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Viable System Thinking: Don't ask what a thing is, ask what it does.

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The Concept of "System"

It is not too uncommon to think of a system as individual parts that are involved in dynamic interactions.  VSM thinking requires that you step away from thinking about the constitute parts and how they react with one another, toward a focus on the process and the purpose of that process.  The system boundaries are thus drawn around the process and not the parts of the organisation.  

Surely a lion in a zoo is the same as a lion in its natural habitat?  Not necessarily.  Yes, the object (in this case a lion) is technically identical, but system is not.  The system of having a lion in a zoo has a purpose to attract visitors, whereas the system of having a lion in its natural habitat has the purpose of being a predator at the top of a food-chain.  Thinking about systems in this way, highlights how the individual parts of the system is not all that relevant.  The process, and the purpose of the process, is more pertinent when trying to build a viable system model.

The Concept of "Variety"

In VSM thinking the concept of variety also plays an important role and needs to be viewed in a specific way.  Take a classroom environment.  The class will consist students that posses a variety of different backgrounds, home situations, prior knowledge, motivations etc.  The role of the teacher is to somehow teach the lesson in a manner that reduces the variety of approaches to one that can be broadly viable to the variety of different learning requirements of the students present.  This type of thinking about variety has important implications for the VSM.  Firstly, the complexity of a system is now viewed on the number of different possible variations.  Secondly, the Law of Requisite Variety (Ashby), states that the variety of different options in a system must be equal or less than the variety of options available to the regulating system to which it belongs to.  If the higher order system does not contain sufficient options to adapt to all the possible variations it needs to regulate, then the entire system is not suitable to meet all extremes.  Therefore there are only two strategic options.  Either the variety of the regulating system is increased, or the variety of the sub-system is decreased.  So going back the classroom example, - either the school increases the number of available classes so that the same lesson can be taught in different ways, or the school streamlines the students to reduced variety of learning needs.

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Stafford Beer - Father of the Viable System Model

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Edited by James Sokolowski, Saturday, 8 Jun 2019, 18:59

"Any viable system contains and is contained in a viable system."

Stafford Beer devised the Viable System Model, based on his theory that a system is only viable by virtue of its sub-system themselves being viable. 

Overview of the Model

The model consists of 5 sub-systems and an environment.  

  1. Operations - the set of activities the organisation which provides value to the environment.
  2. Coordination - the set of protocols that coordinate operations so that different operations do not cause problems for each other.
  3. Delivery - the management activities associated with allocating resources for the operations.
  4. Development - the management activities associated with understanding the environment and future trends.
  5. Policy - the balancing activities to ensure the organisation works as a system, especially balancing the decision-making between the two Delivery and Development systems.

The two most critical tensions in the VSM are:

  • the tensions between the autonomy of the parts versus the cohesion of the whole.
  • the tensions between the current and future needs.

Two fundamental concepts in VSM are:

  • Wholeness - Attributes the systems has as a whole which the sub-systems do not have as components.
  • Emergence - Attributes that emerge as necessary to manage immediate risks/opportunities in the environment.

Too much autonomy and no cohesion and the system's 'wholeness' is lost.  Too much cohesion and no autonomy and emergent attributes fail to capitalise on the environmental risks and opportunities that immediately occur.

Using the VSM as a diagnostic tool, involves assembling key features into the perfect ideal situation.  This 'ideal' is then compared to the perceived reality of the current VSM structure.  The differences that are noticed guide action to move the perceived situation towards the ideal.

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Systems Dynamic Modelling - And Top Tips for Naming

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Edited by James Sokolowski, Saturday, 18 May 2019, 02:14

Picking and Naming Variables

The choice of words is vital.  Each variable must be a noun. Avoid the use of verbs or directional adjectives.  For any variable, always have in mind a specific unit of measure so that the variable can be quantified.  

Basic Tips

identify loop types using R or B to signify reinforcing or balancing.  Always circle the R or B with a small curve travelling in the same direction as the feedback loop itself.

Useful Images

Signing Rules


Puzzling Feedback

Reinforcing Loops

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How To Spot Systems Dynamic Effects

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Edited by James Sokolowski, Thursday, 16 May 2019, 14:05

One why to identify the effects of a system is to recognise how situations are described.  A rich array of metaphors exist in common English.

  • "it only made things worse"
  • "no matter how hard I try"
  • "no good deed goes unpunished"
  • "it came back to bite me"
  • "the fix only made things worse"
  • "a stitch in time saves nine"
  • "it's quicker in the long-run"
  • "plan early, plan twice"

We've all heard these expressions before, however their real value is not in providing advice for an immediate choice of action, but rather they offer clues to the underlying problem that exists within the system.  For example the above list might be better interpreted as....

  • "we've just uncovered an unintended consequence in our system"
  • "even when working at full performance, this problem cannot be addressed by one person working hard."
  • "every time we do the job properly, it has never cause a problem, maybe this is the standard we need to achieve every time?"
  • "we cannot cut-corners in this part of the process, as this has a great impact on the system."
  • "we've just uncovered an unintended consequence in our system"
  • "early intervention at this stage of the process improves our system"
  • "the entire system requires that we invest more time to this stage of the process, otherwise there's a knock-on effect afterwards"
  • "we are not getting accurate information early enough from the rest of the system, to justify planning this early"
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Expert Lessons from Expert System Thinkers

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Edited by James Sokolowski, Thursday, 16 May 2019, 13:43

Jay Forrester

Portray Photo of Jay Forrester

The primary advantage of computer models over mental models lies in the way a computer model can reliably determine the future dynamic consequences of how the assumptions within the model interact with one another.  A secondary advantage of computer models over mental models is that interrelated assumptions are made explicit.  Unclear and hidden assumptions are exposed by the mathematically programs and thus causes hidden assumptions to be debated and examined.

"Because all models are wrong, we reject the notion that models can be validated in the dictionary definition sense of 'establishing truthfulness', instead focusing on creating models that are useful... we argue that focussing on the process of modelling rather than on the results of any particular model speeds learning and leads to better models, better policies, and a greater chance of implementation and system improvement."

Enter any troubled company and speak with its employees and one will generally find people perceive reasonably correctly their immediate environments.  They can tell you what problems they face, and can produce rational solutions to their problems.  Usually the problems are blamed on outside forces, but a dynamic analysis often shows how the internal policies are causing the troubles.  In fact a downward spiral can develop in which the presumed solutions make the difficulties even worse.

Donella Meadows

Portrait Photo of Donella Meadows

  1.  Get The Beat.  Before you disturb the system in any way, watch how it behaves.  Ask people who have been around the system a long time.  If possible graph actual data from the system.  
  2.  Listen to The Wisdom of The System. Aid and encourage the forces and structures that help the system run itself.  Remember the current system has often evolved naturally, so seek the value in the current system and re-enforce it's most successful practices.
  3.  Expose Your Mental Models to The Open Air.  Everything you know, hypothesis or assume can explain is nothing more than a mental model.  Get your model out there and invite others to shoot it down.  Consider all other models plausible until you find evidence to prove the contrary.
  4.  Stay Humble. Stay a Learner.  It is just as important to trust your intuition as it is to trust your rationality.  Lean on both approaches equally.
  5.  Locate Responsibility in The System.  Look for ways where the system creates its own behaviours.  Sometimes outside events can be controlled, but sometimes they can't.  Sometimes blames or trying to control outside influences only blinds one to the easier task of increasing responsibility within the system.
  6.  Make Feedback Policies for Feedback Systems. A dynamic self-adjusting system cannot be governed by a static, unbending policy.  Design policies that change depending on the state of the system.
  7.  Honour and Protect Information. A decision-maker can't respond to information he or she doesn't have, or react correctly to inaccurate information.
  8.  Pay Attention to What is Important, Not Just What is Quantifiable. Our culture is often obsessed with numbers, and consequently what gets measured becomes more important than what we can't measure.  Decide what is more important quantity or quality and ensure the most important is discussed and spoken about.
  9.  Go for The Good of The Whole.  Don't maximise parts of the systems while ignoring the whole.  Aim to enhance the entire system.
  10.  Expand Time Horizons.  The official time horizon extends beyond the payback period of the current investment, or the next election, or even the next generation.  When you walk you must pay attention to obstacles at your feet, just as much as you pay attention to the obstacles in the distance.
  11.  Expand The Boundary of Caring.  No systems is separated from the world itself.  Real systems are interconnected and so caring beyond the immediate boundaries are necessary.
  12.  Expand Thought Horizons.  Seeing systems as a whole requires perspectives from many different disciplines.  All involved must be in learning model to solve the problem together.

Peter Senge

Portrait of Peter Senge

A learning organisation is one where "people continually expand their capacity to create the results they truly desire." The five disciplines of a learning organisation are Systems Thinking, Personal Mastery, Mental Models, Shared Vision and Team Learning.

  1. Today's problems come from yesterday's 'solutions'
  2. The harder you push, the harder the system pushes back
  3. Behaviour grows better before it grows worse
  4. The easy way out usually leads back in Temptation for individuals to adopt the easiest solution, tend to creep back.
  5. The cure can be worse than the disease Passing the burden onto the inventor to fix, when ultimately the underlining problem remains unfixed.
  6. Faster is slower  Virtually all systems are have intrinsically optimal rates of growth, which are far less than the fastest growth possible.  Excessive growth can result in the wide system seeking to compensate by slowing it down.
  7. Cause and effect are not closely related in time and space
  8. Small changes can produce big results - but the areas of highest leverage - are often the least obvious
  9. You can have your cake and eat it too - but not at once Many current dilemmas are by-products of static thinking.  They only exist in the current situation.  Often both goals can be achieved if you are willing to wait for one while focused on the other.
  10. Dividing an elephant in half does not produce two small elephants A system boundary must include the most important issues at hand.
  11. There is no blame We tend to blame others 'someone else'.  The cure lies in your relationship with your 'enemy'.
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Two new books to help me understand Systems Dynamic Modelling

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Edited by James Sokolowski, Wednesday, 15 May 2019, 18:21

A picture of environmental scientist and activist Donella Meadows

Today I discovered work by the environmental scientist and activist Donella Meadows. She contributed to the construction of a System Dynamic model called World3, which attempted to computer simulate human population growth.  I found a link to an version of the World3 model online (although I can't verify its authenticity) https://insightmaker.com/insight/1954/The-World3-Model-A-Detailed-World-Forecaster

As far as I can tell, Donella Meadows was convinced the population of the world will outgrow the world's capacity to support life within the next 100 years.  She wrote a book called Limits to Growth which sold over 10 million copies.  I've just purchased this book, because I'm fascinated with what she has to say. She devoted the rest of her life to living in a completely sustainable "closed-loop".

However, I just can't subscribe to apocalyptic views and dooms-day theories. Humankind has always lived with fear and dread of their own survival.  Jay Forrester (1997) is quoted in saying  "that Systems Dynamics demonstrates how most of our own decision-making policies are the cause of the problems that we usually blame on others."  Surely this is at the very natural of what it is meant to be "human."  When man designed a bicycle, he also created the problem of it not being fast.  When man put a engine in the bicycle, he also created the problem of it being too fast that people died.  The human solution was not to stop riding motorbikes and settle for slow bicycles! No, the solution was design faster bikes and better crash helmets.

Humans create solutions, which create new problems, which result in solutions to new problems.  Surely this is how evolution is meant to work.  I've just purchased a second book call Why e=mc2, and Why Does It Matter?  I'm convinced that Einstein's theory of relativity is a missing link that debunks sustainability theories.  Nature exists in cycles of creation, then destruction, but there still exists a "constant" in the equation.  Surely that "constant" is alternatively called growth, evolution or progress?

World3 SD model

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