The ideas of social systems thinking
(Separating social systems from social entities)
Copyright 2017 Graham Berrisford. One of several hundred papers at http://avancier.website. Last updated 03/03/2020 10:54
The systems of interest to us here feature actors performing activities.
The actors are active structures of any kind, human and/or other.
The activities are behaviors performed over time by actors.
The system’s state is the current status of the system's physical materials and/or logical information/memory.
An earlier paper discussed general system theory; this one focuses more narrowly on social systems.
“The first decision [a theoretician has to make] is what to treat as the basic elements of the social system.
The sociological tradition suggests two alternatives: either persons or actions." Seidl 2001
Some social systems thinkers try to have it both ways, or slip from one to the other without noticing.
Some use the terms of more general system theory, but with different meanings.
An aim here is to disambiguate some terms and point to how general system theory can be reconciled with social systems thinking.
Here "system theory" is activity-centric, whereas "systems thinking" is actor-centric.
In relation to human activity systems, system theory can be said to rest on five premises:
1. Actors act on things and interact in regular activities to meet agreed aims.
2. Interactions involve the exchange of meanings encoded in the data structures of messages and memories.
3. Actors create and find meanings in data structures by encoding meaning in symbols, and decoding (interpreting) meanings from symbols, using a shared language.
4. The meanings of symbols are fixed in a shared language before the system runs
5. The meaning of symbols may be changed by agreement between discrete generations of the system.
On the surface, systems thinking seems similar to system theory.
E.g. Herbert George Blumer (1900 to 1987) was an American sociologist.
His “symbolic interactionism” rests on three premises about human activities:
1. Actors act on things on the basis of the meanings those things have for them.
2. The meanings of things derive from the social interactions between actors.
3. Meanings are handled in, and modified through, an interpretive process.
However, the meanings of things to an actor derive also from their individual experiences of the world.
And note that Blumer's methodology lacks the concepts of a formal shared language, and discrete inter-generational system change.
It is a looser kind of sociological systems thinking, rather than system theory.
What is here called system theory is about regular activities, which are performed by actors.
It embraces Ashby's cybernetics, Forrester's system dynamics, and some “soft systems” techniques.
It surfaces in enterprise, business and software architecture models, such as business activity models, process flow charts and data flow diagrams.
And in social systems definable as activities performable by different actors in different social entities.
In natural language, the term system is often used for a collection of inter-connected things, parts or people.
If every whole divisible into parts is a system, then everything larger than a quark is a system.
If every describable entity or situation (larger than a quark) is a system, then the term is a noise word, it adds no useful meaning.
The systems of interest here are not static structures, like the periodic table.
They are dynamic, meaning they display behavior and change state over time.
E.g. Consider a tennis match, whose current state is displayed on the score board.
So, when and where is an entity or situation properly called a system?
Answer: when and where its parts interact in regular ways, where there is a pattern of behavior.
As shown for example in Checkland’s “Business Activity Model”
What is here called system theory is about regular activities, which are performed by (replaceable) actors.
System theory embraces Ashby's cybernetics, Forrester's system dynamics, and some “soft systems” techniques.
It surfaces in enterprise, business and software architecture models such as process flow charts and data flow diagrams.
Many have adopted the terminology of general system theory, cybernetics and system dynamics.
“Though it grew out of organismic biology, general system theory soon branched into most of the humanities.” Laszlo and Krippner.
What is here called systems thinking is looser than system theory.
If is about a group of actors who interact in some activities to some purposes.
And much of it is particular to human actors and human activities.
In this context, the actors may determine and change the activities they perform.
The first sociological thinkers included:
· Herbert Spencer (1820 to 1903) social systems as organic systems.
· Emile Durkheim (1858 to 1917) collective consciousness and culture.
· Gabriel Tarde (1843 to 1904) social systems emerge from the actions of individual actors.
· Max Weber (1864 to 1920) a bureaucratic model – hierarchy, roles and rules
· Kurt Lewin (1890 to 1947) group dynamics.
· Lawrence Joseph Henderson (1878 to 1942) meaning in communication
Many have likened a social entity or business organization to a biological organism.
And many have presumed that social or business system is homeostatic.
Though these ideas have influenced systems thinkers for 150 years, they are at least somewhat misleading.
They deprecate system theory as "reductionistic" or "linear".
This is either to show ignorance of system theory, or to use those words with obscure meanings.
Some deprecate system theory as "mechanistic".
Yet many thinkers are also fans of "Thinking in Systems" (Meadows), a book on system dynamics - which is mechanistic system theory.
Many social systems thinkers draw the equation: 1 human organization = 1 system.
And use the scientific-sounding term "complex adaptive system".
This paper proposes we must separate the concepts of social entity and social system.
Which is to say, the activity system(s) realised by a given social entity are changeable.
Three kinds of organization
If your interest is in enterprises that employ many people, you might be interested in:
· Organization kind 1: a causal loop structure in which employees are triggered by flows to perform activity instances.
· Organization kind 2: a management structure in which employees are directed as to what their activity types are.
· Organization kind 3: a social network of employees within an enterprise.
· Anarchy: the extent to which employees determine their own activities, and even their own purposes.
A real-world business, like IBM, is a complex adaptive entity in which all of four of these things exist.
In practice we cannot, and are never expected to, describe the whole entity as one coherent system.
3/11 “Our first impulse is to point at [IBM] and to say "the system is that thing there".
This method, however, has a fundamental disadvantage: every [such entity] contains no less than an infinity of variables and therefore of possible systems.
Any suggestion that we should study "all" the facts is unrealistic, and actually the attempt is never made.
What is necessary is that we should pick out and study the facts that are relevant to some main interest that is already given.” (Ashby 1956)
Three kinds of flow between actors
From chapter 1 of "Thinking in Systems" (Meadows), the opening sentence is often quoted, but variously interpreted.
“A system is an interconnected set of elements that is coherently organized in a way that achieves something.”
That definition is so generalized that it embraces readings contrary to assertions Meadows makes about systems.
Consider the three different kinds of flow that appears in the three different kinds of organization above.
1: Causal flows - in causal loop structure(s) - in which employees are triggered by flows to perform regular activities
A causal loop structure organizes some quantifiable stocks, populations or resources in a structure of causal relationships.
Here, coherently organized means system elements are inter-related as in a causal loop diagram (not a management structure).
And to achieve something is to produce "a pattern of behavior over time" (not meet goals set by people).
The flows in a causal loop structure are causal relationships, which trigger actors to perform regular activities.
Most of Meadows’ book is about systems of this kind, as in system dynamics.
(She takes a side-swipe against event-driven models, as are used in most business system modelling.)
It is impossible, and never necessary in practice, to describe every causal flow in a business.
And impossible to address all conflicts that may arise between them.
“There can be no such thing as the unique behavior of [IBM], apart from a given observer.”
"There can be as many systems as observers... some so different as to be incompatible.”
“[Therefore] studying [IBM] by studying only carefully selected aspects of [it] is simply what is always done in practice.” (Ashby 1956).
2: Denotic causal flows - in management structures – which direct employees as to do and/or achieve
A human institution typically organizes people from the top down in a structure of authority/reporting relationships.
Here, coherently organized means people are related in a management structure.
And to achieve something is to perform some duties, or meet some goals, typically cascaded downwards from higher managers.
Some flows between actors in a management structure report the results of activities (usually to those higher up).
Other flows convey goals, duties or obligations (usually to those lower down).
Some call these denotic causal flows – which sound like causal flows, but are very different.
Causal flows trigger the performance of defined activities.
Denotic causal flows define activities to be performed (or tell actors enough to define the activities for themselves).
In a denotic relationship, a manager gives goals, duties and obligations to an employee.
The manager specifies the actor's role in a regular business system (S).
Ashby said: “No machine can be self-organizing in this sense.”
He meant that to re-organize a system, it must be coupled another, “higher” process or meta system (M).
M = the higher process or meta system, in which actors which act to specify the roles, rules and variables of S.
S = the system in which actors perform activities as directed by M.
Here, the manager plays a role in a M rather than in S.
To draw a causal loop structure for S is difficult enough, to draw it for M is bigger challenge.
3: ad hoc information flows - in social networks
A social network is a structure in which actors create connections by communicating with each other.
Much of the inter-actor communication in corporations is ad hoc and impromptu.
Though much of it is essential to a business, the behavior of this network is irregular and outside any definable system.
Again, a real-world business, like IBM, will surely feature all three kinds of flow above.
Some of today’s systems thinking discussion is generic - about how groups of people work effectively together.
It is not about particular business operations; it is instead about how people shape and steer those operations.
It is about a higher process or meta system (M) that defines the workings of regular business operations (S).
Separation of social systems from social entities?
Meadows' book is about regular system dynamics of the kind modellable in a casual loop network.
But some readers equate a human or business organization to a system, and don't notice the incongruity.
Many systems describable as casual loop networks might be observed in one managed human institution.
A causal loop diagram cannot all define the behavior of IBM; it can only define one mechanistic system realized by that entity.
Any system we abstract from the staggering complexity of IBM as a whole is simple, and only one of many, possibly conflicting, perspectives.
Moreover, denotic causal flows (directing actors’ behavior) may have effects that are contrary to regular causal flows.
In chapter 5 of "Thinking in Systems", Meadows observes that manager-set goals can lead to unintended consequences and counter-productive results in operational systems.
So, IBM may well be called a complex adaptive entity.
But sorry Donella, it is meaningless to call IBM a system with no reference to your perspective or model, be it mental or documented.
Having said that, chapter 7 of Meadows "Thinking in Systems" does discuss the importance of verifying system models against realities.
“Expose Your Mental Models to the Light of Day... making them as rigorous as possible, testing them against the evidence,
and being willing to scuttle them if they are no longer supported is nothing more than practicing the scientific method
—something that is done too seldom even in science, and is done hardly at all in social science or management or government or everyday life."
Reconciling system theory with systems thinking
In system theory, a system is a particular way of looking at a real-world entity or situation.
The entity must behave systematically in some useful sense.
System theory is useful whenever we seek to:
· understand how some outcome arises from some regular behavior.
· predict how some outcome will arise from some regular behavior.
· design a system to behave in a way that produces some desired outcome.
· intervene in a situation to change some system(s) for some reason.
An aim in what follows is to clarify ambiguities in wider systems thinking discussion, including those in the next section.
We shall consider the impacts of resolving these ambiguities on social systems thinking and on “complexity science”.
And point to how system theory can be reconciled with systems thinking.
An abiding sin of some “systems thinkers” is over generalisation
They take a word from one domain and use it with a different meaning in a different (often social or business) domain.
This does not produce a more general system theory - it merely draws a superficial analogy between what can be very different concepts.
This phenomenon, sometimes called "overloading", introduces ambiguity into discussions, and the analogy can be misleading.
1 An entity (material object, real-world thing or organization)
2 A system realised by an entity
1 System state change (homeostatic or progressive)
2 System mutation
1 Rule-bound self-assembly (of parts into a whole)
2 Rule-changing improvement (system mutation)
To rescue the system concept, we need to distinguish social entities from social systems, as this table indicates.
A social entity
A social 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 simply a group of people who inter-communicate.
It is an entity, a bounded whole, but is it a system?
When and where the actors creatively invent how they act and interact, the entity does not behave as a system.
The entity is a system only when and in so far as its actors interact in regular ways – where there are describable roles or rules.
A social 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.
Every human actor can belong to many social entities and play roles in many systems.
One social entity can realise several distinct social systems.
And one social system can be realised by several social entities.
Continuous change is the nature of our universe.
However, a system is (by definition) an island of regularity in the ever-unfolding process that is the universe.
It can change in two different ways:
· System state change: a change to the state of a system.
· System mutation: a change to the roles, rules or variables of a system.
The focus of a social systems thinking discussions is often on system mutation.
And the term “complex adaptive system” is often used
For sure, a complex socio-technical entity (like IBM) may continually adapt to changes in the environment - by changing itself.
And IBM might reasonably be called a complex adaptive entity.
But it is meaningless to call it a complex adaptive system without reference to a system of interest – the particular perspective of some observer(s).
Moreover, continuous adaptation or reorganization undermines the concept of a system – which is regularity.
If we don't distinguish an ever-evolving social entity from the various modellable systems it may realise, the concept of a system evaporates.
For more on that distinction, read on, and read second order cybernetics.
Ashby distinguished two kinds of self-organization.
· “Changes from parts separated to parts joined” “Self-connecting” “Perfectly straightforward”
· “Changing from a bad way of behaving to a good.”
The two kinds might be named and differentiated as:
· rule-bound self-assembly (as when autonomous geese join in a flight of geese)
· rule-changing improvement (as when a machine reconfigures itself to behave in a different way).
Of the second kind, Ashby said: “No machine can be self-organizing in this sense.”
He meant that to re-organize a system, it must be coupled another system – which we may call a higher-level process or meta system.
Note that one actor can play a role in both systems, lower and higher.
General system theory doesn’t start from or depend on sociology, or analysis of human behavior.
However, it stimulated people to look afresh at social systems in general and business systems in particular.
“Second-order cybernetics” was developed in the early 1970s.
It was pursued by thinkers including Heinz von Foerster, Gregory Bateson and Margaret Mead.
However, as Ashby’s student Krippendorff wrote, the observer and their particular interest were already recognised in classical cybernetics.
"It is important to stress Ashby defined a system not as something that exists in nature. What we know of a system always is an ‘observer’s digest’.” Krippendorff
Von Foerster asked “Am I a part of the system, or I am apart from the system?”
Krippendorff noted Ashby considered the observer part of the system.
“Although second-order cybernetics was not known at this time, Ashby included himself as experimenter or designer of systems he was investigating.” Krippendorff
Second order cybernetics is said to look at the world as a duality of systems and observers.
This duality means the term “system” is often used the first sense of Ashby’s two senses – referring directly to a real-world thing, socio-technical entity, or business situation.
However, the observers of such a real word entity may describe its properties, problems and possibilities without reference to any system.
And they may describe the entity as realising countless different (possibly conflicting) systems at the same time.
So, other system theorists take a triangular view, distinguishing abstract systems from physical systems.
<create and use> <represent>
Observers <observe and envisage> Physical systems
Second order cybernetics is said to be the circular or recursive application of cybernetics to itself.
For sure, a system’s actors can also be systems thinkers, who study and reorganize a system they play roles in.
However, to do that, the actors must step outside that system, and take a role in a different, higher process or meta system.
(This idea is pursued below and in related papers.)
In this video https://youtu.be/acx-GiTyoNk he proposed a system is “a pattern which connects”.
In other 20th century sources we have more specific and useful definitions of a system’s properties.
In most modern systems theory, the “parts” of a system are actors or components that interact in regular activities to advance the state of the system or something in its environment.
For more read Systems thinking approaches.
Social systems thinking continued alongside the post-war system theory movement, sometimes in touch with it, sometimes far apart from it.
Below are a few notes on sociological matters and sociologically-inclined systems thinkers.
Ackoff noted that the actors in a system, when described as per classical cybernetics, act according roles and rules.
By contrast, the actors in a human social system have free will and can act as they choose.
This prompts the question as to how people do, or should, make choices.
A biologist might look to instinct or homeostasis as the basis for making decisions.
A psychologist might look to emotions or Maslow’s hierarchy of needs as the basis for making decisions.
A sociologist or mathematician may take a different perspective.
A sociological perspective
Herbert Alexander Simon (1916 to 2001) was a political scientist, economist, sociologist, psychologist, and computer scientist.
According to Wikipedia, Simon argued that fully rational decision making is rare: human decisions are based on a complex admixture of facts and values.
And decisions made by people as members of organizations are distinct from their personal decisions.
He proposed understanding organizational behavior in humans depends on understanding the concepts of Authority, Loyalties and Identification.
A theory of why and how humans conform to group norms has to start from the evolutionary advantage it gives them.
Authority, Loyalties and Identification are matters for biology and management science rather than a general system theory.
A mathematical perspective
Game theory is concerned with choices made by agents interacting with each other – as in a game of poker.
By contrast, decision theory is concerned with the choices made by agents regardless of such a structured interaction.
“Decision theory (or the theory of choice) is the study of the reasoning underlying an agent's choices.
Decision theory can be broken into two branches:
· normative decision theory, which gives advice on how to make the best decisions, given a set of uncertain beliefs and a set of values; and
· descriptive decision theory, which analyzes how existing, possibly irrational agents actually make decisions.
Empirical applications of this theory are usually done with the help of statistical and econometric methods, especially via the so-called choice models, such as probit and logit models.
Advocates for the use of probability theory point to the work of Richard Threlkeld Cox, Bruno de Finetti, and complete class theorems, which relate rules to Bayesian procedures
Others maintain that probability is only one of many possible approaches to making choices,
such as fuzzy logic, possibility theory, quantum cognition, Dempster–Shafer theory and info-gap decision theory.
And point to examples where alternative approaches have been implemented with apparent success.” Wikipedia 31.12/2018.
Decision theory is beyond the scope of this work on system theory.
The Wikipedia entry on Decision Theory will give you other links to follow.
For some practical case studies, look here http://www.attwaterconsulting.com/Papers.htm
They feature (for example) the use of a Bayesian approach with Markov Chain Monte Carlo numerical methods.
The applications include assessing the risks of mechanical system failures, in order to inform decisions about their use and maintenance.
In the real world, we make decisions about decision making.
We can choose to make a decision or make no decision (kick the can down the road).
We can choose to make a decision using strictly mathematical analysis, or using a “Pugh matrix”, or using intuition/experience.
For many of the business decisions that top-level managers are paid to make:
· the time or cost of strictly mathematical analysis is too high, or
· the data/numbers entered are unreliable guesses.
This section conforms to the tradition of referring toa social entity as a social group.
In the theory of evolution by natural selection, can a social group be treated as an organism?
Can selection between groups (favoring cooperation) successfully oppose selection within a group (by competition)?
Thinkers who addressed this include:
· Lynn Margulis – the evolution of cells, organisms and societies
· Boehm – the evolution of hunter-gatherer groups
· Elinor Ostrom – the formation of cooperatives.
The evolution of cells, organisms and societies
Lynn Margulis (1970) proposed how nucleated cells evolved from symbiotic associations of bacteria.
The general idea is that members of groups can become so cooperative that the group becomes a higher-level organism in its own right.
The idea was later generalized (Maynard Smith and Szathmary 1995, 1999) to explain other major transitions, such as the rise of
· multicellular organisms
· eusocial insect colonies
· human evolution.
It is common for people draw to a questionable analogy from biology to sociology.
The cooperation between cells in a biological organism is inflexible, rule-bound.
The interactions between people in a business organization are flexible, and may be ad hoc, created by individual actors.
Moreover, the same people may realise several different, possibly conflicting, systems.
The evolution of hunter-gatherer groups
Hunter-gatherer societies are famously egalitarian, but not because everyone is nice to everybody else.
Group members can collectively suppress bullying and other self-aggrandizing behaviors within their ranks.
Boehm (1993, 1999, 2011) saw this as the defining criterion of a major evolutionary transition in human society.
With little disruptive competition within a group, succeeding as a group became the main selective force in human evolution.
The formation of cooperatives
Consider a group of people who share access to resources.
Such as fishermen who share fishing grounds, or farmers who share an irrigation system.
How to avoid “the tragedy of the commons” by which competition exhausts the common resource?
For a while, the fishermen must stop fishing, and farmers stop farming, to define the rules of their social system – a cooperative.
Elinor Ostrom (1990, 2010) defined eight generic conditions for such a cooperative.
1 Clearly defined boundaries
members know they are members of a group and it aims
2 Proportional equivalence between benefits and costs
members must earn benefits and can’t just appropriate them
3 Collective choice arrangements
members must agree decisions so nobody can be bossed around
5 Graduated sanctions
6 Fast and fair conflict resolution
disruptive self-serving behaviors must be detected and punished
7 Local autonomy
the group must have the elbow room to manage its own affairs
8 Appropriate relations with other rule-making authorities
all rules above apply equally to inter-group relations
This section above was edited from this paper http://evonomics.com/tragedy-of-the-commons-elinor-ostrom by David Sloan Wilson.
Read that paper for more detail and references.
David Wilson reports projects that successfully applied Ostrom’s eight principles
However, there are two constraints on how widely applicable they are.
First, the eight conditions are very demanding.
Second, the notion of pre-modern grouping of humans by geographical location has largely broken down.
The modern transformation of social groups
In the ancient world, humans (like apes) were naturally grouped by geography.
They communicated only with people in the same location or territory.
First, vehicular transport transformed our ability to mix in different societies.
Then, telecommunications transformed our ability to communicate remotely.
What is now is a social group?
How does an individual actor join or leave a group? Who decides?
How many groups can one individual be a member of? Are there degrees of membership?
How does an actor prioritise, apportion time and attention, between groups they belong to?
How do they reconcile the conflicting norms or goals of different groups?
Niklas Luhmann (1927–1998) was a German sociologist and student of Parsons.
Like writers a century earlier, he presumed a system is homeostatic and sustains itself, though in a very curious way.
David Seidl (2001) said the question facing a social system theorist is what to treat as the basic elements of a social system.
“The sociological tradition suggests two alternatives: either persons or actions.”
Luhmann chose neither, he proposed the basic elements of a social system are communicative events about a code that lead to decisions that sustain the system.
Each social system is centred on one code, which is a concept such as “justice” or “sheep shearing”.
He endorsed the “hermeneutic principle” that the hearer alone determines the meaning of a communicative event.
Read Luhmann’s ideas for more.
Luhmann’s system is radically different from systems as understood by most other system theorists.
It is well-nigh diametrically opposed to that of classical cybernetics.
Since the system has no persistent structure, no persistent state, and no memory of communication events.
And the hermeneutic principle is contrary to common sense and to biology.
Since communication requires a receiver to decode the same meaning from a message that a sender intentionally encoded in that message.
Luhmann’s whole scheme (like that of Parsons before him) seems more metaphysical than scientific.
Having said that, the idea of system based on a code might been seen as having a counterpart in more general system theory
That is a “domain-specific language” for communication of information about entities and events related to one body of knowledge.
Jürgen Habermas (born 1929) was a critic of Luhmann’s theory of social systems
He developed the social theory of communicative reason or communicative rationality.
According to Wikipedia, this distinguishes itself from the rationalist tradition, by locating rationality in structures of interpersonal linguistic communication rather than in the structure of the cosmos.
It rests on the argument called universal pragmatics – that all speech acts have an inherent "purpose" – the goal of mutual understanding.
He presumed human beings possess the communicative competence to bring about such understanding.
And hoped that coming to terms with how people understand or misunderstand one another could lead to a reduction of social conflict.
A theory of why and how animate entities communicate has to start from the evolutionary advantage it gives them.
Natural human language is inherently fluid and fuzzy; it is a tool for social bonding and communication, but can easily lead to misunderstandings.
Papers now written under the heading of “systems thinking” are often unreadable
E.g Try finding and reading this paper on the web “The Non-Systemic Usages of Systems as Reductionism: Quasi-Systems and Quasi-Systemics”.
A reader writes:
Having quickly read that paper I can't decide whether to call it pseudoscience or deism!
Or Dadaism - an artistic movement from the early 20th century who's purpose was to ridicule the modern world
It is exactly the kind of writing on systems that John Gall's "Systemantics" sends up.
Much "systems thinking" discussion is about the human condition rather than systems of the kind in Ashby’s cybernetics and Meadows system dynamics.
I once wrote a 50-page pamphlet on "human factors in hierarchical organizations".
It never occurred to me that anybody would relate it to system theory.
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