Monday, 7 January, 2013 1 Comment
Tuesday, 13 November, 2012 Leave a comment
There really isn’t much point in me adding to this blog from Ontonix. Maybe one of these days the financial sector will overcome its “prediction addiction”. Maybe!!?
When facing large doses of uncertainty risk model-based methods of managing uncertainty don’t work. One thing is to manage an assembly line producing, for example, computer chips, another is to run a company in a globalized, turbulent, chaotic, non-stationary and shock-punctuated economy. Some things can be accomplished via mathematical modelling some cannot. Sure, you can model anything. Nobody can stop you from dreaming up an equation based on which you invest your own savings or those of other individuals. That is not the point. The point is that some models are unable to produce results that would be good enough to justify the effort of building them. Why is that? Those who have a few decades of experience building math models know that:
Thursday, 19 January, 2012 Leave a comment
- cannot make my mind up how happy I am with it,so
- I have accepted it is best to view it as a collaborative work in progress
- wasn’t entirely sure what I was going to do with it or how best to share it
- am frustrated that people in the “risk business” are unwilling to engage in informed discussion about:
- business – networks – markets as Complex Systems
- identified RM failings in
- conventional Risk/Project Management – Corporate/banking/insurance
- subjective Risk assessment
- Risk rating
- am fed up with people, who should know better:
- being unable to differentiate between risk and uncertainty, or
- confusing the two
- failing to recognise the serious implications of treating uncertainty with risk management tools/techniques and
- making, dangerously naive, assumptions e.g. Read more of this post
Friday, 18 November, 2011 Leave a comment
Since writing this article I have, finally, succeeded in obtaining a copy of the GSI 2010 report. It can be found here. It is certainly worth a read as, like so many reports on the subject (a range of Consultancy reports can be found here), it throws more light upon a subject that can only bring benefit from improved understanding.
HOWEVER, worryingly, the common denominator is not the definition or approach but the lack of an objective, quantitative, solution. Unsurprisingly, this is NOT something lacking in the Ontonix proposition.
That complexity is a source of risk has been established beyond any doubt. As is the fact that, conventional risk management does not possess the tools to distinguish cause from effect in complex business systems. So, identifying sources and mapping non-linear interactions – that, otherwise remain “hidden” within the data – offers a unique insight to the “observer”, enabling the business owner to:
- gain “crisis anticipation” iro endogenous events
- reduce risk exposure at source
- reduce uncertainty
- improve operational effectiveness
- improve profitability
- build-in redundancy
- maintain resilience
…and create a more sustainable enterprise – economy – world.
“In a complex system, learning how all the pieces—constant and variable—interact gives a depth of understanding that averts catastrophe. That is what we mean by humancentred design—understanding the interfaces among technology, people, communities, governments, and nature. This is what makes complexity manageable”