Disambiguating system terminology

Copyright 2020 Graham Berrisford.

Last updated 18/01/2021 14:46

                                                  

This article is a supplement to the work published at the top of this page https://bit.ly/2yXGImr

It draws on analysis of Ackoff’s “Towards a system of system concepts”,

Ashby’s “Introduction to Cybernetics”, and many other sources.

 

Contents

MEADOWS’ THINKING.. 2

Three kinds of thing in a system.. 2

System dynamics FAQS. 4

On “coherently organized”. 6

GENERAL SYSTEM THEORY.. 7

Three kinds of emergence (evolution, hierarchy and interaction) 7

Three misconceptions about emergence (manyness, surprising, complex) 9

Two kinds of holistic thinking (analysis and design) 9

Two kinds of organization (structural and behavioral) 10

Two kinds of part (passive and active structure) 11

Two kinds of actor (automaton and agent) 12

ACTIVITY SYSTEMS THINKING.. 12

Two kinds of system (abstract and concrete) 12

Two kinds of state (physical and conceptual) 13

Two kinds of behavior (process and state change) 14

Four kinds of causality. 14

SOCIAL ENTITY THINKING.. 14

Two kinds of social system (actor centric and activity centric) 15

Two kinds of freedom (to select and to invent) 15

Two kinds of social entity (purposive and purposeful) 16

Two kinds of purpose (intent and outcome) 16

Two kinds of organization (order and social structure) 16

SYSTEM CHANGE. 16

Eight kinds of change. 16

Two kinds of "anti-fragility". 17

Two kinds of agility. 17

Three kinds of self-organization. 17

FURTHER READING.. 18

 

MEADOWS’ THINKING

 

"Thinking in Systems: a Primer" (Meadows) is a good and popular introduction to systems thinking.

This section focuses on some ambiguities in it.

It is all too easy to slip from activity system thinking 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.

 

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

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

This table aligns Meadows terminology with general activity system theory,

 

General activity system theory

Meadows’ system dynamics

Aim

Actor

Activity

Interaction

Line of behavior (state change trajectory)

Function or purpose

Element

Behavior

Interconnection

Pattern of behavior over time.

 

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

She related 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.

Three kinds of thing in a system

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

Chapter one 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.

Did she intend 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.

 

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).

“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: may be read as

·       physical material flows, or

·       logical data flows (signals or messages), or

·       cause-effect relationships (sometimes corresponding to batches of events).

“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.”

 

Functions or purposes: may be read as

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

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

·       outcomes, the consequences of system beahvior

“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.”

 

Meadows' Primer in Systems Thinking appears to use the terms function, purpose and goal both interchangeably, and with different meanings.

The authors slip from one context to another.

The first half discusses activity systems (networks of activities), in which purposes are outcomes (POSIWID).

“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”

 

The second half discusses social entities (networks of actors), in which purposes are motivations.

"one of the most powerful ways to influence the behavior of a system is through its purpose or goal"

 "a change in purpose changes a system, even if every element and interconnection remains the same".

 

We can't have it both ways.

Either purposes are inexorable outcomes of systems, or they are what we intentionally direct or shape a system to do.

Surely better to say:

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

By changing its purpose(s) you can change the activity system(s) that a social entity realizes, and the outcomes it produces.

System dynamics 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 is 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 comments, explored below.

On “coherently organized”

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

This opening sentence in Meadow’s Primer on Systems Thinking is often quoted but generalized to the point it is variously interpretable.

 

What does “coherently organized” imply here?

A hierarchical management structure in which actors are told what to do?

An anarchical structure in which autonomous individuals determine their own activities?

A causal loop structure in which actors are triggered by flows to perform activities?

Probably, all those structures can be found at once in a real-world social entity, like IBM.

 

Meadow was primarily interested in systems modellable in a causal loop diagram (CLD).

Now consider two different ways actors may interact in a given structure.

 

Causal flows of the kind in a CLD

Causal flows organize populations of actors and resources by connecting them in causal relationships.

In this case, coherently organized may be read as saying actors and resources are inter-related as populations in a CLD.

And to achieve something may be read as POSIWID, as producing "a pattern of behavior over time" rather than as meeting given or agreed aims.

 

Denotic causal flows between actors in a management structure.

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

Whereas causal flows trigger actors to perform defined activities, denotic causal flows can define the activities to be performed. Or even tell actors define their own activities.

In this case, coherently organized may be read as saying actors are directed by managers by denotic information flows.

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

 

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

However, denotic causal flows (telling actors what to do) may have effects contrary to those causal flows.

 

Different observers may observe countless different casual loop networks in a business, such as IBM.

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

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

 

In chapter 7, Meadows does discuss the importance of verifying system models against real world phenomena.

“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."

 

GENERAL SYSTEM THEORY

Three kinds of emergence (evolution, hierarchy and interaction)

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

Some systems thinkers see this as the concept most definitive of systems thinking.

This table (bottom to top) presents a history of the universe from the big bang to human civilisation.

 

THINKING LEVEL

Elements or actors

Human civilisation

Human organizations

Human sociology

Humans in groups

Social psychology

Animals in groups

Psychology

Animals with memories

Biology

Living organisms

Organic chemistry

Carbon-based molecules

Inorganic chemistry

Molecules

Physics

Matter and energy

 

Bertalanffy’s writings were influenced by thinking of a biological organism as a system.

In relation to this table, we can identify three kinds of emergence, and three misconceptions about it.

 

There are three kinds of emergence.

·       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

 

This section supplements the earlier discussion with some notes on the first kind of emergence

This idea is widespread outside of systems thinking.

"Today, it’s a real intellectual deprivation to be blind to the marvellous vision offered by Darwinism and by modern cosmology

– the chain of emergent complexity leading from a ‘big bang’ to stars, planets, biospheres,

and human brains able to ponder the wonder and the mystery of it all."

Quoted from this essay on science.

 

Gustavo Romero wrote that reality seems to be organized into.at least 6 levels.

"In order of increasing complexity:

  1. ontological substratum [basic events];
  2. physical [material];
  3. chemical;
  4. biological;
  5. social;
  6. technical."

From “On the ontology of space-time

 

Romero's level 1 "is formed by basic events and precedes the emergence of physical things at the physical level."

This reflects the presumption that events come before entities.

Every entity in the universe is created, changed and destroyed by events.

What we perceive as persistent structures are the transient side effects of behaviors.

An entity can be modified by events in ways that change its state (e.g. cold to hot), or change its type or the activities it can perform (e.g. caterpillar to butterfly).

 

Levels 2 to 5 each contain a collection of entities and events that share certain properties and are subject to certain laws.

At level 2, matter and energy obey the laws of physics.

At level 3, molecules interact according to the laws of chemistry.

At level 4, animals embody biological processes.

At level 5, human behavior requires interactions between

·       chemicals at the chemical level

·       synapses at the biological level

·       perceptions and memories at the psychological level

·       people at the social level.

Each step up to a higher level adds some emergent properties and laws - adds complexity you may say.

 

Level 6 is oddly placed in terms of complexity.

Consider a pendulum, a very simple technology, a physical system that operates at level 1.

That it was designed by a person does not make it more complex than a person or a society.

 

For the purposes of this work, the table below adapts and extends Romero's levels.

 

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

 

 

It seems reasonable to say that complexity (in the orderly rather than messy sense) increases or emerges when moving from a lower level of thinking to a higher level.

Chemical evolution after the big bang complexified the periodic table and the range of possible chemical reactions.

Biological evolution might be described as a process that tends to complexify the organisms of a species, and widen the range of an animal's possible behaviors.

Human evolution has created actors who can envision and produce new things (e.g. paintings), new activities (e.g. card games), new tools and new software systems.

Three misconceptions about emergence (manyness, surprising, complex)

Emergence does not require a system to have many actors

Consider the progress of a rider on a bicycle.

 

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

Designed systems are intentionally designed to produce specified results or effects.

 

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

Because we ignore the internal complexity of whatever parts we select for discussion.

 

In this simple system

We ignore the complexity in

A rider riding a bicycle

the biology of the rider

Planets orbiting a star

the substance and surface of each planet, and life on earth.

A game of poker

the biology of the card players

 

By the way "complexity science" is a heading for jumble of ideas from mathematics to sociology.

This makes it difficult to define the whole field in a coherent way.

For more on complexity science, catastrophe and chaos read this chapter.

Two kinds of holistic thinking (analysis and design)

Holism: studying or describing how things interact (rather than studying and dissecting each thing on its own).

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

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

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

 

Holism not = wholeism

We can never study or describe all that could be known about a thing.

We must necessarily select the aspects of that thing relevant to our interest in it.

The bigger the parts selected, the less we know of what they contain.

The narrower the selection of parts, the less we know of the parts not selected for attention.

 

Holism in system analysis

Holistic thinking can mean looking for causes and rescoping systems

It can mean looking outside the boundary of a given system for the cause of an effect.

And then rescoping the system of interest.

 

Holism in system design

System design is holistic by definition.

The requirement for a to-be designed system is to produce emergent properties or effects.

Systems designers must both

a)     divide the to-be system into parts (subsystems or actors) and

b)     design how those parts interact to produce the required emergent properties.

If any part of the system could produce the emergent properties on its own, the rest of the design would be redundant.

Two kinds of organization (structural and behavioral)

"systems theory focuses on the arrangement of and relations between the parts which connect them into a whole.

This particular organization determines a system” Principia Cybernetica Web

 

What does "organization of parts" mean?

Does it refer to structure in which the parts are connected?

Or the processes that those parts must play roles in?

 

Organization as a structure - a set of connected parts

The study of whole-part relationships is called mereology.

Mereological discussions refer to things - like a chair or an organism - as a structure composed of inter-connected parts.

Mereologists have to address the questions below.

 

What is the scope of the whole?

Bertalanffy had studied biology.

Bounding an organism is easy.

You can see or touch the boundary of the organism - its skin.

By contrast, how to determine the scope of the solar system, a bank, or a legal system?

 

What counts as a part?

You naturally see everything inside the skin of an organism as a part of that whole.

By contrast, how to determine which actors belong to a social entity?

Every member of a family, tribe or species? Every actor within a geographical area?

Every actor connected by physical contact to other actors?

Every actor connected by communication in a "community".

 

Or every actor with a membership number?

 

Can one part be a member of several wholes?

Can those wholes be in conflict with each other?

Can parts have degrees of membership from loose to strong?

 

In what sense does a whole "contain" its parts?

Your body contains organs and cells that have no role outside of your body.

But a game of poker does not contain card players.

They have lives outside of playing cards, and play roles in countless other systems.

And even while playing cards their minds may stray to their roles in unrelated systems.

 

A system thinker has proposed “by understanding its structure, we can understand the states a system will exhibit.”

To the contrary however, for example, the organization chart of a business does not explain the processes it performs or their results.

 

Mereology is insufficient as a basis for understanding the activity systems of interest to us.

Focusing on structures leads some to confuse a physical entity with the activity systems it may realize.

And/or confuse actors with roles they play in different systems.

 

Organization as a behavior - a set of interactions

How actors connect in a structure is one thing.

How actors interact is another.

 

Find and watch a video of a “double pendulum".

Its structure is very simple - just two components - connected at one point.

Understanding its structure does not help you predict its behavior or the states it will exhibit.

The double pendulum looks to be a very simple machine.

Yet it displays what some might call "complex" or "chaotic" behavior.

Two kinds of part (passive and active structure)

Structures can be classified in many ways; we must at least distinguish active structures from passive ones.

 

Passive structures

Is every entity divisible into related parts a system?

A garden fence and a pack of cards are structures composed of related parts; but they are passive rather than active.

The chemists' periodic table of elements is another passive structure.

It is a pattern, it is interesting, and some may call it a system.

But it is not a system of interest in the sense here.

https://www.sciencekids.co.nz/pictures/chemistry/periodictable.html

 

Active structures

Active structures are actors who can not only connect in some way, but also interact to advance the state of a system.

 

The systems of interest both include active structures, and may be seen as active structures.

Think of:

 

Bear in mind the passive/active distinction is a matter of perspective.

A sandstone pebble is an entity composed of sand or quartz grains connected by cement.

It is a discrete entity - created, changed and destroyed by events.

We normally see it as a passive structure.- as acted on rather than an actor.

However, it can be recast as an active structure; it can be presented as dynamic system that absorbs and excretes a small amount of water.

 

Narrative and mathematical descriptions are passive structures.

However, some descriptions can be animated.

Human and computer actors not only read descriptions of activities to be performed, but also perform those activities.

Two kinds of actor (automaton and agent)

Automatons are actors that have no choice other than to follow given rules.

If they do select from a fixed range of activities, then it is by following some deterministic rule.

Or else by making a random choice (perhaps qualified by some degree of probability).

 

Agents are actors that can choose to exercise some degree of autonomy.

The term agent coves a spectrum of possibilities, since an agent may be free to

 

At the extreme, an agent may be entirely self-serving.

And act in ways that are regardless of any other actor or other actors' aims.

 

ACTIVITY SYSTEMS THINKING

Two kinds of system (abstract and concrete)

Remember that holism not = wholeism.

When people speak of a system, they may speak of

·       an entity - the whole of a thing or situation, 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 system theory

 

Actually, we need to distinguish four concepts

 

Concept

Example

observer

a card game designer

abstract system

the rules of poker

physical system

a game of poker

Social entity

a card school with a pack of cards

 

Two kinds of state (physical and conceptual)

The passage of time is revealed by changes.

The universe is an ongoing process in which the state of things changes over time.

 

E.g. Consider a tennis match.

At any moment in time, the players, the balls and the tennis court have a current material or physical state, which we don't attempt to measure.

At the same time, the tennis match has the conceptual state shown on the score board.

In practice, we represent the physical state of a thing as a conceptual state, as a vector containing the values of state variables.

 

E.g. In physics, the vector of an object might contain its spatial coordinates at a moment in time.

And in economics, the vector might contain the current inflation rate and unemployment rate.

A graph can be drawn to show the trajectory of change to a state variable's value over time, be it continuous or discrete.

 

 

Suppose the vector has only two or three numerical variables.

Its value at any time can be represented as a point on a two or three-dimensional graph.

And the trajectory of the vector over time can be shown visually as two or three-dimensional shape.

 

[Examples would be nice here.]

 

Systems thinking is concerned with things whose state vector changes over time.

Some things advance continually from one state to the next.

E.g. a moon rocket, or a tennis match.

 

Some things cyclically return to the same point, or stabilize themselves in an equilibrial state.

E.g. a solar system, or a thermostat-controlled room temperature.

 

An attractor is a vector value to which a system is drawn (given various starting conditions).

Systems with a vector close to an attractor tend to remain close it, even if slightly disturbed.

The attractor can be a point, a line, a curve, or a multi-dimensional manifold.

It can even be a set with a fractal structure - known as a strange attractor.

Two kinds of behavior (process and state change)

In business and software architecture, a behavior is usually a process.

Any action, activity, operation or procedure that takes time to perform.

Perhaps shown in some kind of flow chart.

 

In system dynamics, a behavior is a state change trajectory - a “line of behavior”.

It shows how the value of a state variable changes over time.

Four kinds of causality

Ashby discussed how a system may respond to input events or disturbances in two ways – deterministic and stochastic.

You may read "input" as being an event or a condition.

 

Here, causality (or cause-to-effect processes) is classified into four kinds.

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

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

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

4.     Innovative means that given input A, an intelligent actor/agent invents a new action, outside of any range that has been considered or modelled so far.

 

Activity systems thinking (as in cybernetics) addresses 1, 2 and 3.

Even a possibilistic process is "regular" in the sense that an actor is constrained to choose between a defined range of possible actions.

 

SOCIAL ENTITY THINKING

 

Sociology may address all four kinds of causality above.

Whether our psychology is deterministic or not at the biological level is irrelevant.

At the sociological level, we must treat people of sound mind as having free will.

 

So, where the actors in a system are anthropomorphic rather than computational, we assume they can not only choose between options (3) but also be innovative (4).

E.g. in a poker game, the range of actions is limited to those that characterize the system.

The rules do not tell players whether to "call", "raise" or "fold".

Players strive to make their choices unpredictable.

They also try to detect probabilities in how others choose between the possible, allowable, actions.

 

Outside of their role in playing cards, the same human actors are innovative; they invent new responses to events and conditions.

Where actors do invent actions, a social entity is not well called a system.

So, might a complex adaptive system (CAS) with human actors better be called a complex adaptive social entity (CASE)?

Two kinds of social system (actor centric and activity centric)

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

 

Two branches of social systems thinking may be distinguished.

Activity system theory - about regular activities, performed by actors playing roles (e.g. a poker game).

(The actors are changeable, and may act outside the system.)

Aocial entity thinking - about actors, who perform activities (e.g. a card school with a pack of cards).

(The activities are changeable.)

 

Other views are possible (e.g. aim-centric and state variable-centric).

But the dichotomy above is the best explanation of why so much systems thinking discussion is confused or confusing.

 

Why say social entity rather than social system?

Paradoxically, some systems thinkers promote anarchical social structures and/or irregular one-off behaviors.

They propose human actors respond to events and conditions as they choose, learn from experience and respond to novel situations in innovative ways.

Which is fine.

The trouble here is, it undermines the general concept of a "system".

If actors continually exercise their freedom (as autonomous agents) to innovate, then there is no regularity or repetition, and no recognizable activity system.

Two kinds of freedom (to select and to invent)

Freedom might be defined as the degree to which the actors in a system can make decisions and choose between paths.

Ironically, every decision is a constraint in the sense that choosing one path denies another.

 

Freedom to select activities: In an activity system, actors can select from a range of regular activities.

The range is limited to what the system allows.

Giving actors a wider choice of actions, a higher degree of freedom, increases the system’s complexity.

 

Freedom to invent activities: In a social entity, actors may invent their own activities, even their own aims.

They may do this without any overarching change control, and make ad hoc decisions that lead them down novel paths.

Two kinds of social entity (purposive and purposeful)

Ackoff characterized social entities as "purposive" or "purposeful", differentiable thus.

 

The purposes of a purposive social entity lie in the desire of external actors for that entity to produce particular state changes in the state of its environment.

The internal actors may be seen as slaves to that end.

 

The purposes of a purposeful social entity are found in the desire of its internal actors to produce internal and/or external state changes, as they choose to do.

For more on Ackoff's classification scheme read this other article.

Two kinds of purpose (intent and outcome)

Purposes can be intentions - aims we have - reasons to do things.

Or else, purposes can be uses we find for things, and outcomes of systems we observe.

The distinction is:

·       Purpose as motivation

·       Purpose as outcome (POSIWID)

 

We can't have it both ways

One of the most powerful ways to influence the behavior of a social entity is through its purpose or motivation.

By changing its purpose(s) you can change the activity system(s) that a social entity realizes, and the outcomes it produces.

Two kinds of organization (order and social structure)

In social entity thinking, the term organization is used to mean two different things

 

The management structure of a social entity under which human actors are arranged to perform the required activities.

 

A pattern of behavior, a repeated pattern of inter-actor communication in a group or network of actors from which properties emerge.

 

SYSTEM CHANGE

Eight kinds of change

System change can be classified in three ways:

·       continuous or discrete

·       state change or mutation

·       natural/accidental or designed/planned.

 

Of eight potential varieties of change, four are continuous and four are discrete.

 

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

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

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

4.     Continuous designed mutation (impossible)

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

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

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

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

Two kinds of "anti-fragility"

The terms robustness and resilience (or some say, antifragility) are used variously with reference to how a system is designed or evolves to survive.

The terms are perhaps most simply distinguished as follows.

 

To survive in the face of a changing environment or disruptive input.

A robust system handles disruptive or unwelcome events and conditions (think, homeostasis, or immunity to infection).

A resilient system mutates to handle new events and conditions (think (evolution).

Two kinds of agility

Agile system mutation

Agile system development implies a designed system mutates in small ways, and frequently, from one generation to the next.

 

Agile activity system

An agile activity system is one that can handle changes in its environment, without having to mutate when the environment changes.

Three kinds of self-organization

Discussions of self-organization can refer to at least three kinds of change; the last is the most interesting here.

 

Goal-seeking state change.

An entity is drawn to one or more "attractor" states and resists being moved from such a state - as in homeostatic biological and electro-mechanical control systems.

 

Self-assembly or growth.

This is another kind of state change.

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.

 

Self-improvement.

This is a kind of state mutation.

Self-improvement implies changing from "bad to good" in some way.

Ashby's way of thinking about this is discussed in other chapters.

 

FURTHER READING

 

An influential or oft-quoted teacher or expert is often called a guru.

Systems thinking gurus include Ackoff, Ashby, Beer, Capra, Checkland, Churchman, Forrester, Jackson, Meadows, Midgely, Ostrom, Senge, Snowden and Von Foerster.

This article has only mentioned a few.

 

First, two primary sources and my analysis of them.

 

Ref. 1: Ashby’s “Introduction to Cybernetics” 1956.

For my analysis, read Ashby's six core ideas.

 

Ref. 2: Ackoff’s “Towards a system of system concepts” 1971.

For my analysis, read Ackoff's ideas.

And in the first third of this article, Ackoff says a few things disputed above.

 

Next, some notes supporting this article.

 

Ref. 3: On Bertalanffy, here are some interesting notes I don't entirely agree with:

On the history of Ludwig von Bertalanffy’s “General Systemology”, and on its relationship to cybernetics.

 

Ref. 4: This companion article compares social entity thinking and activity systems thinking.

 

Ref. 5: On CHAT, from Wikipedia.

"Cultural-historical activity theory (CHAT) is a framework which helps to understand and analyse the relationship between the human mind and activity…

Core ideas are:

1) humans act collectively, learn by doing, and communicate in and via their actions;

2) humans make, employ, and adapt tools of all kinds to learn and communicate; and

3) community is central to the process of making and interpreting meaning – and thus to all forms of learning, communicating, and acting."

 

Finally, related articles include 1 and 6 in “the book”