Risk Management of Power Law Systems
May 14, 2008
In the previous post, we explored the behavioural differences of simple and complex systems. We saw that complex systems display power law distributions, the key characteristics of which are increased unpredictability and an increased likelihood of extreme events when compared to simple Gaussian systems. Additionally, the existence of positive and negative feedback loops makes them more resistant to causal analaysis: the potential for repeated amplification of trivial trigger events can make it very difficult to understand what is going on (see the 1987 stock market crash as an example). We will now examine the implications of those differences for risk management, focussing in particular on IT project delivery.
Conventional business management practices are based on the implicit assumptions we have inheritted from our cultural past, that ultimately have their roots in the scientific tradition: we use specific instances or case studies to infer a generalised understanding of a domain; that understanding then allows us to predict it, and once we can predict it we can then define an effective strategy for managing it. On the other hand, in our everyday lives and throughout the natural world reactive risk management is the norm. For example, to avoid being run over by a car when crossing the road we do not need to understand how a car works but only what it looks like (i.e. fast moving metallic thing on wheels). Similarly to avoid being eaten by a lion, a deer does not need to understand big cat physiology but only what one looks like (i.e. fast moving furry thing on legs).
From this, we can see that risk management strategies can be grouped at the most basic level into one of two categories:
- Cause based:
- Standard business practice
- Analyse cause, then define strategy
- Observation based:
- Normal practice in daily life and natural world
In situations where they both work, the latter is obviously inferior as it affords no potential for proactivity and forward planning. However the former is critically dependent on the predictability of the thing being managed.
Now previously we have seen that IT project success in real terms appears to display power law behaviour. Possible explanations for this might include:
- There is a simple causal relationship with an underlying pseudo-power law phenomenon. It might just be that the size of investment in IT projects follows a roughly power law distribution and that the returns generated are directly proportionate to that investment. Most projects receive small to moderate investment whilst a few get massive investment and that is what results in the correlated power law distribution of generated business value.
- The world of IT project delivery is a complex but deterministic system, hence it displays power law behaviour.
- IT project delivery has dependecies on truly random phenomena, hence the generation of delivered business value displays power law behaviour.
Which of these is most accurate is a matter of conjecture: some people might argue for the first explanation, whilst others might stand by the second. We are going to stand back from that debate. Instead we will only assume this: that to the best of our knowledge, all of the explanations sound to some extent reasonable and one of them actually happens to be true. As discussed in the first post of this series, this then allows us to assess each strategy against possible explanation/scenario as follows:
This demonstrates that in the absence of certain knowledge, adaptive metholodies clearly represent the lowest risk approach to IT project delivery as they are effective for every explanation. More generally, we can summarise this by stating:
- Simple, independent processes that are described by normal distributions are best managed by predictive strategies
- Complex, interdependent processes that are described by power law distributions are best managed by adaptive strategies.
In the next post we will start exploring what a fully adaptive IT risk management strategy might look like, within the context of lessons we can learn from other areas including evolutionary biology.