Systems thinkers and their approaches

(How systems thinkers came to eviscerate the system)

Copyright 2017 Graham Berrisford. One of about 300 papers at http://avancier.website. Last updated 22/07/2017 10:21

Contents

System thinking approaches. 1

Classical general system theory and cybernetics. 2

Class 1: “Hard systems thinking” approaches. 6

Class 2: “Soft systems thinking” approaches. 9

Class 3: “Critical systems thinking” approaches. 15

Is the three-way classification a progressive history?. 16

Conclusions and afterthoughts. 17

 

System thinking approaches

What people call “systems thinking” runs from the sciences to the humanities: maths > physics > chemistry > biology > psychology > sociology > politics.

There is a spectrum from the most scientific of engineering to the most political of management consulting.

 

“There are many different ways of classifying systems.” Ackoff 2003.

A naive classification, for example, could divide systems into machines, organisms and societies.

 

Social systems thinkers like to classify not only systems, but also approaches to thinking about systems and solving problems.

This three-way categorization is copied from another source.

Class 1: Hard systems thinking approaches

Class 2: Soft systems thinking approaches

Class 3: Critical system thinking approaches

1956 General Systems Theory (Bertalanffy)

1956 Classical (classical) cybernetics (Ashby)

1957 Operations research (Churchman et al.)

1962 Systems engineering (Hall)

1963 Socio-technical systems (Trist et al.)

1965 RAND-systems analysis (Optner)

1971-72 System Dynamics (Forrester; Meadows et al.)

1971 Inquiring systems design (Churchman).

1972 Second order cybernetics (Bateson)

1972 Soft systems methodology (Checkland)

1981 Interactive management (Ackoff)

1981 Strategic assumption surface testing (Mason and Mitroff)

1988 Cognitive mapping for strategic options development and analysis (Eden)

1983 Critical system/situation heuristics (Ulrich)

1990 System of systems methodologies (Jackson)

1990 Liberating systems theory (Flood)

1991 Interpretive systemology (Fuenmayor)

1991 Total systems intervention (Flood and Jackson)

2000 Systemic intervention (Midgley).

 

This paper reviews this classification, with reference to several of the authors listed.

It looks at each kind of approach with reference to the concept of a system that is found in general system theory and classical cybernetics.

It challenges using the labels “hard” and “soft” to distinguish classes 1 and 2, and proposes a different, more schismatic, distinction.

It challenges the suggestion that there is a progression from class 1 to class 3.

It presents class 3 not as an advanced form of general system theory, but as departing from it and eviscerating the concept of a system.

 

It is easy confuse classifications of systems thinking approaches with classifications of systems.

This paper regards a systems thinking approach as a meta system to any system it is used to analyse and/or design.

Classical general system theory and cybernetics

Before discussing systems thinking approaches, the basic ideas of general system theory ought to be set out.

“The principal heuristic innovation of the systems approach is what may be called ‘reduction to dynamics’ as contrasted with ‘reduction to components’ ” Laszlo and Krippner.

Bertalanffy: general system theory

Ludwig von Bertalanffy (1901-1972) was a biologist who established the idea of a general system theory, soon after the second world war.

He looked for what is common to systems in different sciences.

You may recognise that systems are generally described in terms of aims (motivations), behaviors (processes) and structures (actors or components).

This table illustrates these system concepts.

System concept

Example

Meaning

Aims

Win the world cup

Target outcomes, which give an actor a reason or logic to select and peform behaviors.

Behaviors

Compete in world cup matches

Processes, which run over time towards a final aim.

Active structures

Players in a national football team

Nodes (related in a hierarchical or network structure) that perform activities in behaviors.

Passive structures

Pitches, footballs

Objects acted upon during behaviors.

 

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

System environment: the world outside the system of interest.

System boundary: a line (physical or logical) that separates a system from is environment

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

Information theory

Bertalanffy related GST to communication of information between the parts of a system and across its boundary.

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

 

Information: a meaning created or found by an actor in any physical form that acts as a signal.

Any description or direction that has been encoded in a signal or decoded from it by an actor.

 

Signal: any structure of matter or energy flow in which an actor creates or finds information.

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

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

 

Information flow (aka message): physically, a signal passed from sender to receiver, logically, a communication.

Information state (aka memory): see “state”.

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

Weiner: cybernetics

Norbert Wiener (1894-1964) founded cybernetics – about how regulators monitor and control behaviors using feedback loops.

Information feedback loop: the circular fashion in which system inputs influence future outputs and vice-versa.

 

In 1948, he published Cybernetics or Control and Communication in the Animal and the Machine.

The phrase “control and communication” highlights the importance of information flows.

The phrase “the animal and the machine” suggest the principles apply to both animate (inc. human) and inanimate (inc. computer) systems.

 

Cybernetics has influenced systems thinking in general, and business system thinking in particular.

Nowadays it seems trite point out that a business system is connected to its wider environment by feedback loops.

It monitors and directs entities in its environment; and gathers, stores and produces information to do this.

Ashby: cybernetics

W. Ross Ashby (1903-1972) was a psychologist and systems theorist.

“Despite being widely influential within cybernetics, systems theory… Ashby is not as well known as many of the notable scientists his work influenced.

W Ross Ashby was one of the original members of the Ratio Club, who met to discuss issues from 1949 to 1958.” Wikpedia 2017

 

In “Design for a Brain” (1952), Ashby presented the brain as a system.

He eschewed discussion of consciousness; his basic idea might be distilled into one sentence thus.

A brain is collection of brain cells that interact to maintain body state variables by sending/receiving information to/from bodily sensors and motors.

 

The GST ideas of system state and information flows are central to cybernetics.

Ashby saw the brain as a regulator that maintains a body’s state variables in the ranges suited to life

He presented the brain-body relationship as an information feedback loop.

A brain holds (or has access to) an abstract model of the body’s current state.

The brain receives information from sensors, and sends instructions to motors and organs.

The aim is homeostasis – to maintain the state of the body - and so help it achieve other desired outcomes.

 

In “Introduction to Cybernetics(1956), Ashby declared that:

“Cybernetics does not ask "what is this thing?" but ''what does it do?" It is thus essentially functional and behavioristic.”

“[It] deals with all forms of behavior in so far as they are regular, or determinate, or reproducible.” Introduction to Cybernetics (1956) W. Ross Ashby)

 

System: a structure whose elements interact (directly or indirectly) in regular behaviors.

For example, regular, or determinate, or reproducible behaviors can be observed in:

·         A biological organism in which cells play roles in processes, which sustain the organism’s body or help it reproduce.

·         A society in which actors play roles in the performance of processes, which sustain the group or serve other purposes.

·         A software system in which objects instantiate classes in the performance of operations, which update memories and send messages.

·         A business in which changes in one stock level lead to changes in another stock level - as in System Dynamics.

Forrester: System Dynamics

Activity systems feature regular, or determinate, or reproducible behaviors.

They are dynamic, meaning the state of the system changes over time in response to events or forces.

Forrester (1971) promoted System Dynamics as tool for system observers to describe system behaviors in terms of stocks and flows.

A stock is a dynamic set of things – it has a number of members - a quantity - a stock level.

A flow between two stocks represents events that change stock levels over time.

 

For example, increasing the stock of plants decreases the stock of carbon dioxide and increases the stock of oxygen.

Increasing the stock of animals does the reverse.

 

For example, increasing the stock of sheep increases the stock of wolves.

Increasing the stock of wolves decreases the stock of sheep.

Short-term interactions between these two stocks are “deterministic”.

However, the long-term impact if these interactions on their populations can be “chaotic” rather than homeostatic.

 

(Later papers compare supposedly continuous system dynamics with discrete-event driven dynamics.

In practice, continuous dynamics is commonly simulated using discrete dynamics, by treating short time units as events.)

Systems as conceptualizations

Many systems thinkers fudge the distinction between system descriptions and physical realizations of them.

Abstract system description: a description or model of a concrete system.

Concrete system (aka System): a system that runs in reality (and is testable against its description).

 

Ashby was keen we separate abstract system descriptions from concrete entities that realise them.

“Cybernetics depends in no essential way on the laws of physics or on the properties of matter.” Ashby 1956

He said a real-world entity is not a system per se; it is only a system in so far as it performs the behaviors in an abstract system description.

Abstract system description

Theoretical system

System description

Concrete system realization

An empirical system

Real world behaviors

 

This triangle further separates system describers from system descriptions and the real world behaviors they observe.

Ashby’s cybernetics

System descriptions

<create and use>                  <realised by>

System describers <observe and envisage> Real world behaviors

 

For example, we can separate the US founding fathers (and their successors) from the US constitution and its realization by actual federal governments.

US government

US constitution

<created>                        <realised by>

US founding fathers  <envisaged>       US governments

 

The US constitution defines the roles and rules of the essential actors in the US federal government system.

The roles include the Congress (the legislative branch), the President, the court system (the judicial branch) and the States.

It also defines relations between actors playing those roles.

(It does not define the roles or rules of subordinate institutions created by federal governments.)

 

Different people may conceptualise the same named entity as different systems - or no system at all.

A particular (concrete) federal government is a system when its actors performs behaviors described in the generic (abstract) US constitution.

However, the same federal government may perform other behaviors, and may be conceptualised from a different perspective as a different system.

 

Note: The US constitution also defines the meta system to be used (by system thinkers who succeed the founding fathers) to amend the constitution.

 

This Glossary contains more general system theory terms and definitions than we have space for above

Introducing General System Theory contains deeper exploration of system theory concepts in the context of particular sources.

Class 1: “Hard systems thinking” approaches

A “hard” systems thinking approach applies ideas found in general system theory (after Bertalanffy) and classical cybernetics (after Weiner and Ashby).

How does a “hard” systems thinking approach work?

There are natural systems and designed systems.

Natural system: a system that runs before it is described by man.

Designed system: a system described by man before it runs.

System design typically runs from requirements analysis, through system description, building and testing to roll out.

 

Even mechanical systems are designed to meet different goals, some of which conflict.

So engineers are taught processes such as:

1.      Study the current situation, learn the requirements and concerns of stakeholders

2.      Outline optional solution visions, along with value propositions for each stakeholder

3.      Analyse trade offs between stakeholder perspectives and solution options with respect to goals and constraints

4.      Describe a target system that compromises between conflicting goals and constraints

5.      Plan the work to move from the current to the target system

6.      Follow the plan to build and test the target system

7.      Roll out the target system.

 

The fact that stakeholders approve a target system description doesn’t mean they share the same perspective of, or purpose for, the system.

Boulding: system theory in management science

Bertalanffy, Boulding and others established the Society for the Advancement of General Systems Theory in 1954.

Kenneth Boulding (1910-1993) wrote in 1956 about applying GST to “management science”.

 

Boulding on society

Boulding described social systems at two levels, populations and individuals.

He said behaviors performed by an individual include joining or leaving a population, processing information and communicating meaning to others.

Also, remembering and acting on mental images (internal state data), transcribing mental images into historical records and restoring system state to some kind of norm.

In system theory terms, his society of humans might be described thus.

A population of individuals that interact by processing information in the light of mental images they remember, and exchange messages to communicate meanings to other individuals and related populations.

 

Boulding on individuals as deterministic

Like many early systems thinkers, Boulding was concerned with how systems maintain their state.

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

He accepted the principle of GST that the state of an entity intervenes between stimulus and response.

Also, that a human being is deterministic.

Deterministic: the quality of a system that means its next state is predictable from its current state and input event.

A deterministic system, in a given state, will respond to a stimulus by acting in a predictable way.

This is true, for example, of systems described by sociologists, biologists, psychologists and control system engineers.

 

Domain

actors

act

in the light of

Biology

cells

respond to chemical/electrical signals

their current chemical state.

Machine regulators

components

send messages to devices

messages received about environment state changes.

Computing

objects

perform operations in response to messages

current state variable values.

Entity/event models

entities

change state in response to events

current state variable values.

System dynamics

stocks

change volume in response to event flows

current stock volumes.

Sociology

people

communicate in response to messages

their memories (or “mental images” as Boulding called them in 1956).

 

Boulding presumed that a human actor, in a given state, will respond to a stimulus by acting in a predictable way.

He said the difficulty with applying GST is that an actor’s state data (their “mental images”) is unknowable.

And since you cannot know the state of an actor, you cannot predict an actor’s response to an event.

 

Boulding didn’t mention that distributing a population’s state data between individual actors makes it hard to maintain the integrity of a population.

And difficult to collect management information about it.

(Just as distributing a software system’s state data between individual objects makes it hard to maintain the integrity of the system, and collect management information).

 

Boulding on social systems as complex

Boulding classified systems into nine kinds, presenting them on a scale from “simple” to “complex”.

He placed social systems at level 8, below “transcendental” systems at level 9.

Beware! Terms are used glibly in systems thinking discussion, sometimes contrary to how a scientist would use them.

When listing system types from simple to complex, Boulding presumed that a higher or wider system must be more complex than its component subsystems

And particularly, that human social systems are an especially high level and complex kind of system.

 

Boulding’s presumptions do not hold

First, because there is no recognised way to measure the complexity of a thing.

Bertalanffy said system elements can be counted.

“In dealing with complexes of 'elements', three different kinds of distinction may be made: according to their number; their species; the relations of elements.” von Bertalanffy

But there is no agreement about how the counting system elements and relationships (types and/or instances) could be used to measure complexity.

 

More importantly, you can only measure what you can describe.

And the internal structure of atomic elements in a system description must be ignored.

Describers relate the atomic structures (actors) in larger structures, and relate atomic behaviors (actions) in longer processes.

The system’s complexity is found in how atomic elements are organised in higher structures and longer processes.

It is not only normal but necessary to disregard the internal complexity of atomic elements when measuring the complexity of a whole system.

And thus, a higher or wider system can be simpler than its component subsystems.

 

Boulding on roles and actors

The actors in examples above are not dedicated to any one system; they can act (in different roles) in countless higher/wider systems.

The “parts” of an active social system are not so much the actors, as the performances by those actors of actions in system roles.

Like Weber before him, Boulding suggested the essential business system element might be roles rather than actors.

But whether he took a role-centric or actor-centric view of systems was not entirely clear.

And ever since Boulding, social systems thinkers have tended to confuse those two viewpoints.

Is a “hard” systems thinking approach related to any particular system class?

Gerald Midgley (2000) said “hard” systems thinking is about systems classifiable as having a “unity of purpose”.

Yet the purposes of a thing are subjective; and even to one observer, a thing can have different purposes in different contexts.

A cuckoo clock that fails to keep time well may be cherished for its alarm function, or its charming design.

 

Bausch (2001) alluded to Churchman, Ackoff and Checkland as the triumvirate who created the “soft systems methodology”, wrote:

“[They] consider hard systems methodology to be a special application of systems theory in situations where the objectives are not in question”

Yet the objectives of system sponsors, stakeholders and individual participants are well-nigh always questionable.

Even mechanical engineers are taught to manage stakeholders and trade off between conflicting goals.

It would be more accurate to say the reverse, that social system theory (SST) is a special version of general system theory (GST).

The unforeseen success of GST ideas in the digital transformation of business processes

It is said that the most complex system in nature is the human brain.

By any measure, today, the most complex systems humans design are software systems.

 

Unforeseen by Bertalanffy, Weiner and Ashby, software systems are applications of their core GST ideas.

A population of objects that interact by processing information in the light of state data they remember, and exchange messages to communicate with other objects and system users.

We don’t require that every concrete activity system matches an abstract system description perfectly.

But software systems are perfect in the sense that, at run time, they can do only what is described in their code.

 

As in classical cybernetics, a software system is typically designed to monitor and/or inform entities in its environment.

This has enabled the information feedback loops that connect a business with entities in its environment to be digitised.

Thus, the application of GST ideas in software systems has had a profound impact on society.

The regular behaviors of businesses and other social systems have increasingly been automated, supported and enabled by computers.

Half a century after we entered the “Information Age”, people talk of using software systems to make “digital transformations”.

In the future, transformations will increasingly involve artificial intelligence.

Class 2: “Soft systems thinking” approaches

Most soft systems thinking is about a government or business system in which the actors include humans.

Soft system thinking approaches are designed for use by the analysts, designers and planners of these systems.

The aim is to help them to study a problem situation, capture requirements, model systems and propose changes to them.

 

Churchman, Checkland and Ackoff were notable contributors to soft systems thinking.

Churchman initially wrote for actors in what was then commonly called the operations research department.

Operational researchers act as a meta system to business systems.

They study regular business operations and propose how to optimise or otherwise change them

Operational research

System descriptions

<create and use>                    <realised by>

Operational researchers <observe and envisage> Business operations

 

Hmm… this triangle looks like the one for Ashby’s cybernetics.

This section challenges the notion that soft systems thinking is significantly different from hard systems thinking.

It points to “second-order cybernetics” as being far more significantly different.

Soft systems

The term “soft system” can be read as having two very different meanings.

To some, it means an empirical or concrete system in which human actors play roles.

To others, a soft system is a theoretical or abstract system, as described by an individual or group.

Remember Ashby said infinite theoretical/abstract systems may be abstracted from any empirical/concrete entity.

Similarly, Checkland defined a system as a perspective of a reality, a world-view or “Weltenshauung”.

 

Checkland (1981, p. 102.) wrote:

“Implicit here is the notion that an observer engaged in systems research will give an account “of the world, or part of it, in systems terms; his purpose in so doing; his definition of his system or systems; the principle which makes them coherent entities; the means and mechanisms by which they tend to maintain their integrity; their boundaries, inputs, outputs, and components; their structure.”

 

In other words, system describers describe a real world system as: a coherent entity, a structure of components that interact in mechanisms that maintain the integrity of the entity, and send/receive inputs and outputs to/from each other and external entities

The table below maps that definition, along with those of Ashby, Boulding and object-oriented software, to one generic structure.

Generic structure

Ashby’s design for a brain

Boulding’s social system

Checkland’s Soft System

An object-oriented software system

Active structure

A collection of brain cells that

A population of individuals that interact by

A coherent entity, a structure of components

A population of objects that interact by

Behavior

interact in processes to

processing information in the light of

that interact in mechanisms that

processing information in the light of

State

maintain body state variables by

mental images they remember, and

maintain the integrity of the entity and

state data they remember, and

I/O Boundary

sending/receiving information

exchange messages to communicate meanings

send/receive inputs and outputs

exchange messages to communicate

Environment

to/from bodily sensors and motors

to others and related populations

to/from each other and external entities

with other objects and system users

 

All four specific cases fit the generic structure reasonably well.

And in all cases, the real world entity is regarded as a system in so far as it realises a system description.

So, is there any significant difference between hard and soft systems?

 

After several decades, Checkland wrote "Soft Systems Methodology: A Thirty Year Retrospective" (2000)

In his review, he said the hard-soft distinction had proved slippery; people grasp it one week and lose it the next.

He said the term "soft" was intended to describe not systems, but an approach to solving problems in human activity systems.

Which brings us to his methodology.

Soft systems methodology

Checkland developed a soft systems methodology (SSM) that can be summarised thus:

1.      Enter the problem situation.

2.      Express the problem situation (a rich picture)

3.      Formulate root definitions of relevant systems

4.      Represent human activity systems as conceptual models (aka business activity models)

5.      Compare the models with the real world

6.      Define changes that are desirable and feasible

7.      Take action to improve the real world situation.

 

Checkland’s methodology may be represented in our triangle thus.

Soft systems methodology (SSM)

Rich pictures, root definitions & conceptual models

<create and use>                                              <realised by>

Soft system thinkers <observe and envisage> Problem situations and human activities

 

Some see SSM as “situation thinking” rather than “systems thinking”.

However, the aim is to model regular behaviors in a human activity system, and change some of them.

 

Checkland proposed a generic design pattern for structuring a human activity system and information flows within it.

He said the essential functions of a business include

·         defining targets (system aims)

·         operations (system behaviors)

·         monitoring and controlling of operations (cf. system regulation in classical cybernetics, or management in common-speak).

 

Beer proposed a more complex design pattern, called the Viable System Model.

Beer: the Viable System Model

Stafford Beer (1926- 2002) was a theorist, consultant and professor at the Manchester Business School.

He respected Ashby’s cybernetics, but was focused more on what might be called “management science”.

He designed the Viable System Model (VSM) as a tool for diagnosing human organization design issues.

Today, some consultants happily use the VSM as a tool to analyse a business and generate change proposals.

However, quantified evidence for the effectiveness of Beer’s VSM is sparse, and the VSM is not only the viable management structure.

The trouble with the hard-soft distinction

Soft systems thinking is little different from hard systems thinking, because all systems-of-interest are perspectives of reality.

And all system design methodologies involve capturing different perspectives, drawing abstract designs and trading off between different objectives.

 

Checkland proposed SSM starts not with spotting a system to be reengineered but with spotting a confusing or complex situation.

OK, but these are not mutually exclusive starting points – and the end product is indeed a re-engineered human activity system.

 

Checkland said he designed SSM as a “learning system”.

OK, but all traditional system design methodologies include activities intended to help designers learn about the context.

And it is important not to confuse the meta system with the system.

Defining a systems thinking approach as a learning system is very different from defining the target system as a learning system.

 

Which leads us towards second order cybernetics.

Checkland observed the distinction between hard and soft system approaches is slippery.

By contrast, the distinction between classical and second order cybernetics is schismatic.

Social entities v systems

Social system: a system in which animate actors play roles in regular, repeatable processes.

E.g. bees collecting pollen for a beehive; an orchestra’s performance of a symphony.

The symphony score is a system description; every performance of that symphony instantiates that system description in reality.

 

Social entity: a group of actors who may chose their own behaviors, and may interact to reach agreed aims.

E.g. the group of actors hired to play in an orchestra, who may agree to hold a party after the performance.

 

The orchestra’s actors both play roles in a system and belong to a social entity.

The actors cannot perform other symphonies in parallel, because the dynamics of a symphony are continuous.

But other social entities can play roles in different (discrete event-driven) systems - in parallel.

A business can be seen as a social entity in which actors play roles in many systems

Those business systems may be consistent or inconsistent, coordinated or unrelated, or even in competition with each other.

 

System theory is primarily about the roles in the symphony (the system).

That is the sense in which enterprise architecture “regards the enterprise as a system”

“Systems thinking” is more about the actors in the orchestra, and their motivations.

Ackoff: human organizations as purposeful systems

Russell L Ackoff (1919-2009) was an American organizational theorist, operations researcher, systems thinker and management scientist.

He blurred the distinction between social systems and social entities (and so, between classical and second order cybernetics).

 

In“System of System Concepts” (1971), Ackoff began by endorsing Ashby’s and Checkland’s point that a system is a conceptual model of reality.

“Different observers of the same phenomena may conceptualise them into different systems” Ackoff

In other words, observers of one named social entity may describe it as different systems.

Later, Ackoff contradicted himself about the nature of systems: “A church, a corporation or a government agency is a system”.

In other words, one named social entity is one system, regardless of any observer or conceptualization.

Thus, Ackoff confused social systems and social entities.

 

Ackoff’s four-way system classification (which evolved from 1971 to 2003) had some curious features.

E.g. his “animate systems” exclude lower animals; his “social systems” exclude non-human social groups.

But like most social system thinkers, his focus was on human organizations of the kind discussed in “management science”.

 

What did Ackoff mean when he said “All organizations are social systems”?

His primary interest was organizations that employ human actors.

But he was not concerned with informal organizations that have little or no bureaucracy (e.g. a pick-pocket gang, or a choir).

By “organization” he meant a hierarchical bureaucracy that administratively organizes people and their work - in the public or private sector.

And like others in the 1970s, Ackoff considered government institutions to be on the point of collapse.

 

Social systems thinkers presume any such organization or institution can be called a “system”.

Ackoff classified them more particularly as “purposeful systems”.

Meaning that people employed in organizations (being self-aware) have their own purposes, which shape what they choose to do.

 

In 1972, Ackoff wrote a book with Emery about purposeful systems which focused on how systems thinking relates to human behavior.

He defined a human-created system as "purposeful" when its "members are also purposeful individuals”.

These individuals intentionally and collectively formulate objectives and are parts of larger purposeful systems.

He said a purposeful system or individual is also ideal-seeking if it chooses objectives that lead towards as wider and more strategic ideal.

 

Thus, Ackoff promoted a kind of systems thinking that is special to human organizations (rather than general).

"The capability of seeking ideals may well be a characteristic that distinguishes man from anything he can make, including computers".

 

Again, there is a schismatic distinction between:

·         social systems in which actors’ roles and rules are describable

·         social entities in which actors choose their own behaviors in pursuit of some ideal they share.

 

Like many social systems thinkers, Ackoff tended to blur the distinction.

After all, his concern was not to design system roles and rules in detail; he expected system actors to change their behaviors.

His agenda was to rather to bemoan the state of institutions, diagnose problems in them and propose interventions to change them.

Ackoff’s view

Interventions

<propose>                      <to reorganise>

System thinkers <observe and envisage> Purposeful organizations

 

Bateson: second order cybernetics

Bateson (1972) introduced second order cybernetics.

This allowed that system actors can observe the outcomes of past behaviors and choose their own future behaviors.

Like Ackoff, he viewed a social system as containing not only system actors but also systems thinkers.

 

GST and classical cybernetics focus attention on system behaviors ahead of system structures.

And they define systems in terms of general roles rather than individual actors.

 

Second order cybernetics is radically different; it shifts attention:

·         from behaviors to their purposes (aka goals, objectives, targets).

·         from abstract roles to individual actors, who each have their own purposes.

The same schism in management science

This table describes two management styles which correspond to the systems thinking schism above.

Most management involves something of both approaches; they emphasise one or other depending the context.

 

Process-oriented management

Aim-oriented management

Fixed processes.

Behaviors are tightly constrained.

Give people processes to follow.

E.g. Call centre scripts

E.g. Programming tools.

Flexible processes.

Behaviors are loosely constrained.

Give people aims only.

E.g. Door-to-door sales people are given targets

E.g. Software developers hold meetings to revise processes (even revise aims)

GST and classical cybernetics

Second order cybernetics.

 

Of course you can ask a group of people to meet some goals, by doing whatever they choose to that end.

You rely on the individuals’ abilities to interpret your direction and choose behaviors that lead to the goals you set.

If the nature and nurture-given abilities of the actors are up to it, they may succeed, but they may not (for any number of reasons).

 

This group of actors form a social entity in so far as they interact,

In GST terms, they only form a system where, when and in so far as they agree some roles and rules and follow them for a while.

If the actors do agree some roles and rules, the system they describe is probably a simple one.

Whatever else they do – that is ad hoc, irregular, unrepeated, disorderly - is not systematic.

Class 3: “Critical systems thinking” approaches

There is a long tradition of social system analysts and activists proposing and making "interventions" to change social situations.

For example, Oliver Cromwell and Karl Marx could plausibly be described as critical system/situation thinkers.

Critical system/situation thinking

Interventions

<propose>                      <to change>

Analysts & Activists <observe and envisage> Social Situations

 

Critical system thinking approaches are intended to help people solve problems in, or otherwise change, a situation.

The terms “total” and “systemic” in the titles of references above suggest making a root and branch transformation.

If not a revolution, the result is a generational change from a current state of being to a new state of being.

 

The term “critical system thinking” may be applied to using a variety of management consulting tools and techniques.

Using that title implies these approaches inherit from systems thinking approaches discussed above.

But it doesn’t mean the “situation” is a “system” in any sense beyond being a named entity - a named organization or named group of actors.

 

Wherever an intervention involves describing regular behaviors, realizing them and testing the outcomes, it is an application of GST.

But consultants may make interventions of a different kind – with little or nothing by way of process definition or testing of outcomes.

(Even where outcomes are measured, the “Hawthorne effect” always offers an alternative explanation for any improvement measured.)

 

Some modern system thinking discussion is about defining purposes, goals or targets and motivating people.

You may assemble some people and motivate them to work towards given goals, paying little or no attention to how they do the work.

That group is a certainly a social entity, and perhaps a successful one, but it is not a system in the sense Ashby would recognise.

Or else, the meaning of “system” has been so far hollowed out as to derogate from, or even abrogate, GST.

Is the three-way classification a progressive history?

Social systems are sometimes classified into what is presumed to be an evolutionary or historical sequence.

E.g. a history that starts with family groups and clans, and progresses through feudal societies to democracies.

 

Some present such a historical progression as an evolution towards a better or optimal state.

E.g. Kenneth C. Bausch in “The Emerging Consensus in Social Systems Theory” (2001).

Bausch also suggests systems thinkers have a mission to herald a new era of social organization, of advancing participative democracy.

Thus he presents systems thinking as a political movement.

 

Can systems thinking approaches also be classified as an evolutionary or historical sequence?

Look again at the three classes of system thinking approach on the top of this paper.

Notice that the reference dates run suspiciously neatly in sequence from class 1 through class 2 to class 3.

Gerald Midgley (2000) presented three classes as an evolutionary or historical sequence.

He wrote of three phases of inquiry, each relating to a particular focus of the systems field which brought with it a new set of methods.

·         Wave 1 focused on concrete issues of ‘problems’ and problem solutions for issues where there was perceived “unity of purpose”.

·         Wave 2 began with the wider soft systems perspective on people and their perspective on issues.

·         Wave 3 is presented as the latest development in a historical progression.

 

This paper suggests you may view the history of ideas as running in exactly the opposite direction!

·         Critical system thinking resulted in the Magna Carta - a reorganization of England’s government system agreed on 15 June 1215.

·         Social systems thinking (SST) emerged towards the end of the 19th century (Pareto, Durkeim, Tarde etc.)

·         General systems theory (GST) emerged in the middle of the 20th century.

 

Gurus like to present their approach as the latest development in a historical progression.

Modern-day consultants sometimes describe their preferred approach as a kind of systems thinking.

That doesn’t mean their approach represents a historical evolution of GST, or applies its core ideas in a scientific way.

“Critical system thinking” can be seen as a kit bag of management consulting ideas and techniques for “situation thinking” whether there is a described system or not.

Conclusions and afterthoughts

Systems thinkers, from the 1970s onwards, have often claimed institutions are in crisis, and something must be done.

Institutions will always have problems, and "situation thinking” will always be needed.

Management consultants continually generate approaches to identifying problems in a situation (usually an institution) and solving them.

The question here is whether classifying these approaches as varieties of "systems thinking” has any useful meaning.

 

If every entity we name or point to is a system, then the term is meaningless.

Every enterprise or “organisation” employs actors who interact, directly or indirectly.

So, it may rightly be called a social entity. But when can it rightly be called a “system”?

The answer depends on what kind of systems thinker you are.

 

Social entities, social systems and enterprise architecture

There is a schismatic distinction between.

·         A social system - in which interacting actors realise roles and rules - which are describable in accord with GST and classical cybernetics.

·         A social entity - in which interacting actors choose their own behaviors to reach agreed goals - as in second order cybernetics.

Any large business is likely to include several of each, some of them in conflict.

 

Managers may decompose business goals and assign them to individual actors.

Managers can encourage actors to share the goals of the business, e.g. through team building.

They can also help actors reach their own goals, most obviously by paying them.

But if how the actors work is ad hoc, continually changing, then there is no describable system in the GST sense.

 

In the GST sense, a social entity is judged to be a system only where it demonstrably repeats regular processes.

This implies the roles, rules and regular processes of the system can be described.

Enterprise architecture sees a large enterprise as many distinct systems in this GST sense.

These many systems may be uncoordinated; they may overlap, duplicate, conflict and fail to share information.

The ultimate vision of enterprise architecture is to unscramble this mess of systems.

To coordinate, de-duplicate and integrate distinct systems into one system, without duplication or disintegrity.

 

Systems that are changed by design and under change control

There are, broadly speaking, two kinds of change

·         System adaptation: a change to the state of a system

·         System evolution: a change to the nature of a system.

 

Evolutionary biology plays two roles in the papers on this System Theory for EA page.

First, it explains why animals developed the ability to model the world in their minds - which helps us answer philosophical questions about the description-reality dichotomy.

Second, it helps us distinguish evolutionary changes (between system generations) from adaptive changes in a live system (in one generation).

Changing a designed system’s roles and rules is to produce a new system (even if the system’s name and the actors in it remain the same).

Enterprise architecture is about changing a designed system under change control, from one generation to the next.

 

“Complex adaptive systems” that are natural and change continually

The meaning of “complex” is debatable, but in GST terms, a complex adaptive system is a complex evolving social entity.

If it is a system at all, it is probably a simple one.

Ashby would surely distinguish social systems (that realise described roles and roles) from ever-changing social entities (in which actors choose their behaviors).

An organization in which actors can continually change their roles and rules is sometimes called a “complex adaptive system”.

But in classical cybernetics, this named entity is the very antithesis of a system; it is rather an ever-unfolding process.

All that can be described or tested are goals given to the entity by some external observer(s).

 

Revising the three-way systems thinking classification

This table is an attempt to map the three-way system thinking classification we started with the 2-way schism discussed in this paper

3-way classification

About

4-way classification

2-way classification

Class 1 hard systems thinking

Systems in which actors/components perform regular behaviors

General Systems Thinking

General System Theory and

Classical cybernetics

Class 2 soft systems thinking

Systems in which human actors perform regular behaviors

Social System Thinking

Named organizations in which humans determine their behaviors

Social Entity Thinking

Second order cybernetics and

Management Consulting

Class 3 critical systems thinking

Problematic situations

Situation Thinking

 

The schism

The meanings of “system” in classical and second-order cybernetics are radically different.

In GST and classical cybernetics, a system is a conceptualization of some regular behaviors as a set of roles and rules.

If you change the abstract roles and rules, then you change any concrete system that realises that system description.

The system is different, or at least, a different generation.

 

In second order cybernetics, a system is typically a named entity or organization that employs actors.

Actors can act as systems thinkers; they can change the roles and rules of the organization they work in, perhaps even change its aims.

As long the system retains the same name, people think of it as the same system.

 

Bridging the schism

It is possible to recast second order cybernetics in a way that maintains the integrity of the system concept.

To put it another way, GST can be extended to reconcile classical and second order cybernetics.

1.      First, consider system change as incremental (generation-by-generation) rather than continual

2.      Second, separate the meta system M from the system S

3.      Third, allow an actor to switch between roles as system thinkers in M and system actors in S.

 

A new system classification

Here is the latest of several system classifications I have toyed with.

I don’t mean to present this as a scale of complexity, or a progression of any kind.

 

Actors relationship to scripts

Examples

Strong or

Involuntary

System

Actors/parts use no script

Natural

Solar system

Designed

Cuckoo Clock

Actors/parts rigidly follow scripts

Natural

Biological organism

Designed

Software system

Weak or

Voluntary

System

Actors/parts interpret scripts

Designed

Orchestra, Church

Actors/parts script their own roles

Natural

Marriage, Small business

 

How system thinkers came to eviscerate the system

Some suggest modern social systems thinking (SST) derives from, or is an advanced application of, general system theory (GST). E.g.

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

 

Actually, SST approaches both preceded and depart from GST in one or more of the ways listed below.

General system theory (GST)

Can be contrasted with approaches that are

Scientific

Scientistic

General

Specific to situations in which humans interact

About system roles and rules

About individual actors (purposeful people)

About systems (S)

About meta systems (M)

About describing testable systems

About solving a problem or changing a situation

 

This paper has touched on some of these differences, which are further explored in related papers at avancier.website.

 

On science and scientism

GST and classical cybernetics is scientific in so far as its deals with behaviors that are regular, or deterministic, or reproducible.

Actual (empirical) performances of behaviors can be tested for conformance to abstract (theoretical) descriptions of those behaviors.

As, for example, the actual orbits of planets are tested for conformance to astronomers’ descriptions of those orbits.

And the actual behaviors of US governments are tested for conformance to the description of those behaviors in the US constitution.

Social system

Abstract system description

US constitution

Concrete system realization

US government

 

Evidence-based medicine is scientific; a medicine man (or shaman) is scientistic.

Some social systems thinking ideas are scientistic, meaning there is little or no evidence to verify or falsify them.

It easy to find problems in human organizations, more difficult to propose viable changes, and more difficult again to make them.

And then sometimes even more difficult again to prove whether the change was for the better or not.

 

System complexity?

Some systems thinkers use terminology as a weapon to belittle GST and classical cybernetics.

They deprecate GST by saying it is reductionistic rather than holistic, yet GST is indeed holistic.

They say it is systematic rather than systemic, yet systemic usually means throughout the system – which is probably not what they mean.

They say is it is linear rather than non-linear, yet GST can be used to model systems that display non-linear behaviour.

They say it addresses simple systems rather than complex adaptive systems; yet in GST terms, that is an evolving social entity, an ever-unfolding process, rather than a system.

The primary distinction to be drawn is between what this paper calls social systems and social entities.

 

System granularity?

System structures/components and behaviors/processes are composable and decomposable.

There are shorter processes within components, and longer processes that connect components one way or another.

Cross-component processes are needed to maintain the integrity of a system.

These cross-component process may be realised by either orchestration of, or choreography between, components.

 

These general system design issues are currently debated under the heading of “microservices”.

A useful discussion of microservices has to address the level of granularity.

And explore the consequences of dividing what could be one coherent data structure.

For some discussion, try this paper.

http://grahamberrisford.com/AM%202%20Methods%20support/08Microservices-Microapps/Microservices%20-%20Micro%20apps.htm

 

 

References

For further appraisal of systems thinkers’ ideas, try the system theory page at http://avancier.website.

For more background on systems thinking try “Introducing Systems Approaches” http://www.open.edu/openlearn/ocw/pluginfile.php/704004/mod_resource/content/6/Introducing-systems-approaches_ch1.pdf

 

 

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