The Difference between Risk and Uncertainty

6 Oct

There is a big difference between risk and uncertainty, and getting the two confused can have a significant impact on the effectiveness of decisions.

The economist Frank Knight drew a line between risk and uncertainty, and I’ll very crudely summarise it in a sentence risk is in the past (empirical) and uncertainty is in the future (unknown). So, any attempt to predict\ forecast off empirical data is in some degree flawed. Basing future planning off empirical data alone without sufficient contingency for uncertainty is not only potentially lethal for an organisation (see Value at Risk for an example), it also stifles innovation and adaptability which you need when plans unravel as they get hit by uncertainty.

This is a problem for risk management and risk registers, filling them in can create confusion between risk and uncertainty; managing risk is not the same as being prepared for uncertainty. Preparing for uncertainty is about being adaptable, with the psychologist Gerd Gigerenzer calling on organisations to develop “adaptive toolkits” to increase their resilience.

In my mind adaptive toolkits are well illustrated in Klein’s (1989) original studies of how firefighters actually make decisions in natural settings. The firefighters didn’t actually believe they made decisions, instead they “just knew what to do”. The vast levels of experience, along with the immediate feedback they received on the effectiveness of their choices, allowed them to intuitively select a path of action from their memory, and then adapt it to the specific situation. The firefighters did not use complex decision models or plans, they used tested “rules of thumb”, imperfect short cuts which work well most of the time. If a rule of thumb didn’t work the firefighters adapted it, adding to the reservoir of available experience.

The firefighter’s domain was fast, feedback was quick, they were directly impacted by their decisions, and the environment was saturated in visual data-cues and patterns which were tractable. In contrast, the world of most organisations is far more abstract, with distance between decision and action, planner and implementor. And most significantly, the levels of uncertainty are higher due to the increased levels of volatile parameters-basically people not doing what you thought they would. Expertise in these types of environment is far harder to get, but instead of trying to develop it, the instinct is to go for risk management models, procedures and greater levels of complexity. This is evolutionary understandable but not appropriate.

Complex initiatives actually increase the distance between the organisation and their environment, making them far less able to adapt. By contrast, Gigerenzer (2014) has demonstrated that even in the most complex of financial situations,  rules of thumb beat complex models. Taleb (2012) has gone further with his demonstrations of optionality.

The confusion between risk and uncertainty has a control\manage effect, but the data the control is built on has limited value when dealing with uncertainty. The better option is to focus on the development of an adaptive toolkit which contains the rules of thumb that have proven to deal effectively with complex situations; the equivalent of the fire fighters intuition. Extracting these rules of thumb can present a challenge and a very different mind-set, but the evidence is gathering that this is the more effective route for an organisation to go (see Taleb, 2012).

Gigerenzer, G. (2014) Risk Savvy: How to Make Good Decisions. Allen Lane

Taleb, N.N. Antifragile. Things That Gain from Disorder. Penguin.

Klein, G. A. (1989). Recognition-primed decisions. In W. B. Rouse (Ed.),
Advances in man-machine systems research (Vol. 5, pp. 47–92).
Greenwich, CT: JAI Press.

Frank Knight’s work has a good Wikipedia entry

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