More on self-organisation varieties
Copyright 2017-9 Graham Berrisford. One of more than 100 papers on the “System Theory” page at http://avancier.website. Last updated 30/11/2019 19:03
Ashby’s classical cybernetics has been called the science of observed systems.
His system is what an observer sees as the regular behavior of a material entity or real-world phenomena.
Ashby’s student, Krippendorff, wrote:
“What we know of a system always is an ‘observer’s digest’.”
Heinz Von Foerster is credited with initiating second-order cybernetics – which he said to be the science of observing systems.
He wrote that, in the years after classical cybernetics was introduced:
“Something strange evolved among the philosophers, the epistemologists and, the theoreticians.
They began to see themselves more and more as being included in a larger circularity; maybe within the circularity of their family;
or that of their society and culture; or even being included in a circularity of cosmic proportions!”
"Second-order cybernetics is the science of observing systems." von Foerster "Ethics and Second-Order Cybernetics"
Von Foerster posed a question - typical of his playful phrasing of aphorisms and questions.
“Am I a part of the system, or I am apart from the system?”
Was this really a new question? Krippendorff wrote:
“Although second-order cybernetics (Foerster et al. 1974) was not known at this time, Ashby included himself as experimenter or designer of systems he was investigating.” Krippendorff
Imagine we asked Ashby: Are you a part of the system, or you are apart from the system?
Here is a reply based on Ashby’s writings.
“Heinz, I can be both! Cybernetics does not ask "what is this thing?" but ''what does it do?"
For sure, I can be an actor who plays a role in a social or socio-technical system,
But I can separate myself from what I do in performing that role.
I can both act in the system, and observe the behavior I contribute to it.”
“One of Ashby’s goals was to repudiate that interpretation of self-organization, commonly held, that a machine or organism can change its own organization.”
(Goldstein’s introduction to Ashby’s 1962 treatise on self-organisation.)
To make sense of self-organization, Ashby’s divided it into two kinds characterized below as rule-using and rule-setting.
Under this heading, Ashby wrote:
“Changes from parts separated to parts joined”
“Self-connecting” “Perfectly straightforward”
Ashby’s self-connecting of parts may be divided into at least two kinds.
This means growth or increase by the gradual accumulation of additional layers or matter.
E.g. The accretion of a crystal growing in a super-saturated liquid is a simple process that has been called self-organisation.
The accretion of a city growing as it attracts more people and money has also been called self-organisation.
This means to move or go together in a crowd.
E.g. The shoaling behavior of fish.
The behavior of a flock of starlings wheeling in the sky.
In both examples, many simple interactions between many adjacent actors produces a complicated moving shape.
However, the complexity of these state change (visual) effects is more in the eye of the observer than in the system itself.
To Ashby’s joining of parts, we can add other says in which order emerges from following rules.
The means the emergence of an orderly structure or state from interactions between initially disordered elements.
Noise is random events or perturbations that stimulate a system to move through a variety of states in its “state space”.
As it does this, the system may arrive near an “attractor” drawing it into a steady state, or an orderly state change pattern.
This kind of self-stabilising behavior can be observed in simple mechanical systems.
It is a feature of classical rather than second-order cybernetics.
Self-regulation means an entity maintains its own state in a stable, orderly or homeostatic fashion.
Maturana coined the term “autopoiesis” for the self-sustaining nature of biological organisms.
Self-sustaining means the system’s processes maintain the structures that perform processes that (and so on).
Ashby wrote of how a system can appear to be to self-organizing.
“Changing from a bad way of behaving to a good.”
“No machine can be self-organizing in this sense.”
“The appearance of being self-organizing can be given only by the machine S being coupled to another machine x.
Then the part S can be self-organizing within the whole S+x.”
In Goldstein’s introduction to Ashby’s 1962 treatise on self-organisation, he described Ashby’s approach in this way.
“A new higher level of the machine was activated to reset the lower level's internal connections or organisation.”
Ashby’s higher and lower level machines are “coupled” in a wider or aggregate machine.
But the aggregate machine is only ever partially self-organising, since one part of the aggregate machine always drives the change to the other part.
This means changing behaviour in a pre-ordained way.
This means switching a system (whether randomly or by design) from one already-configuration to another.
This means the inexorable growth an embryo into an adult
By any measure, the morphogenesis of an organism is a complex process.
The process, predetermined by DNA, inexorably builds an adult organism from an egg.
As the process proceeds, new kinds of component and interaction emerge, increasing the complexity of the organism.
This means changing the behaviour of a system (randomly or by design) from one generation to the next.
Where the new generation is created or invented.
The principle derivable from Ashby’s work is this: to re-organize a system we need a distinct process or meta system
Any entity that realises a system (S) must follow its rules.
To change S, we need higher process or meta system (M).
If M is to change a real machine that realizes S, it must:
1. take as input the abstract system (S version N) realized by the real machine
2. transform that input into a new abstract system (S version N+1)
3. trigger a real machine to realise the new abstract system.
To change a system from bad to good implies that M has a sense of better and worse.
M may iteratively make random changes to a system, favouring ones that enable an entity to behave better in some pre-defined way.
An intelligent M is marked by the ability to understand the abstract system and invent changes that are likely to make it better.
Self-organisation = absence of a design or pattern?
This means there is no law, rule or definition of how a system forms or changes.
However, one may say there is a blueprint for much so-called “self-organisation”.
The blueprint for self-organisation in:
· a solar system is found in the laws of physics
· a molecule is found in the chemists’ periodic table
· a biological organism is found in its DNA.
· a social system is found in the minds or documents of its actors.
In the first three examples, the self-organisation is predetermined and predictable (in theory if not in practice).
Self-organisation = decentralised control?
Decentralisation means there is no central control - no overarching controller of system processes.
Rather, the processes of the system are distributed between atomic components or agents. E.g.
In sociology: this may be called anarchy or a participatory democracy (which is a vision, many social systems thinkers endorse).
In software: this corresponds to the “choreography” design pattern rather than the “orchestration” pattern.
The latter is found in very simple software systems; does not imply “self-organisation”.
Self-organisation = business reorganisation?
All the processes above are very different from the “self-organisation” of a social or business organisation.
The morphogenesis of a business organisation is a process that leads to outcomes that are not inexorable or pre-determined.
Organisation changes are stimulated by a variety of internal and external forces - that might either complexify or simplify the organisation
Ultimately, external forces (political, economic, social, legislative and environmental) dominate the internal ones.
A business, though it relies on countless systems, is not is well described as a system - as a whole.
Inside all business organisations, the actors behave in a mix of ad hoc and regular ways.
Sometimes actors act in business systems, where they behave regular ways; often they act outside of any describable system.
Some of the ideas expressed by second order cyberneticians are axiomatic.
They underpin not only classical cybernetics but all modern science.
What might be called self-organisation appears in many different forms.
There are self-regulating, self-sustaining, self-stabilising systems.
However, Ashby and Maturana rejected the idea the notion of a “self-organising system”.
Since it undermines the very idea of a system in classical cybernetics and more general system theory.
"If the system is to be self-organising, the self must be enlarged to include… some outside agent." (Ashby 1962)
The roles and rules of a lower level system cannot be set or changed from inside the system.
But they can be set or changed by a higher level process or meta system.
Some systems cannot be changed by the actors who play roles in them.
The variables and rules of this system
are found in
Planets in a solar system
The law of gravity produced by the universe
Termites building a termite nest
The DNA produced by a process of reproduction
Players in a tennis match
The laws produced by the Lawn Tennis Association.
By contrast, many human social systems are partly or wholly defined by the actors who play roles in them.
In this context, the term self-organisation means the actors who play roles in a system can observe and change the variables or rules of that system.
For sure, the actors who play roles in a system may also observe it, and agree to change its variables, roles or rules.
But whenever actors discuss and agree such a change, they are (for that time) acting in a higher level or meta system.
And once the change is made, the actors (still members of the same social network) now act in new roles in a new system (or system generation).
One actor can act as system definer who changes the roles and rules of a social system they (separately) play a role in.
However, one action is in one or the other system – not in both.
The need for re-organisation to be incremental
Some social systems thinkers treat the idea of self-organisation as a political agenda or mission,
They promote the idea of a “participatory democracy”.
The term implies actors must understand and agree to any change before it can be rolled out
Such inter-generational evolution is different from chaotic ad hoc change.
If actors continually change the properties and functions of the organisation they work in, then the concept of a system is lost.
So, note that continuous mutation is impossible here, since it is contrary to the notion of a system.
Distinguishing social networks from social systems
Ashby urged us to distinguish a system (a set of variables) from the real machine or animal that realises it.
He said one machine or animal can realise infinite different systems.
We need to distinguish:
· a social system - a set of roles and rules that actors may comply with
· a social network - a group of inter-communicating actors who can realise any number of systems.
The second order cybernetics idea of a self-organising social system confuses the two concepts.
If the roles in system S include actions that change the roles in system S, that makes a nonsense of the system concept.
Imagine several actors, who currently play the same role, each changing that role – as they see fit - while the system is running.
The result is the opposite of a system; it leads to chaos - to disorderly, irregular and likely uncoordinated behaviour.
Distinguishing a higher or meta system from a lower system
Ashby would surely agree that a human actor playing a role in system S can observe that system and envisage changes to it.
But to adhere to classical cybernetics, that change must be made under change control.
Ashby’s concept of a higher level machine helps us reconcile classical cybernetics with self-organisation.
To change a role in a system S, the actor must step outside the lower system to act (however briefly) in a higher level or meta system (M) to system S.
Consider how two tennis players can change the rules of a tennis match they are playing.
They stop the match (step outside it) agree a rule change, then restart the match.
Via successive changes, the two players may radically change the nature of a tennis match.
"Change the rules from those of football to those of basketball, and you’ve got, as they say, a whole new ball game.” Meadows
Consider how a society can avoid “the tragedy of the commons”.
The lower level system is a group of people who share access to limited 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?
The meta system is the cooperative in which the fishermen or farmers agree their rules.
Now and then, the fishermen must stop fishing, and farmers stop farming, to define the rules of their social system.
(Elinor Ostrom (1990, 2010) defined eight generic conditions for such a cooperative.)
This idea needs a name, and for the want of anything better it is here called 3rd order cybernetics.
3rd order cybernetics seems a better fit (than second order cybernetics) to most systems of interest to us, including social systems.
The need for a circular sense-respond loop
Obviously, people, in everyday life and in business, must use their imagination and creativity.
They must communicate, learn from each other and respond in ad hoc ways to unforeseen inputs.
To verify any non-trivial logic requires social verification by peer review and/or empirical verification by observation of test results.
All system designers, enterprise architects and software developers know the importance of peer review and testing.
And all must respond to the experience of system implementation.
We don’t need second order cybernetics to tell us this circular sense-respond loop is vital.
Distinguishing the motivation of people in a social network from the behaviors of a system
Of course, managers may coordinate human actors in a social network by giving them a goal, or asking them to agree a goal.
Business managers may create an organisation in which people are given only goals (not rules).
And then encourage those people to act and cooperate however they see fit.
That is not a general system theory; it is a very special human-only system theory, and little or nothing to do with cybernetics.
It is better called “management science” or some such, than system theory.
Professor Manfred Eigen was a Nobel prize laureate in chemistry. https://lnkd.in/dsVBPYc
He and Peter Schuster researched and published on hypercycles, in the trans-disciplinary domain of bio-physical chemistry.
Hypercycles demonstrate natural phenomena of self-organisation.
They demonstrate functionally coupled self-replicative entities.
A hypercycle is a cyclic symmetry of autocatalytic reactions that are arranged in a circle so that each reaction's product catalyses the production of its clockwise neighbour.
What does "self-organising" mean here? It sounds more like self-sustaining (autopoeisis), self-perpetuating, or cyclical symbiosis.
Ashby modelled systems as machines that are predictable to some extent.
He wrote of determinate machines, which respond predictably to each combination of input and current state.
And wrote of Markovian machines, which respond less predictably, using probability rules.
Even if his machine could have well-nigh infinite states, it cannot have infinite functions/rules/probabilities.
And to add a new variable or function/rule/probability is to make a new machine/system/organisation.
Ashby did not mention Turing machines.
A reader has suggested a Turing machine (as opposed to a finite state machine) can be self-organising.
A Turing machine is "a mathematical model of a hypothetical computing machine which can use a predefined set of rules to determine a result from a set of input variables."
Can a Turing machine change its predefined set of rules? Can it invent new variables or new rules?
Suppose a Turing machine could organise itself, then we’d want know:
· In what ways can it change its own state variable types or rules?
· Are change options prescribed within the machine, or infinite?
· What triggers the machine to change itself?
· How does it choose between changes it might make to itself?
Moreover, note that in most systems of interest to us, many actors or machines can play the same role
When a state variable or rule is changed, how it is distributed to all actors in the system?
Pending answers, I favour an Ashby-like version of self-organisation, in which a higher or meta system changes the description of another.
It seems a better fit to the social and business systems of interest to us.
Of course, there is much to be said about human organisations that is outside of system theory.
· John Kotter: Organizations won't change unless there's a "burning platform".
· Thomas Kuhn: New models are accepted when the adherents of the old models retire.
· James C. Scott: Organizations become optimized to make them easier to observe and control, which is at odds with making them better and more efficient.
· Public Choice Economics: Organizations don't have goals. Individuals in organizations do.
· Bruce Bueno de Mesquita: People at the top have to be good at accumulating power. Act as if that's their only goal.
· Pournelle's Iron Law: "In any bureaucracy, the people devoted to the benefit of the bureaucracy itself always get in control and those dedicated to the goals the bureaucracy is supposed to accomplish have less and less influence, and sometimes are eliminated entirely."