1a A brief
history of systems thinking
Copyright 2017 Graham Berrisford. Now a chapter in “the book” at
https://bit.ly/2yXGImr. Last
updated 24/02/2021 20:38
“A very interesting potted history
of the evolution of social entity thinking and the different strands of
thoughts that have evolved.”
The more systems thinking discussion you read, the more confused you may become. Many confusions stem from people over-generalizing different schools of thought. Even respected authors use the terms of one school with reference to different concepts in another.
This 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. One school is not an evolution of the other; they are not competitors; both are needed. On the way, this chapter also highlights some misconceptions about “emergence” and “holism”.
Contents
Preface: beginnings of systems thinking
Part
one: general system theory
Part
two: activity system theory
Part
three: generalizing activity system theory
Part
four: social entity thinking
Many ideas that figure in systems thinking today emerged more than a century ago. Notable authors have included:
· Isaac Newton (1642-1726) who described the world as a system of objects that interact by forces, according to the laws of motion.
· Adam Smith (1723-1790) wrote on specialization of, cooperation and competition between, businesses (cf. autonomous agents).
· Charles Darwin (1809-1882) wrote on the evolution of a species (or phenotype) by reproduction with modification.
· Claude Bernard (1813-1878) wrote on homeostatic feedback loops; and
· Vilfredo Pareto (1848-1923) is famous for the Pareto principle.
Sociologists have often drawn terms and concepts from biology. The first sociological thinkers include:
· Herbert Spencer (1820-1903) on social systems as organic systems.
· Emile Durkheim (1858-1917) on collective consciousness and culture;
· Gabriel Tarde (1843-1904) on social systems as emerging from the actions of individual actors (cf. autonomous agents);
· Max Weber (1864-1920) on bureaucracy, hierarchy, roles and rules.
· Kurt Lewin (1890-1947) on group dynamics; and
· Lawrence Joseph Henderson (1878-1942) on meaning in communication.
For a distillation of ideas attributed to the nineteenth century thinkers above, you could read my sketchy notes here thinkers who foreshadowed system theory. Some 19th century ideas were supplanted by more scientific one in the 20th century.
When system theory became established as a topic in its own right is debatable. Some suggest that social system thinking is branch of general system theory; others suggest the reverse.
“Systems theory, also called social systems theory... https://www.britannica.com/topic/systems-theory
The general concept of a system became a focus of attention after second world war. There was a burst of theory development in the period 1940 to 1980, out of which two major schools of system science emerged. First, cybernetics, after Weiner, and Ashby. Later, system dynamics, after Forrester, and Meadows. Both schools address emergent, non-linear, chaotic, adaptative and self-organizing behavior. But not necessarily how some sociologists use those terms - there is terminology torture out there.
Strictly, cybernetics was established before general system theory, but it is convenient to outline the latter first. The 1954 meeting of the American Association for the Advancement of Science in California was notable. Some at that meeting conceived a society for the development of general system theory. The founding members of the International Society for the Systems Sciences (1955 to date) included:
· Ludwig von Bertalanffy (1901-1972) the cross-science notion of a system
· Anatol Rapoport (1911 to 2007) wrote on game theory and social network analysis.
· Kenneth Boulding (1910-1993) applying general system theory to “management science”.
Ludwig von Bertalanffy was a biologist who promoted the idea of a cross-science general system theory from the 1940s.
“There exist models, principles, and laws that apply to generalized systems or their subclasses, irrespective of their particular kind.”
He looked for patterns and principles
applicable to several sciences and domains of knowledge. This section introduces some of his concepts and defines
associated terms.
“We presently "see" the universe as a tremendous hierarchy, from elementary particles to atomic nuclei… to cells, organisms and beyond to supra-individual organizations.”
“We cannot reduce the biological, behavioral, and social levels to the lowest level, that of the constructs and laws of physics.” Bertalanffy 1968
This table (bottom to top) presents a history of the universe from the big bang to human civilisation. Bertalanffy’s writings were much influenced by thinking at the biological level, thinking of a biological organism as a system.
|
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 |
"Rather than
reducing an entity to the properties of its parts or elements,
systems theory focuses on the arrangement of and relations between the
parts which connect them into a whole.” Principia Cybernetica Web
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 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)
“Every living organism is essentially an open system. It maintains itself in a continuous inflow
and outflow…” Bertalanffy 1968
System: a pattern, an island of regularity or
repetition, in processes of the universe.
System boundary: a line separating a system from its environment.
System environment: the world
outside the system boundary.
Open system: a system connected to its wider environment by inputs and outputs.
Closed system: a system not connected
to anything outside its boundary.
System interface: a
description of inputs and outputs that cross the system boundary.
“Another development which is closely connected with system theory is that of… communication. The general notion in communication theory is that of information.” Bertalanffy 1968
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.
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.
A second central concept of the
theory of communication and control is that of feedback.” Bertalanffy 1968
Feedback loop: the circular fashion in which output flows
influence future input flows.
Feedback is a central concept in cybernetics and system
dynamics (see part 2 below), and central to the success of business operations. For more on feedback, read the section on
system dynamics below. For more on information and communication read the
second version of this chapter.
Anatol Rapoport was a mathematical psychologist and biomathematician who made many contributions. In researching cybernetic theory, he pioneered the modelling of parasitism and symbiosis. This gave a conceptual basis for his work on conflict and cooperation in social groups.
When actors interact in a system, they may cooperate, as in a football team or a business system. But they don’t necessarily help each other. They may compete, as in a game or a market; or hurt each other, as in a boxing match or a war. Cooperation, conflict and conflict resolution is a focus of bio-mathematics and game theory.
Game
theory: cooperation and conflict resolution. In the 1980s, Rapoport won a
computer tournament. It was designed to further understanding of how
cooperation could emerge through evolution. He was recognized for his
contribution, through nuclear conflict restraint, to world peace.
Social
network analysis. Rapoport showed that one can measure flows through large
networks. This enables learning about the speed of the distribution of
resources, including information through a society, and what speeds or impedes
these flows.
Bertalanffy
didn’t like some directions in “the system movement”. But he saw it as “a
fertile chaos” that generated many insights and inspirations.
Kenneth
Boulding
was among the first to apply general system theory to business. He is
known for his article in volume 2 of the journal “Management Science”, 1956.
The article is
remembered for a hierarchical classification of system types. Boulding proposed a hierarchy that places
socio-cultural systems near the top (the end of this list).
1. Static structures
2. Clock works
3. Control mechanisms
4. Open systems
5. Lower organisms
6. Animals
7. Man
8. Socio-cultural systems
9. Symbolic systems
“[Boulding’s hierarchy] is impressionistic and
intuitive with no claim for logical rigor.” Bertalanffy 1968
The
hierarchy mixes up three different scales: steps in complexity (simple to complex);
steps in composition (small to large); and third. steps in the evolution of the
universe.
Odd
features of the hierarchy include the following.
· It stretches from static structures (1)
through robotic and self-aware organisms to their logical products (9), which
are static structures (1).
· Clockwork mechanisms (2) are open (4), since
they consume energy from a winder, and give that energy to some movable entity.
·
Interactions
between cells in an organism (5) can be more complex than interactions between
animals in a socio-cultural system (8).
In the same article, Boulding made a less noticed but arguably more fundamental point. He questioned whether the elements of a social system are actors, or the roles they play. This brings us to a fundamental dichotomy in systems thinking.
A fundamental dichotomy
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
This chapter goes on to contrast two kinds of systems thinking. 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).
This section outlines three schools of activity
systems thinking, which have a lot in common.
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.
Cybernetics: the science of how a physical, biological or social machine can be controlled. It emerged out of efforts to understand the role of information in mechanical system control.
General principles include:
1.
Regular
activities maintain variables that describe the state of actors (organisms,
machines, societies)
3.
Observers
may observe the current state of a system, and draw a graph to show how the
system's state changes over time.
The general system theory of cybernetic was
discussed and promoted by two influential groups.
1941 to 1960: The Macy Conferences - cross-disciplinary meetings in New York, with a mandate to aid medical research. Topics included connective tissues, metabolism, the blood, the liver and renal function. Also, infancy, childhood, aging, nerve impulses, and consciousness.
1949 to 1958: The Ratio Club - a cross-disciplinary group in the UK, founded by the neurologist John Bates to discuss cybernetics. Members included psychologists, neurobiologists, engineers, physicists, and mathematicians. Many went on to become prominent scientists.
Well-known cyberneticians include:
· Norbert Wiener (1894-1964) the science of system control.
· W. Ross Ashby (1903-1972) the law of requisite variety.
· Alan Turing (1912 –1954) finite state machines and artificial intelligence.
Wiener discussed how a controller directs a target system to maintain its state variables via information feedback. Information feedback loops are found in both organic, mechanical, business and software systems:
·
A
missile guidance system senses spatial information, and sends messages to
direct the missile.
·
A
brain holds a model of things in its environment, which an organism uses to
manipulate those things.
·
A
business database holds a model of business entities and events, which people
use to monitor and direct those entities and events.
· A software system holds a model of
entities and events that it monitors and directs in its environment.
Ashby popularized the usage of the term 'cybernetics'. We’ll return later to how Ashby addressed self-organizing systems. Initially, cybernetics addressed what may better be called self-regulating systems.
For decades, thinkers had been interested in self-regulating homeostatic systems. In “Design for a Brain” (1952), Ashby
presented the brain as a regulator. It maintains a body’s state variables in
the ranges suited to life. This table distils the general idea.
|
Generic activity system |
Ashby’s
design for a brain |
|
Actors interact in orderly activities to maintain system state and/or consume/deliver inputs/outputs from/to the wider environment. |
Brain cells interact in processes to maintain body state variables by receiving/sending information from/to bodily organs/sensors/motors. |
Homeostatic entities and processes are only a subset of systems. Ashby’s cybernetics is a more general theory of how an entity behaves, or should behave.
This book interprets and expresses Ashby’s ideas in the triangle below. Read the triangle below left to right thus. Observers <create and use> Abstract systems <represent> Physical systems.
|
Ashby’s cybernetics |
|
Abstract
systems <create and use> <represent> Observers <observe and envisage> Physical systems |
An abstract system is a model or type that defines an instance of that type. A physical system realizes an abstract system description. E.g. A real-world hurricane realizes a recognized abstract weather system. E.g. Your heart beats in accord with an abstract system known to medical science.
State: the current material or information structure (variable values) of a
system. State variable values may represent ever-changing quantities of
stocks, populations or resources, or else more qualitative attributes.
Most systems of interest to us are stateful, rather than stateless. Ashby wrote
“A variable is a measurable quantity that has a value.”
“The state of the system is the set of values that the variables have.”
|
System |
State |
|
|
|
Information
state |
Material
state |
|
A
prey-predator system |
wolf and sheep
populations |
the physical condition of
each wolf and sheep. |
|
A
tennis match |
game, set and match
scores |
the condition of the
court, balls and players. |
Krippendorff, a student of Ashby’s cybernetics, wrote:
"Ashby defined a system [as] a set of variables chosen for attention and relationships between these variables, established by observation, experimentation, or design."
A
control system monitors and directs the state of some target system. The
control system can record the state of the target system in some kind of
memory. That memory is updated by the receipt of information from the target
system.
Process: a behavior, a sequence of activities that changes
or reports a system’s state, or the logic that controls the sequence.
Line of behavior: the trajectory of a system’s state change over time, as shown on a graph. The shape of the line is an inexorable result of the system following its rules. The line for one variable value over time might be linear, oscillating, curved or jagged. The progress of a system with two or three variables can be represented on a graph as a two or three-dimensional shape.
Three
ways to model the dynamics of a system
Continuous-variable
dynamics. This presumes a system's state changes continuously. The rates of
change of state variables can be defined differential equations. This approach
is rarely used in modelling a business or software system. And if time is
divided into small enough intervals, the next approach is approximately the
same.
Fixed-increment
time progression. This is the basis for software tools that help people use
Forrester's system dynamics. Time is divided into time slices that can each
contain several events. The system state is updated according to the set of
events that happened in the preceding time slice.
Next-event
time progression. This is the basis for most business and software system
modelling, and Ashby's cybernetics. In a discrete event driven system, the
state advances over time in response to discrete inputs. Each event occurs at a
particular instant in time. Between events, no change in the system occurs.
For
more on cybernetics in general, read the chapter on Ashby’s
top ten ideas, and Beer’s
ideas. Ashby’s cybernetic ideas are applied every
day in modelling business activity systems.
System Dynamics: a technique for modelling a set of stocks increased and decreased by inter-stock flows.
General principles include:
1.
Regular
flows increase/decrease variable stocks that represent the state of resources
or populations of any kind
2.
Feedback
loops connect stocks that respond to changes in each other. The whole model
represents a closed system or ecosystem.
3. Observers may draw a diagram of flows between stocks, and draw a graph of stock level changes over time.
Core concepts include:
Stock: a variable number representing the quantity of a stock, population or resource. E.g. wolf and sheep populations, happiness level, sunlight level, or the variables that affect climate change
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.
Systems dynamics was founded and promoted by:
· Jay Forrester (1918 to 2016) every system is a set of quantities that are related to each other.
· Donella H. Meadows (1941 to 2001) resource use, environmental conservation and sustainability.
Jay Forrester was a professor at the MIT Sloan School of Management who defined the method. Typically, the modeller begins by drawing a causal loop diagram that names stocks and connects them by flows. The nodes in the diagram represent stocks or populations. The arrows connecting the nodes, represent inter-stock flows. The modeller can go on to define the mathematical rules for how flows modify stock quantities.
Feedback
loops
"Self-regulating mechanisms have existed since antiquity, and the idea of feedback had started to enter economic theory in Britain by the 18th century, but it did not have a name.... In 1868, James Clerk Maxwell wrote a famous paper, "On governors", that is widely considered a classic in feedback control theory. This was a landmark paper on control theory and the mathematics of feedback." (Wikipedia)
Feedback occurs when the outputs or effects of a system influence its future inputs or effects. Feedback loops can connect subsystems, systems, and systems to their environments. Suppose stocks A and B are connected in a loop by flows in both directions. The loop might be one of three kinds.
|
Kind of loop |
Short
term effect |
Long
term effect |
|
Amplifying, or reinforcing |
increasing A increases B
and vice-versa e.g. Population <increases> Births <increase> Population |
The explosion of
population or resource |
|
Diminishing |
decreasing A decreases B
and vice-versa e.g. Population
<increases> Deaths <decrease> Population |
The extinction or
exhaustion of a population or resource. |
|
Dampening, or balancing |
increasing A increases B,
but increasing B decreases A e.g. Births
<increase> Deaths <decrease> Births |
maintains the two
variables in a stable or homeostatic state. |
When completed, the model can be animated. You give the state variables some initial values, then set the system in motion. Its state will change in discrete time-steps, and this result can be reported on a graph.
Forrester
was concerned with entities that can be modelled as having continuous
dynamics. Mathematically, the model is a
set of coupled, nonlinear, first-order differential (or integral)
equations. How do software tools animate system
dynamics? They convert continuous dynamics into discrete event-driven
dynamics. They divide time into a succession of discrete time
events.
Then step through the model - one interval at a time - and report how
stocks, populations or resources change over time.
|
Mathematical causal loops <create
and animate>
<represent> System
modellers
<observe and envisage> Inter-related quantities |
Donella
Meadows was an environmental scientist, teacher, and writer. She was much
concerned with resource use, environmental conservation and sustainability.
She is known as lead author of the popular and influential book “Thinking
in Systems: a Primer.”
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.
How accurately can a system dynamics model represent reality? Does it show us how real-world stocks, populations or resources change over time? Sometimes, but it is often difficult to identify all relevant stocks and flows. And then difficult to represent the flows and rules accurately in mathematics.
|
Aside: sometimes what looks like a
causal loop diagram is not one. People use drawing tools to draw what look like
causal loop diagrams. They do it to illustrate an idea, present their own
beliefs or tell a story. The diagram may be more akin to a concept graph than
a system model. |
Meadows took a side-swipe against
event-driven models. However, a causal flow often represents a stream of events
happening the real world. E.g. a flow may represent a batch of predation events
in which wolves kill sheep.
For notes on some ambiguities in Meadows’ popular book, read chapter 5.
This site gives more detail on system dynamics http://systemdynamics.org/what-is-sd. For my take on system dynamics, read System Dynamics. Akin to system dynamics are agent-based approaches to the analysis of systems.
The term “soft system” emerged in the 1970s. General principles include:
1.
Regular
activities transform inputs into outputs wanted by customers
2.
Feedback
loops connect a business to its environment thus: a) it detects changes in the
state of its environment. b) it determines responses and c) it directs entities
to perform activities.
3. Observers may draw a business activity model, and read the current state of a system in its data store(s).
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. And all the gurus below combined some activity system thinking with some social entity thinking about a business “organization”.
Churchman, one of the first soft systems thinkers, said "a thing is what it does". He outlined these considerations for designing a system managed by people:
· “The total system objectives and performance measures;
· the system’s environment: the fixed constraints;
· the resources of the system;
· the components of the system, their activities, goals and measures of performance; and,
· the management of the system.”
Like others, Churchman
sought to integrate system theory into “management science”. In replacing
“business” by “system” he conflated the ideas of social entity and social
activity system.
Somewhat better-known soft systems thinkers include:
· Russell L Ackoff (1919-2009) human organizations as purposeful systems.
· Peter Checkland (born 1930) the Soft Systems Methodology.
· Stafford Beer (1926- 2002) management cybernetics and the Viable System Model.
Russell Ackoff wrote many works on systems. In his vocabulary, an “abstract system” represents a “concrete system”. To paraphrase his second and third definitions of terms:
· Concrete system: a system that has two or more objects.
· Abstract system: a system in which the elements are concepts.
Ackoff’s abstract system is a model of how an entity behaves, or should behave. His concrete system is any entity that realizes to an abstract system. He wrote that: “Different observers of the same phenomena [or discernible entity] may conceptualize them into different systems and environments.”
|
Ackoff’s
system theory |
|
Abstract systems <create
and use>
<represent> Systems
thinkers
<observe and envisage> Concrete
systems |
Ackoff
wrote wisely about the management of human institutions or organizations. He
did somewhat lose the plot when speaking of human organizations as systems. For my take on Ackoff’s view of systems Ackoff’s
ideas.
Peter
Checkland promoted a “soft systems methodology”
for business analysis and design. He
regarded a business as an input-to-output transformation system. His business activity model shows
a network of activities that transform inputs into outputs Different observers may perceive
different systems, possibly in conflict, in one organization. He called each perspective (a “soft system” if you like) a
“weltenshauung” or world view.
|
Checkland’s Soft Systems Method |
|
World
views <create and use> <represent> Observers <observe and envisage> Business
organizations |
Checkland noted the distinction between hard and soft system approaches is slippery. He wrote that people get it one day, and lose it the next. He said the term “soft” was intended to characterize his method – not a system. For more, read Checkland’s ideas.
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 architecture frameworks like TOGAF.
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.
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.
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.
|
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 |
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.
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.
Society as organism
Many ideas discussed by systems thinkers today emerged more than a century ago. Early sociological thinkers drew terms and concepts from biology. Notably, homeostasis, hierarchy, roles and rules. And the emergence of effects or results from interactions between autonomous actors. Then and today, some liken a human organization to a biological organism. Like many metaphors, this can be more misleading than helpful.
Society as tribe
The societies from which we emerged (and still prevail over much of the world) were dominated by clans and tribes, in which loyalty to kin overrides any other consideration. Some anthropologists concluded 25 people is the magic number for a tribal community. That number includes children, elders and only 7-8 productive foragers. "The Foraging Spectrum: Diversity in Hunter-Gatherer Lifeways." (Robert J. Kelly ,2007).
Society as homeostat
The idea? The biologist Claude Bernard introduced the idea that a homeostatic organism, maintains its state in equilibrium. Several early sociologists compared human societies to organisms. Spencer declared three principles for a social system: evolution (creates and changes a system), equilibrium (maintains a system in a stable state) and dissolution (destroys a system).
A common idea in sociology is that a society (or its members) has an equilibrium. It resists changes and strives to maintain its roles and rules. The idea appears today in the idea of the "social cell". Stafford Beer treated a business as homeostatic; and applied cybernetic principles to the supply chain of state-run businesses in Chile. Later, in "The Brain of the Firm", he used the structure of the central nervous system as a metaphor for the structure of a business organization.
Society as knowledge builder
The idea? Knowledge comes from social interaction. The sociologist Herbert George Blumer (1900 to 1987) wrote of “symbolic interactionism”, which rests on premises about human activities.
Society as autopoietic (self-sustaining)
The idea? Maturana proposed autopoiesis to be the unique and defining feature of a life form. It is the self-sustaining process by which a biological organism manufactures its own structures from primitive chemicals. And those same structures perform those same self-sustaining processes.
Stealing the term rather than the concept, by way of an analogy or metaphor with biology, Nicklas Luhmann defined a theory of "autopoietic social systems". It is based on the idea that for every particular topic or theme, there is a self-sustaining system of communication events.
Society as network structure
The idea? A society is a structure that connects people in communication and/or dominance relationships. Graph theory is a field of mathematics (well introduced by Robin Wilson). It is concerned with how things connect in network structures. There is considerable interest in applying graph theory to social entities.
Society as an ecology of interacting
organizations
The idea? Human health and welfare advanced by an ecology of trading relationships between autonomous tribes or organizations. Rather than by warfare or central planning.
Society as an organization within a market
The idea? A business organizes how actors cooperate to meet the aims of the business. Max Weber spoke of a bureaucratic model, with a hierarchy, roles and rules.
For discussion of the views above, read the chapter on social entity thinking.
Second order cybernetics 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. The position here is that this a confused way of thinking about systems. Remember Boulding’s distinction between roles and actors? Second order cybernetics conflates them, and so undermines the concept of an activity system, in which actors are defined by their roles.
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. Read other
chapters for more on this,
This chapter distils centuries of systems thinking, and brings some new clarity to the field. Many confusions stem from people over-generalizing different schools of thought. Even respected authors use the terms of one with reference to different concepts in another.
Above all, it is necessary to differentiate two schools: social entity thinking and activity systems thinking. One is not an evolution of the other; they are not competitors; both are needed. The following chapters will reconcile the two schools, and relate them to Enterprise Architecture (EA).
Read the chapter on social entity thinking for more on that. Read the chapter on activity systems thinking for more on that.
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 4a 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 5a of “the book”.
5. Michael Jackson’s 2003 review of system thinking approaches
Beware that second half of reference 4 confuses social entity thinking with activity system thinking. Beware that in reference 5 Jackson (like Midgely and others have done) draws some false distinctions between “hard” and “soft” systems thinking.
Read on for issues to beware of in references 4 and 5.
Reference 4 tends to confuse social entity thinking with activity thinking.
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.
"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.
“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.
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.
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