Is systems thinking science or poetry?
Including signs and symptoms of shamanism or pseudo science
Copyright 2016 Graham Berrisford. One of several hundred papers at http://avancier.website. Last updated 06/11/2018 23:45
This essay explores the notion of pseudo science.
It explores whether social systems thinking is science or poetry.
Where poetry means more or less related to reality, but open to whatever interpretation the reader brings.
And asks: What if anything does “systems thinking” add to “human situation thinking”?
Finally, the paper draws a conclusion that is wholly unrealistic.
The scientific method involves testing a thing against a theory.
Software engineers do this: they test that a system in operation matches a system description.
Those who design and test human activity systems do the same.
As a human individual, you act, in different roles, in a variety of systems that have been carefully designed and tested.
In December 1999, a poll of scientists in Physics World magazine ranked Richard Feynman (1918- 88) as one of ten greatest physicists of all time.
He left us with insights that go beyond the world of physics.
He spoke about pseudo science in this BBC interview in 1981 (the whole series is worth viewing).
“Because of the success of science, there is a kind of a pseudo science.
Social science is an example of a science which is not a science.
They follow the forms. You gather data, you do so and so and so forth,
but they don’t get any laws, they haven’t found out anything.
They haven’t got anywhere – yet. Maybe someday they will, but it’s not very well developed.
“At an even more mundane level, we get experts on everything that sound like they are sort of scientific, expert.
They are not scientists. They sit at a typewriter and they make up something like:
‘a food grown with a fertilizer that’s organic is better for you than food grown with a fertilizer that is inorganic’.
Maybe true, may not be true. But it hasn’t been demonstrated one way or the other.
But they’ll sit there on the typewriter and make up all this stuff as if it’s science.
And then become experts on foods, organic foods and so on.
There’s all kinds of myths and pseudo science all over the place.
“Now, I might be quite wrong. Maybe they do know all these things. But I don’t think I’m wrong.
See, I have the advantage of having found out how hard it is to get to really know something.
How careful you have about checking your experiments, how easy it is to make mistakes and fool yourself.
I know what it means to know something. And therefore, I see how they get their information.
And I can’t believe that they know when they haven’t done the work necessary.
They haven’t done the checks necessary, they haven’t done the care necessary.
I have a great suspicion that they don’t know and that they are intimidating people by it.
I think so. I don’t know the world very well but that’s what I think.” (Richard Feynman, transcribed from a 1981 BBC TV programme)
In short, pseudo science is the practice of asserting things to be true in a way that sounds scientific, but without the support of evidence accepted by scientists.
Science is more than asserting a theory; it involves testing assertions in an attempt to confirm or deny them.
As Thomas Edison said: “Genius is one percent inspiration, ninety-nine percent perspiration.”
In science, the one percent inspiration is the creation of a hypothesis (which may be intuitive, near magical).
The ninety-nine percent perspiration is the work to test whether that hypothesis is true - whether it is knowledge or only supposition.
This work involves logical analysis, research, experiments and testing of physical realities.
Software engineering is not immune to pseudo science.
System design fashions (OO, CBD, SOA, Microservices) come and go with little or no testing of one fashion against another.
As long the code works, people don’t look into whether it works optimally or not.
Karl Popper advanced the view that hypotheses or theories should be readily falsifiable.
In other words, a well-formed hypothesis or theory is potentially disprovable by measuring test results.
Consider for example the following assertions, proposals, statements, hypotheses or theories.
Four well-formed theories
Eating carrots gives you night vision.
This is easily tested, and known to be wrong.
The myth derives from rumours that British pilots in WW2 had night vision powers thanks to their healthy diet of carrots.
Gum stays in your stomach for seven years.
This is easily tested by placing some gum in stomach juices for a while.
Tests show any food substance we swallow is dissolved and excreted within days - most within hours.
Eating cheese before bed will give you nightmares.
Tests by the British Cheese Board found no relation between eating cheese and an increase in nightmares.
Organic foods are more nutritious and safer.
This is harder to test, since it requires measuring many people over a long time.
In 2012 Stanford University’s Centre for Health Policy did the biggest comparison of organic and conventional foods.
It found no robust evidence for organics being more nutritious.
In 2016, a review repeated its finding: “Scientific studies do not show that organic products are more nutritious and safer than conventional foods.”
Four not so well-formed theories
The biomass on earth is a coherent organism equipped with a sort of intelligence
How could this assertion be disproved?
It makes a peculiar, one might say poetic, use of the words “coherent organism” and “intelligence”.
To say the biomass is an organism is an analogy whose meaning and testability is unclear – more on this later.
IBM is a complex adaptive system
Is this a theory or an axiom? More on this later.
Raising the interest rate is the best way to reduce inflation
Even if it reduces, the trouble is, we don’t know what the alternative policy would have done.
“we cannot simultaneously compare two policies if one says to raise interest rates and the other says to lower them.
Our experiments are not repeatable. You can’t dip your toe in the same river twice.” Steven E Wallis
This management “intervention” will solve your problem
Again, it may be surprisingly difficult to disprove this assertion.
Suppose managers invest both financial and political capital in making some recommended changes.
After they have done that, they may have no wish to measure or test the effect of those changes.
If they do measure the results/outcomes, the findings may be disputable because:
· failure may be attributed to any unforeseeable interference from sources beyond control
· success has a thousand fathers – others may claim the success is down to them
· failure or success may be attribute to the Hawthorne Effect.
The Hawthorne Effect is a well-documented phenomenon that affects many experiments in social systems.
It is the process where human subjects of an experiment change their behavior, simply because they are being studied.
E.g. Productivity improves, not because of any particular management intervention, because managers show interest in their workers.
A doctor who treats people without the support of evidence accepted by scientists is often called a shaman.
That may be unfair on real shamans, but here, the word shamanism is used in that narrow sense.
Watch out for assertions, proposals, statements, hypotheses and theories associated with any of the following.
The implicit agenda
A proposal may be made to advance a hidden agenda, perhaps a political one.
The misuse of scientific terms or concepts
A proposal may use words from hard sciences, but use them differently, or questionably.
Doing this may give a theory the appearance of certainty without the substance.
The misleading analogy
A proposal may be based on belief in a misleading analogy.
“Software engineering is like electrical or electronic engineering.”
“Enterprise architecture is like building architecture, or city planning.”
The fear of calamity
This tactic has often been used by politicians and economists.
There must be a wrong before people will buy a remedy, a condition before a cure.
Fear of a calamity may scare people into swallowing the medicine that is offered.
The questionable correlation or statistic
A proposal may be based on misconstruing a correlation or statistic as a cause-effect relationship.
Example 1: an increase in sales of ice cream causes an increase in deaths by drowning.
Does the former cause the latter? Of course not; another variable (sunshine) triggers both outcomes.
Example 2: so far, scientific research has concluded that giving vaccines to children does not cause autism.
It is presumed that the first symptoms of autism are related to another variable - the age when vaccines are given.
The rare success
Suppose the prescription is: “Adding people to a late-running project brings it back on track.”
Twenty managers test that theory on real projects; 19 projects fail, but no manager publicises that fact.
One project succeeds, and the manager writes up the project as a case study published in – say – The Harvard Business Review.
CEOs all over the world read the paper and urge late-running projects in their organization to add more people.
The theory that cannot be disproved
The average shelf life of conclusions published in scientific journals is surprisingly short (reference to be added).
Pseudo-scientific theories can survive longer than scientific ones, because it is impossible to disprove them.
Suppose I announce a theory and use it to recommend an action.
If the action it succeeds, you may be suspicious, because success has a thousand fathers.
If the action fails, I may say you didn’t try hard enough, or unforeseeable phenomena got in the way.
E.g. a consultant may assert a debt crisis can be solved by accruing more debt (a de facto policy implemented today).
Without evidence to support or contradict the policy, you can’t rationally argue for or against it.
If the policy fails it’s only because you did not pile on more debt fast enough, and so, the guru is always right.
Of if the policy succeeds, it might be down to other influences – say, unforeseeable market forces.
General system theory includes some common sense notions of a system.
A simple analysis of natural language usage (here) suggests these four system properties are widely recognised.
· Wholeness (or holism): parts cooperate in processes that produce the properties of the whole.
· Inter-relationship of components: all parts are related directly or indirectly.
· Orderly or rule-bound behavior: parts and processes are bound by the rules of physics, chemistry or man.
· System boundary (or encapsulation): parts inside the system are separable from things outside.
Suppose somebody refers to “the Chilean economic system”; what does that mean?
Any two people may answer those questions very differently.
People do casually point to an entity (or aggregate of entities) in the world and call it a system.
Many systems thinkers have recognised this naive.
Checkland wrote that system as a perspective of a reality or “Weltenshauung”.
Ashby wrote: “we must be clear about how a "system" is to be defined.
Every [concrete entity has] an infinity of variables and therefore of possible systems.
We [must] pick out and study the facts that are relevant to some [given] interest.”
To identify a system is to abstract an island of stable orderly behavior from an infinitely complex and ever-changing universe.
That behavior must be describable and testable.
E.g. the behavior of the planets can be tested for conformance to an astronomer’s descriptions of their orbits.
The behavior of a pair of sticklebacks can be tested for conformance to a biologist’s description of their roles in mating.
The behavior of a US government can be tested for conformance to the description of those behaviors in the US constitution.
A physical system
A biological system
A social system
Abstract system description
Male and female role descriptions
Concrete system realization
Planets in motion
A US government
Scientific systems thinkers create system descriptions (theories) that lead to predictions testable in operational systems (reality).
The hypothesis-test process might be distilled thus:.
· Scope: observe or envisage an entity as realising the repeatable activities of a system.
· Abstract: describe/model the effects of those activities on measurable state variable values.
· Realise: observe (or build and set) the social network in motion, and note the results/outcomes of activities
· Test: check that the results/outcomes of the social network match what the system description/model predicts.
If the reality departs from what a prediction, then the system description may be discarded or changed, or the real-world social network might be corrected to match it.
You can use Systems Dynamics to model anything describable in terms of flows between stocks, populations or resources.
The stocks may be of passive structures (food items, widgets, hospital beds) or actors (machines, rabbits, viruses).
Read Systems thinkers and their ideas for a history with System Dynamic ideas advanced Forrester and Meadows.
Systems Dynamics has been used in a scientific way to predict changes in stocks or populations.
The hypothesis-test process runs thus.
· Scope: observe or envisage an entity as a set of stocks related by inter-stock flows.
· Abstract: describe/model the effects of those flows (events) on measurable stock (entity) levels.
· Realise: set the abstract model in motion, and note the trajectory of stock level changes over time.
· Test: check that stock levels in the real world change over time as they do in the model.
You start with a theory of how discrete stocks/resources increase and decrease in response to events.
You model events as flows that connect one stock to another, perhaps in a causal loop diagram.
You turn that static diagram into dynamic model of flows between stocks.
Then run the model forwards from an initial state to predict the long-term outcome of those interactions between stocks.
Still, running the model does not prove it; and proving it may be difficult for various reasons.
It may be impractically difficult to test that the abstract model matches the corresponding concrete reality.
Subtly different versions of the model (or initial states of one model) may lead to substantially different outcomes.
The effects of real world stocks and flows outside those in the model may cause the concrete system to depart from the abstract one.
Also, if one of stocks in the systems is a human population, then those people may change the dynamics of the system.
Even if concrete and abstract systems produce the same result/outcome at one point in time, the trajectories of change to that point may differ.
And suppose, the changes to real world variables and modelled variables follow the same trajectory, but merely continue a past and linear trend?
The following is a conjecture, rather than a fact I have evidence for.
As a predictor, a model matching straight line growth in reality is less convincing than one which matches a more complex graph.
Why? Two different models may produce the same straight line graph - more likely than produce same complex curve.
Suppose that the straight line graph matches behavior in the real world, then, which of the two models is correct?
The straight lines produced by those two models may (later) change angle (dramatically or not) at different points in time.
On “The limits to growth”
We surely are headed for a worldwide calamity, sooner or later.
If a virus, a volcano, a comet, magnetic pole switching, or global warming doesn’t get us sooner, the sun will burn out later.
Also, infinite population growth is impossible, and it could reach a point where dramatically catastrophic effects will occur.
However, we still can’t predict the weather next month, and it is difficult to be confident about predictions of when a catastrophe will happen.
“In the 1970s, the Club of Rome (Meadows et al, 1972) released its first report “The Limits to Growth”.
The scientists and philosophers of the Club took a systemic look at the development of present-day civilizations
by consideration the interactions of global subsystems in the areas of population growth, agricultural production, dwindling resources and pollution.
On the basis of the computer simulation of the future course of the world ecology, they predicted worldwide calamity by the year 2025.” Ref. 4
Meadows’ team modelled industrialization, population, food, use of resources, pollution.
They modelled the historical data, then several scenarios up to 2100, with varying assumptions about action on environmental and resources.
One model – the “business-as-usual” model - predicted a catastrophic collapse in the economy, environment and population before 2070.
Nearly half a century after the report was published, it was pointed out that the “business-as-usual” model matches reality pretty well so far.
Look at the graphs in this article.
A conclusion was drawn that the catastrophic collapse will happen when the model predicts it will happen.
So far, the graphs show near-to-linear continuations of past trends, which is a default prediction.
The predicted break from linear change to non-linear change may well happen.
But it remains impossible to be confident the model predicts when a catastrophe will occur.
Or that the primary causes will be the same as those in the model.
Or that changes will be dramatically catastrophic, because when things start to go badly wrong, some international action is likely.
(For example, using more of the massive amount of food that is either rejected or thrown away by supermarkets.)
For sure, some actions (e.g. on renewable energy, on plastics) should be taken now.
The aim here is not to question that, only to explore what it takes to be confident that a system model or theory is accurate.
By the way, aside from a doubling of the global population, what has happened in 50 years since “The limits to growth” was published?
Surprisingly, the United Nations report huge advances in the health, life expectancy, education and welfare of people across the globe.
Google anything you can find from Hans Rosling, especially “200 Countries, 200 Years, 4 Minutes - The Joy of Stats”.
Again, this not to question the need for actions now, only to explore what it takes to be confident that a system model or theory is accurate.
Much systems thinking discussion uses scientific-sounding words in questionable ways.
If actors in a social network changes the roles or rules of a system they realise, that may be called self-organization
But if the actors make changes continuously, then that social network cannot reasonably be called an “organization” at all.
Complexity theory is an odd term, since there is no agreed measure of complexity.
By complex system, people often meaning it has non-linear dynamics.
Or that system changes the state of world in a chaotic fashion.
But the equation of complex and chaotic is misleading.
Even a very simple orderly system can produce chaotic results over time (as may be revealed by System Dynamics).
Some systems thinkers use related terms like “strange attractors” and “fractal geometry”.
Other scientific-sounding terms include “emergent properties”, and “entropy”.
Some radically change the meanings of terms; e.g. Luhmann radically reinterpreted the biological concept of “autopoiesis”.
“Complex adaptive system”
MIT offer this definition of a Complex Adaptive System.
"Complex Adaptive Systems are dynamic systems able to adapt in and evolve with a changing environment.
It is important to realize that there is no separation between a system and its environment in the idea that a system always adapts to a changing environment.
Rather, the concept to be examined is that of a system closely linked with all other related systems making up an ecosystem.
Within such a context, change needs to be seen in terms of co-evolution with all other related systems, rather than as adaptation to a separate and distinct environment."
Surely this describes any and every network of actors who communicate with each other?
“IBM is a complex adaptive system that exhibits both linear and non-linear behavior.
As per chaos theory, incremental changes to one of its state variables can turn linear quantitative change into qualitative change.
There comes a point where the system behaves in a qualitatively different way.
As per catastrophe theory, the topological shape of the system state space may change dramatically.” Source lost
What does it mean to assert that IBM is “a system”? And how measure its complexity or adaptiveness?
OK, IBM is a social network in which some actors interact in performing some actions.
IBM might reasonably be called a complex adaptive social network, but if it is a system – what system is that?
In Ashby’s view, a social network is only a system in so far as it performs the behaviors in a given system description.
IBM is a social network that realises (instantiates, manifests) countless different systems.
Some of those systems are complex, others are simple; some are adaptable, others are inflexible.
Typically, people use the term complex adaptive system when speaking of a human organization or institution.
But such a social network can be described from different viewpoints as realising countless different, even conflicting, systems.
Adaptive means the social network may gain and lose actors, may change its activities and aims, and may change the roles and rules of any system it realises.
But that may be said of any social network of actors who communicate with each other, directly or indirectly.
General system theory (GST), which emerged in the 1950s, is about concepts and principles applied in each of sciences.
Some think social systems thinking derives from, or is an advanced application of, GST.
However, much of what people now call “systems thinking” started much earlier, in 19th century sociology.
Before and after GST emerged, some have told system stories that are more mystical or magical than scientific.
Read Systems thinkers and their ideas for a history that contains links and references to many systems thinkers.
Many sociologists have looked at a social network as though it were a biological organism.
Considering social stability, some have applied the idea of homeostasis, as in a biological organism, to a society.
Considering social reorganisation, some have applied the notion of evolution, after Darwin, to a society.
A misleading analogy?
Drawing an analogy between sciences isn’t the same as applying the scientific method.
Ackoff deprecated the organismic model of a social network; as others have done.
“Organic analogies are inadequate as models of social systems.” Ref. 4
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”
A business might organise its actions so as to be regular or repeatable, allowing actors to organise themselves to perform those actions.
It might organise its actors under a management structure, allowing them to determine their action they perform.
Some systems thinkers focus on the management structure of organized institutions.
The fear of calamity?
Ackoff was a doomsayer about the condition of human organisations.
“Most contemporary social systems are failing. Head Start is said to be a failure.”
“The US has a higher percentage of its population in prison than any other developed country, but nevertheless has the highest crime rate.”
“Most of the corporations formed each year fail before the year is up. Half the corporations on the Fortune 500 list twenty-five years ago no longer exist.”
“One could go on citing deficiencies in the management of our principal social systems.” Ackoff 2003
The initial premise is questionable in many ways.
To begin with, most contemporary social systems succeed to some extent.
Some failures are inevitable - because the goal is impossible, it is no longer wanted, or some reason other than poor management.
There are countless reasons why managing human institutions is inherently difficult, and failures don’t have common cause.
And bear in mind that individuals in a population must die to make room for new ones.
Friedrich von Hayek used the term scientism in relation to the central planning of a nation’s economic system.
Clearly, governments must collect data for planning.
But can they monitor and/or direct any large and complex socio-economic entity from a central Operations Room?
Hayek knew of Beer and Cybersyn, and may have had them in mind when writing his Economic Sciences Nobel prize acceptance of 1974.
The speech is entitled “The Pretence of Knowledge” (click on the hyperlink to read it).
Hayek criticized centralized planning for being unresponsive to shifting realities.
He argued it was bound to fail, because it could never do what the free market’s price mechanism could do.
It is impossible to codify, collect and aggregate the knowledge, judgements and whims of individuals in the market, which guide their behavior.
In what useful sense can we refer to any large and complex socio-economic entity as a single “system”?
A nation is an entity comprising many individuals who act in countless roles in countless describable systems, some with conflicting aims.
There are infinite boundable systems, each influenced or controlled to some degree by countless other systems.
Those who see societies and institutions in terms of power relationships tend to contrast:
· centralization of control - totalitarianism, a top-down management hierarchy
· distribution of control - individualism, a participatory democracy or anarchy.
An implicit agenda?
Beer attempted to reconcile the two views above by building information feedback from workers into a centralised control and planning system.
Other systems thinkers have looked to advance “participative democracy”.
“If systems theory is applied to social processes in the manner exemplified in this book, it offers practical and ethical methods for advancing participatory democracy.” (Ref. 4)
Bausch considered that systems thinking should herald a new era of social organization.
The question here is not whether “participative democracy” a good thing, it is whether systems thinking has a political agenda.
Some see sociological systems thinking not a science, but as set of informal mental models for analysing and changing human situations.
Still one wants to know: Is there a correct interpretation of a mental model? Or only more or less useful interpretations?
Which mental models have proved successful and which have failed, in which situations?
Bausch (2001) reviewed many sociological systems thinkers, some more metaphysical than scientific.
For example, he pointed to Feynman-like criticisms of Parson’s work
“any critique of the Parsonian framework must contend with its loose conceptual structure” (Buckley quoted in Ref. 4)
Parson’s thought is “internally contradictory”, and “provides support for a variety of partial, often mutually antagonistic interpretations” (Alexander quoted in Ref. 4)
Some management scientists see business systems in terms of human psychology.
Many businesses use Myers-Briggs personality type indicators (MBTI) to categorise their employees before matching them to roles.
But the MBTI is deprecated by most psychologists
“... the MBTI exhibits significant psychometric deficiencies, notably including poor validity and poor reliability.”
A different kind of approach is Spiral Dynamics.
"Spiral Dynamics draws on the original work of Clare W. Graves, Ph.D, who began to develop a comprehensive model of bio-psychosocial development in the 1950s
It differentiates people by their ways of thinking rather than by personality types.
The first managerial application was published in the Harvard Business Review in 1966."
It is said to "go beneath the surface of human systems to help you understand the whole picture and the issues you need to tackle to steer forward."
Spiral dynamics theory classifies people’s “world views” into 8 kinds.
It proposes people take all 8 views at once and mix them in different degrees when viewing different realities.
As far as I know, the theory is not taken seriously by most psychologists.
A misuse of scientific terms?
An advocate of Spiral Dynamics made this assertion.
"Spiral dynamics theory refers to social systems comprised of emergent, unfolding stages of cognitive awareness, levels of consciousness.
How these interact and harmonize into a coherent social organism is the focus of complex adaptive systems."
The quote is acceptable grammatically, but does it make sense semantically?
The biological analogy (social organism) may be poetic, but its meaning and testability is unclear.
The tern “complex adaptive system” sounds scientific, but see above.
A shaman can be right; may be effective on the basis of their personal skills and experience.
A shaman may prescribe medicines that science later demonstrates to be effective.
The gripe here is not shamans per se, it is presenting shamanism as though it is science.
And the misuse of terms - starting with the term “system”.
In much systems thinking discussion, it is unclear what the system is, or what the term “systems thinking” adds to “human situation thinking”.
Often, the entity being discussed is better called a social network.
Of course, seeing a business as a social network is important; and is a primary responsibility of business managers.
Management consultants continually generate approaches to identifying problems in social networks and solving them.
The question here is whether classifying all these approaches as varieties of "systems thinking” has a useful meaning.
If every problem or situation is a system, if every entity we name or point to is a system, then the term “system” has no value.
To call a social network a system is meaningless unless you have a testable system description in mind.
This table expresses the schism between two kinds of “system thinking”.
General system theory
Social network thinking
Second order cybernetics
General to all domains of knowledge
Specific to situations in which humans interact
About roles, rules and regular behaviors
About individual actors (purposeful people)
About describing testable systems
About solving any social or business problem in any consensual way
Promoting a “participative democracy”
How to extend system theory to embrace “self-organisation”?
Read system stability and change to find out.
My wholly unrealistic conclusion is this.
Social systems thinkers should stop using the word “system” where there is a social network, but no described and testable system.
Since that social network may realise countless describable and testable systems.
They should resist using terms drawn from other sciences, but with different or debatable meanings.
General system theory after Bertalanffy, Ashby and others is supposed to be scientific (ref. 1).
Boulding was probably the first to position social systems thinking as a branch of general system theory (ref. 2).
Read Systems thinkers and their ideas for a history containing a little more on Bertalanffy and Boulding, and further links.
Ref. 1: L. Von Bertalanffy “General system theory: a new approach to the unity of science” volume 23 of “Human Biology”, 1951.
Ref. 2: K. Boulding “General System Theory – The Skeleton of Science” volume 2 of “Management Science” 1956.
Ref. 3: Evgeny Morozov, in a New Yorker article http://www.newyorker.com/magazine/2014/10/13/planning-machine 2018
Ref. 4: Kenneth C. Bausch “The Emerging Consensus in System Theory” (2001) (see also discussion here)
Read Systems thinkers and their ideas for a history, which contains links and references to many systems thinkers.
Read Beer’s ideas for more on Project Cyberysn, the VSM, and a list of relevant references.
This essay is pro science, pro testable theories and pro the traditions of Western thought.
It is agin’ making assertions and presenting mental models as though no evidence is needed to support them.
We surely want people recognise when fake news is not news, and distinguish pseudo science from science.
Soft sciences may be contrasted with hard sciences in various ways.
Some assert their social or soft systems
thinking approach is more “holistic” and/or “systemic”.
To draw this contrast successfully would be to distance their approach from general system theory
Since by definition, the latter is about principles that apply to systems in *all* sciences.
However, both contrasts are questionable.
Holistic means considering how the parts of a whole are related.
Or taking a view that addresses how parts cooperate to the benefit of the whole.
Social and soft systems thinking is no more holistic than other kinds of system thinking.
Systemic is often confused with holistic, but can mean something a little different.
It means relating to the whole rather than a part; reaching throughout the whole, pervasive throughout a system.
E.g. A systemic drug or disease that reaches and has an effect on the whole of a body.
It isn’t clear why social and soft systems thinking should be considered more systemic than other kinds.
If Feynman were alive today, he might be alarmed at trends in the arts and social sciences.
There is relativism - the doctrine that knowledge, truth, and morality exist in relation to culture, society, or historical context, and are not absolute.
There is a post modern dismissal of the scientific method, or the effort needed to apply it, collect evidence and measure things.
And “identity politics” which tends to pit social networks against each other.
It would be unfair to treat you as though you are no more or less than a representative of a social network.
You belong to many social networks at once, some with conflicting norms and aims.
You join and leave social networks; your allegiance to any particular social network may be partial or disputable.
You may be volatile and contrary; you may change your mind about what you want and will do.
It goes against human nature to presume all humans have the same competence and the same authority.
Competence and authority hierarchies in a social network are natural, to be expected.
Different people have different competencies and different authorities in different social networks at different times.
It makes little sense to treat a social network as though it were an organism or individual human.
One can compose software systems into larger systems; at every level, the system can be encapsulated behind an interface of the same kind.
One can compose human roles into larger roles, but cannot compose human actors into larger actors.
Human society is based on relationships and interactions between individual humans, both within social networks and between social networks.
Agile development can be seen as reaction to design thinking.
Instead of “big design up front”, the user is given a partial solution/system as fast as possible.
Based on feedback from experience, the solution/system is iteratively and incrementally changed and extended.
The approach is based on the experience that users’ problems and requirements become clearer when they have something to work with.
Darwinian evolution explains resilience of life forms to changes in environmental conditions.
It has no goal to increase or decrease the total bio mass, though
• the biomass must have increased in the first period after life started on earth
• the biomass must have a maximum - determined by the quantity of energy and materials available
• having reached a maximum, the biomass may fluctuate below that.
The Gaia hypothesis adds a layer in top of Darwinian evolution.
The key point is that it incorporates inorganic materials into the story.
“The Gaia hypothesis is that organisms interact with their inorganic surroundings on Earth
to form a synergistic, self-regulating, complex system that helps to maintain and perpetuate the conditions for life on the planet.
It was formulated by the chemist James Lovelock and co-developed by the microbiologist Lynn Margulis in the 1970s.
Later refinements aligned the Gaia hypothesis with ideas from fields such as Earth system science, biogeochemistry and systems ecology.” (edited from the Wikipedia 26/08/17 entry)
The question here is not whether the Gaia hypothesis is appealing or inspiring.
The question is whether it is science or it is poetry, to be interpreted by each reader.
Is it needed to explain something? Can it be used to predict something? What test would disprove it?
Some thoughts and opinions for your interest
For sure, the biomass not only evolves to fit a changing environment but also influences that environment - especially the atmosphere.
These have interacted in mutually beneficial ways that has helped life forms cope with changes in light from the sun.
That doesn’t mean these interactions are intentional or driven by some purposeful entity or system.
There is a circularity here.
Many happy chances have helped life on earth to evolve: e.g. the asteroid that wiped out the dinosaurs left room for human evolution.
Many of those chances may be regarded as preconditions for life as we know it today.
But we wouldn’t be here to discuss those chances if they had not so far worked in our favour.
And noting the doom saying discussed earlier, who is to say we will be here much longer?
One reader writes of what may be called the weak Gaia hypothesis.
“[It] is an explanation of why the Earth’s temperature has remained within a very narrow band given the luminosity variations of sun over geological time.
The hypothesis is that there are life dependent feedback loops in ecosphere that are responsible for this restricted variation of temperature.
Darwinian evolution is one way in which this could be achieved.
Indeed, the first formal explanatory model put forward by Lovelock and Watson, Daisy World, was a model based on Darwinian selection.
The point being that Darwinian selection is a sufficient mechanism to realise the required temperature regulation.”
Another reader writes of what may be called the strong Gaia hypothesis.
“[It] posits a powerful argument that what we describe as biomass is a coherent organism equipped with a sort of intelligence we have not yet mapped and understood.
… the purpose of Gaia is to enable the formation of life and ultimately consciousness.”
If you (reader) are determined to find something of value to you in this mystical guff, then you may not find this essay illuminating!
“The Gaia hypothesis continues to attract criticism, and be skeptically received by the scientific community.
Several recent books have criticised the hypothesis:
· “it lacks unambiguous observational support and has significant theoretical difficulties"
· “Suspended uncomfortably between tainted metaphor, fact, and false science, I prefer to leave Gaia firmly in the background"
· “it is supported neither by evolutionary theory nor by the empirical evidence of the geological record". (edited from the Wikipedia 26/08/17 entry)
The opposing Medea hypothesis was proposed in 2009 - that life has highly detrimental impacts on planetary conditions.
“Cybernetics deals with all forms of behaviour in so far as they are regular, or determinate, or reproducible.” Ashby 1956
Classical cybernetics sees the world in terms of systems connected by information feedback loops.
A simple homeostatic system is composed of two components - a control (or regulatory) system and a target system (or real machine)
In many spheres and applications, these ideas have succeeded brilliantly.
Stafford Beer was a theorist, consultant and professor at the Manchester Business School.
He saw corporations as homeostatic systems - full of feedback loops between the company and its suppliers, between workers and management.
And if we can make homeostatic corporations, why not homeostatic governments?
Beer urged politicians and economists to employ cybernetics.
On taking office in 1970, Allende nationalized Chile’s key industries.
He promised worker participation in the planning process; and hired Beer to help - using cybernetics.
To a degree, the system was to monitor and direct the actions of actors in Chile’s nationalised businesses.
Beyond that, Beer argued that “information is a national resource” and anticipated collecting what we might now call big data.
A vision was that planners would sit in the central Operations Room, read summary data and issue directives.
Also, data would be input into economic simulation software, used by the government to forecast the possible outcomes of different economic decisions.
A misuse of cybernetics terms and concepts?
Successes claimed for the project seem to lie in the provision of central IT resources for businesses.
Factory "control rooms" sent data to the centre, which transformed the data into management information
The aim was to help each factory set production goals, optimise resource use and make investment decisions.
In that regard, it was an ordinary IT project rather than an application of cybernetics.
Did the application of cybernetics create a participatory democracy?
In February 1973, Project Cybersyn delivered the first operational version of the system.
It is unclear to me how completely the homeostatic control system design was implemented.
If it was, then it didn’t just establish a management hierarchy, it prescribed its bureaucratic operation.
“Frustrated with the growing bureaucratization of Project Cybersyn, Beer considered resigning.
“If we wanted a new system of government, then it seems that we are not going to get it,” he wrote to his Chilean colleagues that spring .
“The team is falling apart, and descending to personal recrimination.”
Confined to the language of cybernetics, Beer didn’t know what to do. (Ref. 3).
The project met its end in September 1973, when Allende was overthrown and Chilean politics swung away from central planning.
In a New Yorker article (ref. 3), Evgeny Morozov concluded the project was utopian and scientistic, a term coined by the economist Friedrich von Hayek.
Scientism meaning “a mechanical and uncritical application of habits of thought to fields different from those in which they have been formed.”
Read Beer’s ideas for a longer account of the project, and a discussion of whether the reality met the vision..
The Viable System Model
After Project Cybersyn, Beer retreated from the world for a while.
He polished his ideas about applying cybernetics to a business, and gathering information feedback from workers.
In “Diagnosing the system for organisations” (1985) Beer refreshed and detailed his “Viable System Model”.
Beer said the VSM was inspired by the structure of the human central nervous system.
But it doesn’t resemble the known structure or workings of the human brain or nervous system.
It cannot be the VSM, since many viable systems have nothing like a central nervous system (e.g. the solar system, a tree, a bee hive, an oyster).
And there is scant evidence of any business operating in a way you could say closely matches the VSM.
A theory that cannot be disproved?
Beer wrote: “There is no 'correct' interpretation of the VSM. We have spoken instead of more or less useful interpretations.”
The VSM must be modified before it is applied, so any success or failure might be attributed to the aptitude of those making the modification.
No doubt the VSM is a useful tool when interpreted by a skilled consultant; but is it science or poetry?
And in what useful sense can we refer to any business or other social network as a single “system”.
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