The importance of having the same vocabulary – part 3/4 – PROBLEM, CAUSE and EFFECT and friends

This is part three of this series about the importance of having the same vocabulary and today we will elaborate on first the word PROBLEM and then the words CAUSE and EFFECT.

Why is problem an important word to bring up?
Art Smalley, a Toyota Production System expert, author and former Toyota employee, states in his recent book “Four types of problems” (2018) [1], that problems can be reactive (caused) or proactive (created), where his focus is the manufactured products. Solving problems for products is very different from organizational problem-solving in focus for this blog book, making it impossible to without consideration use the problem-solving methods from finding and solving problems within products.

We have big difference regarding the repetitive manufacturing (always clear) on one side (WHAT equal to HOW) and non-repetitive product development (always complex) on the other side (WHAT not equal to HOW), but both are organizations. This means that there will be organizational problems, even though they operate in different contexts. So, we leave context out of the picture since we want to be able to handle any organizational problem. This means that it is Important to add that System Collaboration finds these kinds of problems mentioned in Smalley’s book for any organization. And not only finds the problems, but also solves them, and where all methods and thinking are based on current existing science. Since the Systemic Problem Picture Analysis – SPPA, is an iterative and proactive method for finding the organizational problems before they become incidents, as well as that a malfunctioning way of working has built-in problems, we cannot use the terms reactive, caused or proactive used by Smalley, since it would only be confusing. SPPA together with Systemic Organizational Systems Design also makes a better way of working, which means that created is neither proper to use, which is why normal is the term that is used for organizational problems that are not built-in. “Built-in problems” is also a term that focus on making our system better, moving away from the thinking that people can be changed to fit the system. The built-in problems are in turn divided into symptoms and consequences, where consequences are very close to incidents, which makes them close to Smalley’s gap from standard and trouble shooting.

Smaller changes for improvement will normally be covered as a symptom in SPPA, since our people also will bring up when they think we can do something better as a symptom, and here is where we can do continuous improvements, Kaizen. But of course, we always need to be open-minded for future disruptive innovations for organizations, and if, and only if, they also are following our organizational principles, they can make our organization better.

If we use Dave Snowden’s well-renowned Cynefin™ Framework, the solution to an activity, the WHAT (for example any activity when we are designing or manufacturing a product), can quite easily be mapped into one of the domains in the Cynefin™ Framework, when we use the Cynefin™ Framework which can be used in many different ways, as a categorisation model; do we have the knowledge to do it (obvious/simple), can we do some prototypes and get guidance from our experts (complicated), or do we not have the knowledge (complex).

Problems on the other hand have cause(s), meaning they are only symptoms of something deeper rooted. And symptoms in organizations, that are organizational and built-in, cannot be solved, only dissolved. This means that we cannot put built-in (or normal) problems directly in any of the four general domains. But, the Cynefin™ Framework has another domain for that, the domain of disorder. And one sub-domain of disorder is inauthentic disorder, where we will be as long as we do not know in which domain our problem is in. And for symptoms we need to ask multiple why? on them in order to finally reach their root cause(s), which are our negated principles. Remember also that principles are universal, and a negation or why question will not change that, so the symptoms and root causes will be in inauthentic disorder too. Finding the root causes can be easily done in a workshop or similar by collecting the narratives of the organisation, the good and the bad, where the “bad” narratives will be our problems. And preferably these narratives are written down individually and an individual yellow notes session for 10-15 minutes will do the job perfectly. Then it is just to start to ask why on the problems (symptoms) to get down to the root cause(s).

Many of the built-in and normal problems are normally lumped together, and referred to as wicked problems, or intractable problems, as Dave Snowden prefers to call them. And since problems are insolvable symptoms, this means that we can; neither solve them by doing safe-to-fail experiments with multiple, oblique or contrary experiments, nor use set-based design, nor focus on extending the problem definitions phase (to avoid premature convergence), nor situational mapping to nudge people in the right directions, nor change the constraint structure, etc., instead we need to ask multiple why to get to the root causes. If we not do, we will increase our mess due to even more unintended consequences when we are trying the impossible; to solve symptoms.

If we think about the evolution for a moment, it continuously makes safe-to-fail experiments in parallel, in order to design for resilience, adaptability and evolvability. But there is no one controlling these experiments, the eco system is simply a distributed system. BUT the design that fails in nature, will fail. Nature is not going to repair it. This fail is a symptom that could not be known in advance due to the complexity of nature on earth. But still, symptoms are insolvable, so we need to find the root causes and fix them. Here comes the cleverness of the evolution, it is safe-to-fail. This means, that nature has already solved all possible root causes in advance by using parallel paths for the evolution of every specie and its co-existence with all other species and the environment changes on earth. There is really no other way of solving non-resilience or inadaptability within an existing specie, when the environment changes too fast. It is too late. There is also the reason for bigger living creatures to extinct, the number of them are fewer, the number of parallel paths is not only too few, also the feed-back loop is simply also too long for new species to evolve. The extinction of the dinosaurs is one example.
But, as we can understand, the evolution of species is closer to the evolution of software and hardware products, and not for problems within our organisations, since we there cannot only find the organisational root causes, we can also fix them.

And since symptoms are insolvable, they are not only uncategorisable, it also means that there are no available methods to use; in the past, now or in the future. We always need to find the root cause(s) before we can choose the method to dissolve the problems (symptoms). And all symptoms together definitely give a mess, a system of problems, as Dr. Russell Ackoff would have put it. And since we still trying to solve symptoms, the number of methods trying to do this is increasing yearly. And many of the methods are using their own vocabulary, when going into depth regarding the new symptoms that are generated, requires an even increased vocabulary for every new method (discipline). This means that it is not only a mess of problems, we also have a mess of new words, making it even harder to get a mutual understanding that we actually are on the wrong track. We can therefore never find a solution with our current thought pattern, since we are never going for the root causes to our organizational problems.

Trying to categorise too early, lead us to a catch with both Smalley’s and Snowden’s thinking, since they go directly to categorisation of the problems. And a problem as we have seen above, can never be categorised, since it is only an insolvable symptom that not even rocket science can solve. Cynefin™ Framework’s domain Confused, is of course a good start because we cannot categorise a problem, but without asking why? on it (as we need as stated above), we can also never find its root cause(s). When we instead, by asking multiple why?, finally find the root cause(s) to our problem, we will always for built-in problems be in the Complex domain for a new systems design of our organization in order to dissolve the problems one time for all.

Why are cause and effect important words to bring up?
Cause and effect are important to bring up, because when we are in the ordered domains, we have linear cause and effect chains in the Obvious domain in the Cynefin™ Framework, making our prediction very high. In the Complicated domain we have some different choices that need to be done, but where we with help from our experts, still with high prediction and some exploiting prototypes can predict when we will be ready, to what cost, resource need and quality.

But, when we are talking about complexity (sometimes called science of uncertainty) which we find in the Complex domain, the words CAUSE and EFFECT are often avoided, since they are associated with linearity, which we only have in the two ordered domains. Instead, other words are used as; unintended consequence, intervention, dispositional state, phenomena, etc. when we talk about nonlinearity*.

But the question is if cause and effect cannot be used in the Complex domain, or as a matter of fact should be used in order to have a better knowledge transfer between the complexity theory discipline and other disciplines. A better knowledge transfer and understanding between disciplines on a high-level means that we can accelerate transdisciplinary work, many times needed for extraordinary innovations and exaptations.

When we are talking about problem-solving, cause and effect are also synonyms to problem and symptom, where we need to find the root cause to a problem in order to be able to completely dissolve all the negative effects. See this blog post series for a thorough elaboration on problem-solving in order to increase our understanding about problems and to increase our ability to solve problems, not only for products, but also organisational problems and their tight connection.

Here are definitions of the words mentioned above from Cambridge Dictionary (C), Oxford Living dictionaries (Lexico) (O), Wikipedia (W) and Dave Snowden at Cognitive Edge (S):

cause (C) – the reason why something, especially something bad, happens (not necessarily a live system)

root cause (O) – the basic cause of something (not necessarily a live system)

effect (O) – a change which is a result or consequence of an action or other cause (not necessarily a live system)

symptom (C) – any single problem that is caused by and shows a more serious and general problem (not necessarily a live system)

intervention (C) – action taken to intentionally become involved in a difficult situation in order to improve it or prevent it from getting worse (live system)

consequence (C) – a result of an action or situation, esp. (in the plural) a bad result (live system)

unintended consequences (W) – In the social sciences, unintended consequences (sometimes unanticipated consequences or unforeseen consequences) are outcomes that are not the ones foreseen and intended by a purposeful action (live system)

dispositional state (S) [2]- the current view of the system, a set of possibilities and plausibilities in which a future state cannot be predicted or repeated, due to its inherent uncertainty (a situational assessment made, in a crisis or not, will get us a dispositional state) (live system)

As we can see above, on this high level there seems to be very little difference or no difference at all between the different words that are used no matter if we have linearity or nonlinearity; they still mean that actions are taken/happen to get/have a result, which is highly predictable when we have linearity, but only to get feedback from experiments, preferably parallel and contradictive, in order to gain knowledge when we are in the Complex domain. But, if we instead look into if the system is live or not, we can see some differences, that intervention, consequence, unintended consequences and situational assessment are only for live systems. This is a very important difference, since when we talk about system definitions for products or organisation definitions for organisations, we cannot use these words, since a system/organisation definition is not a live system, it is frankly only the principles building up the system.

And we can also see that EFFECT also can be used for live systems, when referring to some important phenomena [3] in complexity:

The Cobra effect (unintended consequences generated) [3]
In British India a reward was offered for dead cobras in an attempt to reduce the danger to humans.  It worked well for a period but then people started to breed cobras to kill then to collect the reward.

The Butterfly effect (small input result in a large output) [3]
Small changes in the environment combine with other small changes which result in a hurricane. The point being that very small things can result in major outcomes, but it is not predictable and the same small changes might not achieve the change in a different context.

The Butterfly Effect means that we cannot make a method or framework, just because 2-3 cases from other companies were successful, since most probably it was a unique combination that will not happen again, not to say all companies that made exactly the same but failed [4].

The Hawthorne effect (the observer/novelty changes output) [3]
Which argues that humans respond well to novelty, but you should not confuse the novel thing with novelty in respect of cause and effect.

So, what is important to emphasize is that there are of course cause and effects in live systems in the Complex domain too, but here there are no linear cause and effect chains, which is very important to understand. Butterfly effect is a good example, where a very small and known cause can generate an enormous effect, but where the effect always is unknown. This means that since system/organisation definitions are not live systems as discussed above, it also means that they are never in the Complex domain, meaning that system definitions and problems regarding it are not under the laws of complexity theory.

This leads to that we have cause and effect chains for products when our system definition is correct, but we by mistake put the wrong component, we are then never entering the Complex domain. But this also leads to that we have cause and effect chains when our system definition is incorrect, since the components we put in the system cannot fulfil the definition, non-existing components. This we would never do on purpose for our products, since if we fail, we will enter the Complex domain, needing to do experimentation hoping to find a solution and rewrite the system definition. But, for our organisations today, this is common, we indirectly build organisations, that our people (agents) in the system cannot fulfil, i.e., we are violating our organisation definition. The only solution is to rebuild the organisation to fulfil the organisational definition (dissolve the organizational problems), since our people’s cognitive abilities cannot be changed. Rebuild means a new systems design, which means that we always will be in the Complex domain. This means that we are avoiding situational assessments which are not taking care of the organisational problems, and therefore only seeing symptoms, that we know are impossible to solve, or impossible to make continuous improvement on.

A common mistake when the word CAUSE is used, is to mix correlation with causation [5], where 10 or 15 cases are studied and from that is drawing a correlation, even though there is no causation, which is a massive problem in 95% of the management text books. Unfortunately, many Agile methodologies is built on this, since the authors are looking for aspects of cases that supports their methodology, “you see only what you expect to see” or confirmation bias, which is not research mentality [6].

As you probably already have figured out, cause and effect can have different “values” like known and unknown, where we also can add a more diffuse “value” like knowable. But that will be the blog post for tomorrow, where we will elaborate on the meaning of “unknown unknowns” and its friends, depending on if we know we have the knowledge or not, and their respective domain affiliation.

C u then.


*In practical terms, this means that a small perturbation** may cause a large effect, a proportional effect, or even no effect at all. In linear systems, effect is always directly proportional to cause. – Wikipedia

**A deviation of a system, moving object, or process from its regular or normal state or path, caused by an outside influence. – Oxford Living dictionaries

***the issue that can be raised here is; what happens if we take away all aspects that differ us humans from animals, like motivation, attitude, intention, identity etc., and only follow rules within our genetics, would it still be the same complex human adaptive system, or something else? something less complex? something less adaptive? a more “termite nest” like system? Definitely something to think about.

[1] Smalley, Art. Four types of problems: from reactive troubleshooting to creative innovation. Lean Enterprise Institute (2018). ISBN 978-1934109557.

[2] Snowden, Dave. Blog posts. Links copied 2019-06-04.

[3] Snowden, Dave. Blog post. Link copied 2019-06-04.

[4] Snowden, Dave. “From Agile to Agility”. Link copied 2019-08-04. at 43:35 minutes

[5] Snowden, Dave. “From Agile to Agility”. Link copied 2019-08-04. at 45:30 minutes

[6] Snowden, Dave. Blog post. Text and link copied 2019-07-29.
“Multiple causes or rather dispositional states with varying modulators are key to understanding complex systems no linear models of single point causality.”