Dissolution of problems in organisations (Human Complex Adaptive Systems) – part 1/4 – Introduction

Today’s blog post is the first and introductory one in this series, and will go through what Dissolution of problems in human Complex Adaptive Systems (CASs) means, where organisations is the main focus. Dissolution of problems and its relation to inductive and abductive approaches, will be a part of the discussion. In the next post, the method of Dissolution of problems in organisations is presented, and in the third blog post we will go through the theory behind. The last blog post in this series will deepen in why we always need to start with trying to dissolve our problems, which is a (reversed) deductive approach, before we (even think about) using abductive or inductive approaches for organisational problem solving. The latter is especially treacherous, since its focus today is only on the effects we will get with the new bright shiny, never to get rid of the current problems within the organisation to transform, since the problems are never even mentioned.

Dissolution of problems is only one step, as well as the first step, in a “Systematic approach for a systemic learning” approach, used for solving organisational problems and by that achieve a systemic learning in the whole organisation.

There are different ways to describe a Complex Adaptive System, a CAS, and the most common is to describe it as containing agents with interrelationships. About CASs, our complexity guru Dave Snowden* has stated; “The only thing we with certainty can say about a CAS is that any intervention with the system will have unintended consequences”, where the Hawthorn effect [1] and the Cobra Effect [1] are good examples of unintended consequences, or sub-optimisation, as a result of an intervention in the latter case. This means that we cannot intervene with any agent in a live system, not model it nor measure on its parts, since then we will sub-optimise the system; or as Russell Ackoff always put it “all interventions with the agents are anti-systemic**”, egoistic is an apt word since any intervention then will generate a sub-optimisation in the system.

The implication of this is that any interventions in a complex adaptive system will cause unintended consequences. This means that inductive techniques (bottom-up logic), no matter if they are based on many cases or not, will be very problematic, since they will set up an ideal state and try to close the gap. This indirectly means that they never will look for the root causes, and if the inductive approach solves a root cause anyway, it is only luck. This means that they instead always will start chains of unintended consequences, since they are trying to indirectly, without of clue of the real problems, try to solve the problems directly (impossible), and are therefore not suitable for neither complexity nor uncertainty. Abductive techniques (some of them) on the other hand, are more suitable, since they are trying to avoid premature convergence of the solution, by solving smaller problems (kind of nudging), and with fast feedback analyse the result. An example of an abductive approach is mapping the dispositional state in a situational assessment and identifying adjacent possible states in the fitness landscape, and then make small hypothesis and experiments and draw conclusions from the observations. One tool for this is SenseMaker® with big similarities to making hypotheses and do safe-to-fail experiments in parallel, see Dave Snowden’s blog posts for more information [2], [3].

Since any intervention with (the agents within) a human system gives unintended consequences, the list of things we so far have found out that we then need to cautiously consider, when trying to solve the problems within a human system, is very long. And since any intervention means sub-optimising the system, the number of items is still counting. Here are some examples to consider: dealing only with parts of the system, modelling the system, premature convergence on a solution, directly changing people, only one (or too few independent parallel) observer, the need of weak signal detection, problems with the Pareto distribution (when referring only to the normal distribution), just ask questions in facilitated conversation, open space techniques, confusing correlation with causation, retrospective coherence and cognitive bias, post hoc rationalisation, controlling or manipulating the outcome of the process, norming and pattern entrainment, bias at situational assessment, strange attractors, dark constraints***, predetermination of only positive stories, talk about how things should be (effects), idealistic state as a goal and closing the gap, theory without validation (case-based or inductive approaches), interpretative conflict, subjectivity or (better) unobjectivity, framing, gaming, engaged facilitation, relationships of people involved in a work shop or assessment, inattentional blindness, patterns of group interaction, sub-optimisation and the impossible symptoms solving that only will generate more symptoms and consequences, etc,, where some of them are what Dave Snowden calls “the tyranny of the explicit” [4]. Many of them are also an outcome, new symptoms and consequences, originating from the impossibility of trying to solve symptoms, which is the same as sub-optimisation. Sub-optimisation always means that we are not heading towards the roots of our problems, but in the diametrically opposite direction, meaning more and more unintended consequences. This makes it possible for the list above to be infinitely long, which is why it is still counting. This means that we need to be careful also with abductive techniques, not only the inductive ones, since both approaches means interventions of some kind, directly on symptoms and consequences, which are impossible to solve.

Kind of tricky, right! So, what can we do about it?

Instead, we need take another approach and re-think about in what cases we can take advantage of hindsight, we frankly need to think differently, or as Einstein stated:

Without changing our pattern of thought, we will not be able to solve the problems we created with our current patterns of thought.

Taking advantage of hindsight for organisational problem-solving can be done at significantly more occasions than can be seen today in methods and frameworks, and also how uncertainty, complexity and ambiguity generally are presented. By taking this advantage, we can in the light of the current knowledge (problems seen), change the actions done in the past, so the actions instead solve the root causes to today’s problems, which means to dissolve the problems of today.

So, it is not about changing the future from the current situation, like intervening with the current fitness landscape, which will generate many of the items to consider in the list above, due to the unintended consequences that will be generated from the interventions.

Here the Cobra Effect is an apt example to have in mind not only regarding unintended consequences from interventions (bounty), but most of all due to the fact that the Cobra Effect means that there is effect and cause in human systems. Because, it was impossible for the Englishmen to understand the sudden increase of cobras at first. But when the cobra farms were found, the missing piece (symptom from the sub-optimal strategy of bounties for cobras) were found. This meant that the effect-and-cause chain could in hindsight be completely drawn backwards in time from the last effect to the first cause, which is the essence of a root cause analysis.

With the Cobra Effect in mind, we can also see the importance of having all symptoms needed in order to conclude the effect-and-cause chain down to the roots of the clearly visible problems (increasing pay-out of bounties and increasing population of cobras). And with all things to consider above, we really need to have a systematic approach to achieve true systemic learning in our organisation, so we can avoid the need to consider the items in the list. We need a very easy and straightforward way of looking at problem-solving and learning in our organisations. The first step (and many times enough) of “A systematic approach to systemic learning” is a true eye-opener for the detangling the problems in an organisation.

In the next blog post, we will go through a short version of the approach.

C u then.

 

*At Cognitive Edge, the father of the Cynefin™ framework

**Do not mix anti-systemic with non-systemic. Non-systemic means that there are no side effects. A good example where there are no non-systemic cures, only anti-systemic, are medicines taken orally trying to cure a symptom in the body. This is because the body is a complex system too, which means that no symptoms in the body can be cured with any medicine without effecting parts of the body with side effects. Instead, the root cause(s) to the symptoms need to be found to really cure. The same goes for our organisations and only the root cause(s) to the symptoms can be solved, since trying to solve the symptoms mean more other symptoms, that can be hard to foreseen and never directly be solved either.

***dark constraints; a term by Snowden, that is affecting the current behaviour of the complex (adaptive) system, but where we only can see the effect, but not the cause of it or modulating factors [4].

 

References:

[1] Snowden, Dave. Blog post. Link copied 2019-06-04.
Of effects & things – Cognitive Edge (cognitive-edge.com)

[2] Snowden, Dave. Blog post. Link copied 2020-12-12.
The dispositional state – Cognitive Edge (cognitive-edge.com)

[3] Snowden, Dave. Blog post. Link copied 2020-12-12.
Power laws & abductive research – Cognitive Edge (cognitive-edge.com)

[4] Snowden, Dave. Blog post. Link copied 2021-07-20.
Yes but… (and the Isaiah moment is still with us) – Cognitive Edge (cognitive-edge.com)

[5] Snowden, Dave. Blog post. Link copied 2021-07-06.
The tyranny of the explicit – Cognitive Edge (cognitive-edge.com)

 

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