An lo, block 2 of my Systems Practise course loomed, and I was not ready.
I’m a week behind here and having to do some serious catching up. However, a new block brings a new subject matter and I can’t be hanging about. I seriously hope to create more notes for this block than I did for the previous one. I need to, this block looks hard.
It’s not the subject that’s difficult – I find environmental issues quite interesting – it’s the methods of thinking that I’m going to struggle with. I’m used to thinking in small bursts, of little parts of things; reductionism does make many problems easier to imagine, but that’s not what this course is about. Systems Practice is about big thinking, trying to encapsulate the wider issues, to think holistically.
I find that very difficult.
Anyway, the block starts of with setting out some methods that will help us sort out “wicked” problems. I’ll write about these a little bit after the fold:
According to the OU course notes, one of the “central concept of systems thinking is ‘mutual causality’: that A gives rise to B and, in turn, B gives rise to A. You cannot have one without the other.”
I don’t get this at all. Is this always the case? If carbon dioxide causes global warming is it inevitable that this warming has some effect on carbon dioxide?
I guess that is possible but I can also imagine that most systems have more than two factors. Many more than two. So, feedback from factors in a system is one of the more important aspects in Systems Practice. OK, stored and remembered.
Intuition and visual modelling
Now, I’m going to struggle with this!
Intuition is a slippery little bugger to grasp. For the purposes of Systems Practice it seems to mean “using our learned knowledge without thinking too hard about how we do it”. It’s about using our subconscious to process complexity rather than trying to rationalise everything.
This doesn’t mean abandoning rational thought by any means. Part of this process is to use our intuition, initially, to generate an idea of how a complex system might work, and then to design some tests to check that what we’ve imagined is true. Imagine first, think later.
OK, I can try that.
The visual modelling thing isn’t explained at all in the introduction, but I should think it will involve me trying to draw rich pictures or other diagramming techniques to show how these complex systems might be connected.
Analysis and mathematical modelling
This is about looking back at what we’ve done and analysing what is more likely to be true, as I mentioned earlier. I can do this bit, I think. They mention that the maths won’t be too hard (I will hold them to that!)
This is that part where it starts to get really interesting. The course notes talk about “wicked problems”. These are problems that only really manifest themselves when you you start to change a system, or engage with it in some way. They tend to change as the system changes, and they’re difficult to pin down to any particular factor (or factors) in the system.
Action learning is a process that uses both positive and negative feedback within a system model to try and tackle problems in our mental models. These are models of our systems, and we can use these processes to understand them better.
There are different aspects to action learning, and this is a key part of this block, so I’m going to have to attempt to use this throughout my activities on this course.
The course notes describe action learning like this:
we identify a goal we wish to achieve, we develop plans in order to achieve the goal; we act according to those plans; we observe the outcome of our actions and evaluate these against our original goal. If the observations match our intended goal, then our mental models determining future behaviour are reinforced; if the observations don’t, then we change our mental models in a way that we hope would make better plans and actions in the future.
Seems simple enough when written on a page, we’ll see how this works out in practice! There are four aspects of action learning; these are planning, acting, observing and evaluating.
The planning part is where I would set out my objectives for solving a particular problem, and is primarily a modelling exercise. Acting requires me to do something with this model in order to change it (improve it perhaps). For this course the acting part will involve me merely communicating what I’ve learned rather than actually physically making changes to model of some kind (this bit seems a bit woolly to me…).
The observing and evaluating bits should be fairly straightforward. Did it work as expected? If not, why not? What could I do differently?
I’ve probably performed this “action learning” thing many times in the past, I’ve just not known its name. It’s good to develop these skills and I must do my uttermost to write down my activities as I do them. If I don’t – well, the barely-recorded activities from block 1 should make it obvious that my thoughts on this subject will not be fully formed.