems from one real-world entity.

 

It is important in activity system thinking to realize that a system is only one perspective of an entity (such as school, a corporation, or planet earth). Many activity systems can be abstracted from observation of such an entity. Each system each be useful for some purpose, yet two such systems be unrelatable or incompatible. E.g. physicists may model a stream of light as waves or particles.

The need to abstract social activity systems from social entities

“Management scientists” are concerned with socio-technical entities that employ human actors. Many speak of a business as a system – but without reference to any "characteristic set of behaviors" or "pattern of behavior over time". 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? Surely, to some extent, every business depends on actors behave in ad hoc, impromptu, creative and disorderly ways. A business - as a whole – cannot be reduced to 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.

 

In any real-world business, the social entity exists over and above any activity systems it employs. When and wherever actors creatively invent their actions, the social entity cannot be modelled as an activity system. These ad hoc activities lie outside of any activity system employed or deployed by that business.

 

Did Meadows abstract activity systems from social entities?

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. 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 social entity 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.

Issues in Jackson’s review of system thinking approaches

Beware that in reference 5 Jackson draws questionable distinctions between hard and soft systems thinking. Jackson

·       refers to a human organization/institution as a "system" - regardless of any model or perspective of it

·       conflates what are distinguished as social entities and activity systems.

·       suggests "hard systems thinking" is reductionist - contrary to the normal goal, outcome or service-oriented approach

·       suggests "hard systems thinking" produces only one view of a business – rather what does do, produce many partial views and stakeholder perspectives.

·       doesn’t recognize that hard, soft system thinking, and system dynamics, all see an activity system as a set of roles and rules for actors and activities.

·       relates complexity to disorder – whereas as complexity in systems is a measure of order

·       refers to "complex adaptive systems" - with no measure of complexity or adaptivity. 

 

 

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.