Complexity and Knowledge Management Navigators…
Okay, bear with me and the oxymoron in the title [and I am not trying to take on the Cynefin model here :)]…this is going to be a bit dizzying, but I’ll get there!
Late last year I attended a session at KM Asia, hosted by Stuart French, looking at the ancient game of Go, as a metaphor for organisational complexity. First, I admit, he got me addicted to the game; so simple and yet so frustrating! He also got me thinking about how complex the game actually is and how this relates to organisational complexity. This is my journey…
First, the game of Go. I don’t want to labour too much on the game or the rules, but you can find more information here, if you are interested. For now I am just going to focus on the key points of interest for my discussion. To be clear at the outset, I am interested in the game as a metaphor, a lens, for organisational complexity and freely accept the claims that Go is one of the, if not the, most complex games in existence.
Next, let’s set the scene, I am interested in the 19×19 Go board – Its complexity is bound, it isn’t infinite; though it still has a very high level of granularity… it’s just an environment, waiting for probes that will develop disturbances, which will trigger cues, which will bring a response.
There are limits on the environment as, it could be argued, there is with any organisational environment. One of the first things put forward is that there are a potential 2.08168199382×10170 finishing positions within this board – sounds complex enough, but let’s not get too carried away just yet; the same could be said with an organisation, take Apple – who could have predicted that they would move from computers to taking over the mantle as mobile music innovators from Sony (remember the Walkman?) — going back to the beginning, Apple-1 (1976), how many outcome variations would you have gone through to get Apple to their current position; that’s essentially what is being put forward when the number of potential outcomes in a Go game is discussed? Now, look at the problem from a different view, there are only 361 possible options for play on a 19×19 board, and we immediately begin to limit the complexity; just as organisations can use a blend of simple models, such as Porter’s 5 Forces and the Transformational Model (transforming/transformed resources), we probe, limit and understand our environment. Not only that, but in the game of Go there are ‘Known Knowns’ that assist in ‘limiting’ the complexity further by understanding the probabilities of the environment.
“Go players begin with a choice of 55 distinct legal moves, accounting for symmetry. This number rises quickly as symmetry is broken and soon almost all of the 361 points of the board must be evaluated. Some are much more popular than others, some are almost never played, but all are possible”
I have said before, we as humans develop models to deal with complexity; the argument being that we recognise the environment and, in doing so, we develop schema to deal with ambiguity, we begin to develop order and structure – Have a look at my previous discussion on Homo Faber; man the controller. The evidence is there within Go. The game encourages us to develop a schema; cues occur that prompt a response; the quality of the response is based on our knowledge and understanding of the environment, our past experience and the our ability to recognise the patterns: For example see the analysis/theory of Go’s strategy/tactics for opening moves.
Not only that, but the rules of the game drive the environment, causing patterns to be formed; these patterns allow us to learn, to recognise the implication for a particular pattern and for us to respond. It could be said that these patterns link games together, creating affinity within the complexity; is this any different from that which we experience in organisations – we sense self-similarity in the environment, learn lessons from ourselves and others, and develop an appropriate response…this isn’t original, fractal analysis has been used for quite a while to establish the existence of self-affine or non-linear patterns in complexity…we really don’t like the thought of not being able to sense reality, do we? This is not restricted to theory, with practitioners, such as the North American based Aurosoorya organisation, using fractal theory to demonstrate, “a level of organisational dynamics…that is unavailable with the use of conventional organisational seeing and thinking” (I don’t necessarily subscribe to their approach, which seems a bit too ambiguous for my liking, but you get the idea…that statement in itself probably tells you something about me :-)).
Back to the game of Go… the complexity, or granularity, diminishes with every move and we suddenly emerge (that moment of epiphany, brought about by a single move, when you realise you have a winning, or as is more often the case for me, a losing position) to find ourselves in a position of strength or weakness based on moves that at the outset looked abstract – there are even links to loose and tight couplings, and the way in which a chosen strategy, our initial choices, restricts our options as the game progress…Seriously, try the game and you’ll understand what I mean.
So, my argument… and I know I am about to open a huge can of worms here. Complexity can be made less granular (not a BlueRay 3D picture, but certainly one where you can recognise the subject); I would argue that we attempt to do this any time we apply a schema or build models to make sense of our reality. As we probe, and disturbances are understood, we respond to control the level of abstractness; in doing so, we lower the level of granularity. We model.
Organisations exist in a complex environment, but the scope of its operations, the organisation’s purpose, will immediately limit the complexity. The challenges brought on by the environment cause disturbances that reveal elements of a model, a model that needs to be regulated in order for it to be controlled. I’ll try to explain using one of my bug-bears at the moment: The environment, the Knowledge Economy, drives the need for innovation; people and, as such, organisational knowledge and learning processes sit at the heart of the response; therefore, any model or schema that does not account for HR (Learning and Development) policy/processes in the response could fail under the environmental load.
So, the bottom line… I think we know more than we think we know when it comes to complexity. I think it is about developing loose couplings that allow us to become more dynamic; by understanding the environment it becomes less granular; I think that we know that there are constructs within our organisations that need to be ‘regulated’ in order to for us to enable a dynamic response to the cues that emerge from the environment; I believe that we build models to lower the levels of granularity or abstractness. As I have come to understand from one too many hours in an airport playing the ipad version of Go, expect the unexpected, because it always seems to put you in your place just when you think you’ve got it beat! And, dare I say it, I think, with some common sense, we are better at dealing with complexity than we first realise.
The message – we need to get our models right, which means getting a better understanding of the environment, the needs of the individual, the needs of the organisation and the processes that bind them together.