“VoR” – Velocity of Risk …. food for thought …. http://www.mccormickpcs.com/rm/vor.html

Predicting the future is still a guessing game and mathematics will always give us our comfort zone but reality is that variables are infinite therefore predicting probability will always be a crap shoot. You all know that managing risk is only achievable by those variables that we can control and even then we’re still risk factoring probability which is simply “mitigating risk probability” (making less severe).

Risk simply put is – “Yes”, “No”, “Maybe”. Yes can be “we’re OK and No can be “we’re dead” and maybe is “it’s not life threatening”. We all mitigate risk every moment of our lives – “walking across the street” every event is different with varying degrees of “risk”; the road is wet/dry, many cars or few, wearing safe shoes or not…. all the variables analyzed in seconds and probability factor concluded and decision made – to run or not run! Outcome can be good or bad, alive, dead or just a few broken bones “risk impact”.

The human factor is the highest risk contributor, for logic (common since) is too often side stepped and the 33,808 US driving fatalities in 2009 due to excessive speeding is the result of the “human factor”.

Risk is just a bunch of molecules bouncing around with an infinite probability of impact and the end result is the inevitable outcome when it happens.

“To boldly go where no man has gone before” is what makes us human and that’s why we take risk.

Take care to all.

]]>This is an excellent article discussing some of the issues around one of the most common risk assessment tools: The Scoring Matrix. ]]>

Thanks for the thought provoking comment.

You may have hit upon a problem that is not uncommon at the interface between theory and practice. Theoretical models are often based on idealised assumptions that ignore some of the messiness and complexity of real life situations . As a result it can be difficult, if not impossible, to apply theoretical models to real life problems. I suspect (but am not certain) that this is the case here.

I think there is a great deal of truth in the notion of risk as a *social construct*. I have discussed this in relation to IT project risks in this post.

Cox is referring to the situation in which both scales are linear. Log-log or log-linear plots do not change the underlying relationship, which is still multiplicative. Cox’s arguments would still apply as one could, in principle, choose to display the plot on linear-linear axes (though the display may be hard to read!). As before, I’m not sure I’ve interpreted your comment correctly, so feel free to straighten me out if I haven’t.

Regards,

Kailash.

]]>Hubbard makes extensive references to but I don’t think it is really going to be the answer to life the universe and everything.

Hubbard talks extensively of x-axis value compression but makes no reference to log scaling. Cox multiplies impact by probability but if both scales were log you would add them, but what if P() is cardinal and value is log – mulitply or add?

]]>I would love to see your analysis as it is quite possible that I have misinterpreted the term. I’ve sent you an email message.

Regards,

Kailash.

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