Pseudo science in systems thinking
(Seven signs of shamanism)
Copyright 2016 Graham Berrisford. One of about 300 papers at http://avancier.website. Last updated 18/11/2017 11:17
It is easy to make pronouncements that are tosh.
And the easier technology makes it for people to publish pronouncements, the more tosh is produced.
The survival of our species depends in part on recognising when assertion is not truth, fake news is not news, correlation is not causation, and pseudo science is not science.
This paper explores the notion of pseudo science, and the tendency of social system thinkers to stray from the disciplines of science.
General system theory (GST) emerged after WW2, and is about concepts and principles applied across many sciences.
Some suggest social systems thinking derives from, or is an advanced application of, GST.
The truth is that what people now call “systems thinking” started much earlier, in 19th century sociology.
Early social systems thinkers include Marx and Engels; read Some systems thinkers for other names.
Many sociologists looked at a society as though it were a kind of biological organism
Some, interested in social stability, applied the idea of homeostasis (as in a biological organism) to a society.
Others, interested in social change, applied the notion of evolution (after Darwin) to a society.
However, drawing an analogy between sciences isn’t itself being scientific
And today, modern social systems thinkers tend depart from GST in at least one of the ways listed in this table.
This paper is about the contrast drawn in the first row of this table.
General system theory
NOT general system theory
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 systems at the base level of interest
About meta systems that define and change system roles and rules
Describing testable systems
Solving any problem in any consensual way
Promoting a “participative democracy”
Can the scientific method be applied to systems?
Yes, provided that there is a system description against which a system in operation can be tested..
GST and classical cybernetics deal with regular or reproducible behaviors – which are describable and testable.
So, actual (empirical) performances of behaviors can be tested for conformance to abstract (theoretical) descriptions of those behaviors.
For example, the actual orbits of planets can be tested for conformance to astronomers’ descriptions of those orbits.
And the actual behaviors of US governments can be tested for conformance to the description of those behaviors in the US constitution.
Abstract system description
Concrete system realization
To apply the scientific method to a system is to follow a hypothesis-test process of this kind:
1. Observe or envisage a discrete entity.
2. Hypothesise that entity is a system (so can be observed or built to realise an abstract system description).
3. Describe that system by defining roles and rules whose progress can be measured in changes to state variables.
4. If the entity exists go to step 5, else manufacture/build the entity.
5. Observe or set the entity in motion.
6. Measure that the behaviour or state variables of the entity in motion match those predictable from the system description.
A hypothesis-test process of this kind is followed every day, all over the world.
Every software engineer describes a system, and tests that the system in operation matches its description.
The same process is followed in the design and testing of human activity systems.
And we all depend on business systems (human and computer activity systems) behaving according to descriptions of them.
Richard Feynman (1918- 88) was recently ranked as one of the ten greatest physicists of all time, and left us with insights that go beyond the world of physics.
Here is what Feynman had to say about social sciences 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.”
In short, pseudo science is the practice of asserting things to be true in a way that sounds scientific, but isn’t.
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.
Karl Popper taught us that theories should be readily falsifiable.
In other words, a well-formed theory is potentially disprovable by measuring test results.
Consider for example these assertions, propositions, statements, hypotheses or theories:
· Eating carrots gives you night vision
· Gum stays in your stomach for seven years
· Crusts will give you curly hair
· Eating cheese before bed will give you nightmares
· Organic foods are better for you.
· IBM is a complex adaptive system.
· The biomass on earth is a coherent organism equipped with a sort of intelligence
· My management consulting intervention will identify and solve your problem
· Adding people to a late-running project brings it back on track.
Are these well-formed scientific 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.
A review in 2016 repeated its finding: “Scientific studies do not show that organic products are more nutritious and safer than conventional foods.”
IBM is a complex adaptive system
What makes IBM a system? How to test that it is one?
In what ways is it complex? adaptive? How to measure those attributes of an organisation?
The meanings of the three terms are not self-evident and open to interpretation.
(There is further discussion of CAS below.)
The biomass on earth is a coherent organism equipped with a sort of intelligence
The assertion appears true only in a circular sense.
It appears to define “organism” and “intelligence” in a unique way, by reference to the subject (biomass).
How could this assertion be disproved?
My management consulting approach will identify and solve your problem
Management consulting inventions often take this shape:
· Assert or adopt a theory about business organisation or management
· Persuade a customer to believe the theory
· Produce a report making recommendations for business changes.
It can be difficult to disprove an approach to changing an organisation.
After changes are made, there is often little or no will to measure or test the effect of those changes.
Not least, because managers have invested both financial and political capital in making the changes.
And even if measurements are made, the results may prove nothing since:
· Success has a thousand fathers: success might be due to something outside the theory
· The guru is always right: the guru will attribute failure an unforeseeable interference from something outside their theory.
A shaman makes assertions that sound scientific, but with no evidence or only dubious evidence.
This is dangerous, because applying a false hypothesis can make a condition worse rather than better.
Below are things to watch out for.
-1- Abuse of the one-off (it worked once, so must be right)
Suppose the latest management theory is: “Adding people to a late-running project brings it back on track.”
And many managers (independently) test that theory on real projects.
Most projects fail, but one succeeds.
No manager of a project that failed publicises that fact.
The one manager whose project succeeds writes up their project as a case study supporting the theory.
The paper is published in – say – The Harvard Business Review.
CEOs all over the world read the paper and promote the practice in their organisation.
-2- Abuse of terms from harder sciences
A pseudo scientific theory may steal words from harder sciences.
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.
Assertions of this kind can give a theory the appearance of certainty without the substance.
See also the footnote on “The Pretence of Knowledge”.
-3- Abuse of analogies
A pseudo scientific theory may be based on a misleading analogy.
Highly questionable analogies include:
· “Software engineering is like electrical engineering, or electronic engineering.”
· “Enterprise architecture is like building architecture, or city planning.”
· “Systems thinking is like general system theory”.
-4- Abuse of correlations
A pseudo scientific theory may be based on presenting a correlation as a cause-effect relationship.
To give a famous example: an increase in sales of ice cream causes an increase in deaths by drowning.
Statistics show a correlation between these variables; so does the former cause the latter? Of course not.
Another example: scientific research has so far concluded the claim that vaccines cause autism is untrue.
In both cases, the presumption is that another variable (sunshine, age) triggers both outcomes.
-5- The guru is always right
A pseudo scientific theory is not provable or falsifiable by testing of actual results.
The shaman announces a theory and recommends an action.
If the action it succeeds, you may be suspicious, because success has a thousand fathers.
If the action fails, the shaman will say you didn’t try hard enough, or unforeseeable phenomena got in the way.
For instance, a guru 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.
Even software engineering is far from immune to pseudo science.
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.
-6- The theory dies with the guru
A pseudo scientific theory may be so strongly associated with one person that it withers after they die.
On the other hand, pseudoscientific assertions can survive longer than scientific ones, because it is impossible to disprove them.
If they appealing and not disprovable, people may cling to them.
-7- Doom saying in order to offer salvation
Naturally, parents will scour the internet for a cure when their child has a life-shortening illness.
Somewhere in the world, somebody will claim they have the answer.
A pseudo scientific theory may give hope to those fearful of what the future will bring.
The shaman can use fear of a calamity to encourage belief in salvation from following his/her direction.
First, criticise the current state of the world and make pseudo scientific predictions of doom.
Then, promote a particular pseudo scientific recipe for salvation.
E.g. In the 1970s, Ackoff and Beer were among those systems thinkers who have taken a pessimistic view of the world and its future.
They each predicted the imminent collapse of governments, if not western civilisation.
(And, of course, they advanced their own ideas for how to avoid doom.)
Have their predictions have been verified or falsified by the evidence?
It turns out turns out that many statistics have moved dramatically and surprisingly in the right direction since the 1970s.
There have been global increases in health and education, and reductions in poverty.
Google anything you can find from Hans Rosling, especially “200 Countries, 200 Years, 4 Minutes - The Joy of Stats”.
One thing seems certain, we are headed for a worldwide calamity, sooner or later.
If a virus, or global warming, doesn’t wipe us out sooner, the sun will burn itself out later.
Meanwhile, some people will cling to pseudo scientific theories that offer hope rather than despair.
Causal loop diagrams (CLDs) give us a way to present theories of how resources are produced and consumed.
A CLD can model anything that can be modelled as flows between stocks of different resources (happiness, food, etc).
System Dynamics is a method for turning a static CLD into dynamic model of flows between stocks of different resources.
The model can be run forwards from an initial state to predict the long-term outcome of those interactions between stocks.
However, the model remains a theory; and it can be difficult to demonstrate that the model is right or wrong.
Turning a CLD into a System Dynamics model does create something testable in theory.
However, the model’s predictions may never be testable, provable or disprovable in practice.
1. Several options are modelled, meaning no single hypothesis or prediction is actually made.
2. The system owner pursues one modelled option, then discards the model
(because the owner has to live with the chosen option, and does not want the choice to be challenged in retrospect).
3. The predicted outcome is enough far ahead that real-world changes (e.g. unexpected global warming) invalidate the model.
4. The predicted outcome is reached, but the trajectories of reality and model on the way to that outcome are different.
5. The trajectories match, but simply continue past trends, so we are none the wiser as to whether the model captures the true cause.
6. The trajectories match so far in a linear way, so we are none the wiser as to when a catastrophic change (predicted to happen) will occur.
For an example of 5 and 6 see “The limits to Growth” example below.
One thing seems certain, we are headed for a worldwide calamity, sooner or later.
However, since we still can’t predict the weather next month, it is difficult to be confident about predictions of when that calamity 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 civilisations
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.” Bausch 2001
Meadows’ team modelled industrialisation, population, food, use of resources, pollution.
They built a model of these as stocks and flows in a “System Dynamics” model.
They modelled the historical data
Then modelled a range of 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 later some pointed out that the “business-as-usual” model matches reality pretty well so far.
They therefore proposed that the catastrophic collapse will happen when Meadow’s model predicts it will happen.
Look for example at the graphs in this article:
The trouble is:
1. It may be argued the “business-as-usual” label doesn’t reflect how the world as changed over 42 years.
2. So far, the graphs show near-to-linear continuations of past trends, which is anybody’s default prediction.
3. Many scientists would endorse the prediction of doom, regardless of the model.
4. Success has a thousand fathers: success might be due to something outside the SD model (e.g. global warming).
5. The gurus may attribute failure to an unforeseeable interference from something outside their theory.
The predicted change from linear to non-linear change may well happen.
But it remains impossible to be confident the SD model predicts when, or that its primary causes will be same ones the model presumes (think global warming).
Sociologically-inclined systems thinkers strive understand the behavior of human social groups.
The trouble is, these groups contain volatile, irrational, unpredictable and contrary human actors, who change their minds about things.
And this tends to undermine the notion that a social group is a “system” in the normal sense of the term
To Feynman’s point: rather than acknowledging this huge limitation, and without the evidence that harder sciences expect, gurus present their theories as scientific.
For example, gurus describe social entities using terms like "complex, adaptive non-linear systems”.
They use mathematical-sounding terms like “complexity theory”, “nonlinear dynamics”, “fractal geometry” and “chaos theory”.
And use other scientific-sounding terms like “autopoiesis”, “emergent properties”, “strange attractors” and “entropy”.
Where von Hayek might have called this scientism, Feynman would surely have called it pseudo scientific.
Spiral dynamics theory
A reader writes: "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."
Spiral dynamics theory classifies world views into 8 kinds.
Unsurprisingly, it then proposes that people take all 8 world views at once.
And mix them in different degrees in viewing different realities.
What does the theory say about:
· How to define or delimit what a system is?
· How to measure complexity or adaptiveness?
· How to define a reality that is viewed?
· How to measure which world view(s) are taken of one reality?
· How to measure the strength or degree of a world view?
· How to use these measures to predict any outcome?
On the spectrum from science to pseudo science, where would you place the theory?
Complex adaptive systems (CAS)
"CAS 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.
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
This MIT definition of CAS appears to exemplify the giving of a pseudo-scientific label to an everyday concept.
It seems pseudo-scientific because the label is applied by people to a concept that is:
· not a System in the common sense of the term (a stable island of orderly behaviour carved out of the universe)
· not measurable in terms of Complexity or Adaptiveness.
If we cannot measure the named properties of a thing, they are not meaningful in a scientific definition.
And calling a thing a "system” is an empty assertion if we don’t know what defines a system and separates it from is environment.
The everyday concept that people discuss using the label CAS seems to be a human organisation, institution or other social group that
· may gain and lose member actors
· may change its roles, rules and activities and
· may even change its aims.
In other words, it is a normal social entity.
Why call a social entity a system at all? Surely to relate systems thinking to more scientific system theory?
Then, why call it a CAS? Surely to distance systems thinking from more general system theory?
But you can’t have your cake and eat it.
The term CAS is used by systems thinkers in a way that seems irreconcilable with more general system theory.
Sociologists ask what are the trade offs between:
· centralisation of control (think of totalitarianism, or a top-down management hierarchy) and
· distribution of control (think of individualism, a participatory democracy or anarchy)?
There are obvious reasons why hierarchical bureaucracies are inefficient and inept.
· Parkinson's law
· The Peter principle.
· The difficulty of recruiting, motivating and retaining employees to do boring or difficult work
· The impossibility of the top-most manager knowing enough to do much better than random in decision making
· The distortions of behavior (“unintended consequences”) that arise setting targets and imposing them in a top-down manner.
But that doesn’t mean there is no advantage to, or need for, a top-down management approach.
It has benefits as well as drawbacks; and there is a balance to be drawn between hierarchy and anarchy
The “Viable System Model”
1970s: Beer proposed a business should be organised along the lines of his Viable System Model.
The evidence for the effectiveness Beer’s model is sparse.
It cannot be proved wrong, because it is always modified before it is applied, so any failure can be attributed to that modification.
Read Beer’s ideas for more.
2001: Bausch suggested systems thinkers have a mission to herald a new era of social organisation, of advancing 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.”
The hypothesis may be attractive, but seems more a presumption, an assertion, an article of faith, rather than science.
Can you imagine running the US army as a participatory democracy?
Read The consensus in system theory for more on Bausch’s ideas.
The Hawthorne effect
Some other ideas promoted in systems thinking discussion also look pseudo scientific rather than scientific.
And always, the Hawthorne Effect offers an alternative explanation for any improvement made by the application of a theory to a society.
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.
Productivity improves, not because of any particular management intervention, because managers show interest in their workers.
In the 19th century, Thomas Carlyle, a Scottish writer and philosopher, labelled economics "the dismal science".
In the 20th century (1974) Friedrich von Hayek gave an Economic Sciences Nobel prize acceptance speech.
Here’s an excerpt from his speech, which was entitled “The Pretence of Knowledge”.
“It seems to me that this failure of the economists to guide policy more successfully is closely connected with
their propensity to imitate as closely as possible the procedures of the brilliantly successful physical sciences
- an attempt which in our field may lead to outright error.
It is an approach which has come to be described as the ‘scientistic’ attitude –
which, as I defined it some thirty years ago, ‘is decidedly unscientific in the true sense of the word,
since it involves a mechanical and uncritical application of habits of thought to fields different from those in which they have been formed.’"
The resilience of life forms to changes in environmental conditions is explicable by Darwinian evolution.
Darwinian evolution has no goal to increase or decrease the total bio mass, though
• it must have increased in the first period after life started on earth
• the maximum biomass is determined by the changing availability resources of energy and materials
• having reached a maximum, it may fluctuate below that.
The Gaia hypothesis (which has weaker and stronger forms) adds a layer in top of Darwinian evolution.
Many read it as science; but it is included here as an example of pseudo science.
“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 opposing Medea hypothesis was proposed in 2009 - that life has highly detrimental (biocidal) impacts on planetary conditions.
The point here is not to favour either Gaia or Medea hypothesis; it is to question their credentials as science.
Are they provable or disprovable?
Are they needed to explain something?
Can they be used to predict something that can be tested?
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.”
If Darwinian evolution is enough explanation, shouldn’t Occam's razor be applied?
For sure, the biomass both (1) evolves to fit a changing environment and (2) influences that environment - especially the atmosphere.
It might well be that 1 and 2 have interacted in mutually beneficial ways, and helped life forms cope with changes in light from the sun.
But why conjecture that these interactions are somehow intentional, driven by some purposeful entity or system?
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 chances may be regarded as preconditions for life as we know it today.
There is a circularity here: because we wouldn’t be here to discuss these chances if they had not so far worked in our favour.
And on the other hand, who is to say we will be here much longer?
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 paper illuminating!
The Gaia hypothesis continues to attract criticism, and be skeptically received by the scientific community.
Arguments for and against it were laid out in the journal Climatic Change in 2002 and 2003.
Against it are many examples where life has had a detrimental or destabilising effect on the environment.
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".
The CLAW hypothesis initially suggested as a potential example, has subsequently been found to be less credible.” (edited from the Wikipedia 26/08/17 entry)
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