View non-flash version
www.sname.org/sname/mt October 2013 There is a feeling among some risk practitioners, myself included, that theoretical risk management has strayed from our intuition of the world in which we manage risk daily. Historically, risk management has developed from the numer- ical disciplines dominated by a preoccupation with statistics (such as insurance, accountancy, and engineering). is has led to an unhealthy bias toward the numerical in the world of risk management. is should, however, come as no surprise. If we look closely at the historical roots of this newly emerging discipline, we see that risk management as a science only really took o during the 20th century. Its development during this time tended to be dominated by the worlds of mathematics and engineering. In the 1921 book Risk, Uncertainty, and Pro t, Frank Knight distinguished between three di erent types of probability: a priori probability; statistical probability; and estimates. e standard example of the rst type, a priori, is the odds of roll- ing any number on a die. Here the probability of occurrence is known speci cally; that is, if there are n mutually exclusive and exhaustive events and if they are equally likely, then the prob- ability of a given event occurring is 1/ n. For a six-sided die, n=6 and the probability of throwing any single number becomes 1/6. Statistical probability identi es probability with relative fre- quency over a long series of events or the proportion of an event in a large population. In this case, we need to have observed enough relevant data for us to make forward predictions. However, when there is no valid basis of any kind for classify- ing instances, only estimates can be made. In this nal case, the use of any kind of statistical analysis would be meaningless. is becomes increasingly relevant where we are dealing with so called black swan? events, which by their very nature lack statistically available data. Most risk management practiced today focuses predom- inantly on the first two types of probability; either that the outcomes are known de nitively or that there is an underly- ing number or truth that can be found simply by further data analysis and interpolation. is type of uncertainty is termed epistemic. It is due to a lack of knowledge about the behavior of the system. e epistemic uncertainty can, in principle, be elim- inated with su cient study and, therefore, expert judgments may be useful in its reduction. A new type of pseudo-scientific management emerged alongside the mathematical development in the 1950s: proj- ect management. is consisted of the development of formal tools and techniques to help manage large, complex projects that were considered by their very nature to be uncertain or risky. It was dominated by the construction and engineering industries, with companies such as Du Pont developing criti- cal path analysis and RAND Corporation developing program evaluation and review technique methodologies. Following on the heels of these early project management techniques, institutions began to be formed in the 1970s as repositories for these developing methodologies. e American Project Management Institute was founded in 1969; the organi- zation now has more than 700,000 members, with 250 chapters in more than 171 countries. It was followed in 1975 by the UK Association of Project Managers (changed to Association for Project Management in 1999) with 19,500 individual and 500 corporate members, which has its own set of methodologies. ey were set up to explicitly capture and codify the processes by which they believed successful projects should be managed, and they developed quali cations and guidelines to support them in this process. However, while the worlds of physics, mathematics, eco- nomics, and science have moved on beyond Newtonian methods toward a more behavioral understanding of their elds, project and risk management appear largely to have remained stuck to those principles of the 1950s. Risk management e general perception among most project and risk manag- ers that we can somehow control the future is, in my opinion, one of the most ill-conceived in risk management. However, I believe we have made at least two advances in the right direc- tion. Firstly, we now have a better understanding about the 02LEADERSHIP Were better at predict- ing events at the edge of the galaxy or inside the nucleus of an atom than whether itll rain on aunties garden party three Sundays from now. Because the prob- lem turns out to be di erent. We cant even predict the next drip from a dripping tap when it gets irregular. Its the best possible time to be alive, when almost everything you thought you knew is wrong.? ?From the Tom Stoppard play Arcadia