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Management and Anticipation Lesson 2: Trials of Strength

“It’s inevitable that one or other of the mutations will [pass easily between humans] and H5 looks the favourite candidate. Some are saying H5 has been around for 10 years and never will but I don’t believe that.” (Professor John Oxford, cited in BBC news Magazine’s “Is Bird Flu Still a Threat?“)

As with anticipation, there is something peculiar about risks. They do not ‘happen’ but always stand in reserve. Yet risks are not simply future events; they have a presence and the presence is expressed in the language of probability. The presence of a risk shows up discursively and can be supported by a range of devices, most notably statistics, simulation models and anecdotes. Risks are made present through these devices and ‘become real’ as they mobilize concern, call for action, and thus turn into objects to be managed (Van Loon, 2002).

Risks also undergo trials of strength (Latour, 1988). Simulations and experiments are often favoured trials of strength to test ‘what happens if’ a risk is encouraged to become an actuality (and ceases to be a risk). However, life itself is also a trial of strength and the case of Avian Influenza H5N1 is just one example of what happens to a risk if it seems to be ‘failing’ its trial of strength. When that happened, new formats of mediation were deployed to translate this failure into a new virtuality, one that could be deployed to re-affirm the adequacy of the initial anticipation.


One Comment

  1. Pablo Markin wrote:

    While statistics might aid in assessing risks, it seems counter-intuitive that tools that rely on repeated random samples may be of any use for events that in financial theoretical parlance are black swans. Recent public discourse on bad apples sheds indirect light in this regard since an anticipated percentage of failures in a given population of cases does not take into account shocks to the sampled system that would make previous calculations inapplicable. In other words, I am not sure that it is easy to decide whether risks are known unknowns or unknown knowns, to refer to by now famous media statements. If normal distribution of sampled population is a statistical ideal type, than estimates of risks are only as good as the ideal type is close to actual reality. So that phenomena of the black swan type, indicate failures on the scale of the system itself rather than of a single case or a group of cases.

    Saturday, March 28, 2009 at 09:13 | Permalink