Home | Publications | Humanitarian Aid on the move | Humanitarian Aid on the move #14 | Black swans and the Pareto principle: preparing for the unpredictable

The Groupe URD Review

Methods and tools

Quality & Accountability COMPAS Quality & Accountability COMPAS
CHS Core Humanitarian Standard (CHS)
Pictogrammme Sigmah Sigmah Software
Pictogrammme Reaching Resilience

Reaching Resilience
Pictogrammme brochure Environnement Training
Pictogrammme brochure Participation Handbook
Pictogrammme globe terrestre The Quality Mission
Pictogrammme PRECIS Humatem PRECIS Method

Black swans and the Pareto principle: preparing for the unpredictable
François Grünewald

The analysis of future scenarios shows that unpredictable, rare and violent phenomena, known as “black swans”, remain more than probable. The planet is effectively faced with a growing number of events which are increasingly devastating, and which contrast with the statistical data of the past, and remind us of our vulnerability to extreme climatic phenomena and the weakness of our predictive models with regard to the unknown. Black swans are often seen as of secondary importance in global forecasting approaches. However, given the importance of their impacts on communities and on fragile economies, they should be central to discussions about anticipation, prevention, prediction and preparation procedures in more and more regions of the world.

International debate [1] about unknown or extremely unlikely hazards with a major impact – known as “black swans” – is still in its infancy due to the fact that these events - and as a consequence the statistics about them - are so rare. The earliest strategic reflection took its inspiration from the Pareto principle (also referred to as the 80-20 law) which theorises a phenomenon which has been observed in many sectors: around 80% of the effects come from around 20% of the causes. Looking outside the Pareto curve forces us to look at these very rare events with effects on an undetermined scale. The questions raised by Nassim Nicholas Taleb – summarised in his controversial but very welcome book, “The Black Swan: The Impact of the Highly Improbable” – have re-opened debates about the use of statistics, adapting the underlying models and the difficulties of predicting what is uncertain on the basis of data from the past. Fascinating ideas such as René Thom’s disaster theory or research on fractal or chaotic phenomena, as well as the research on prospective methods and based on scenarios, have brought complementary theoretical foundations to address the issue of black swans.

Research about the impact that climate change will have and the uncertainty about the impact of phenomena like the melting of permafrost in Siberia is just beginning to produce the first predictive models. Indeed, these force us to move out of the Pareto curve and look at the parts of the curve which are very infrequent, which are often overlooked. The last report by the Inter-governmental Panel on Climate Change (IPCC) [2] on managing the risks of extreme events and disasters to make progress on climate adaptation is very alarming. With an increase in the average temperature of 1°C during the last century, radical changes are beginning to be observed. The planet is faced with more and more devastating events which do not correspond to the statistical data of the past. The increasingly frequent waves of drought which regularly affect the Horn of Africa, the Sahel, Australia and the Southern coast of the United States, and which are often accompanied by large scale forest fires; cyclones or torrential rain which regularly affects the tropics; devastating tornadoes in the United States; and floods in Europe, and notably the South-East of France, are there to remind us both of our vulnerability to extreme climatic phenomena and the weakness of our predictive models in relation to the unknown.

The many methodological precautions which exist (see table below [3]) remind us that the more extreme and therefore rare an event is, the less data there is about it, and consequently the more difficult to construct models based on historical frequency of occurrence.


The future is…

[1] See, for example, the work of Didier Sornette, about predicting disastrous events: http://www.ffsa.fr/webffsa/risques.nsf/b724c3eb326a8defc12572290050915b/0e7a2ea7d30774f7c12573ec0042ec93/$FILE/Risques_50_0026.htm and that of Anis Borchani about the statistics of extreme values in relation to discrete laws: http://hal.archives-ouvertes.fr/docs/00/57/25/59/PDF/10009.pdf

[2] IPCC website : http://www.ipcc.ch
MICE website (Modelling de Change of climate extremes): http://www.cru.uea.ac.uk/projects/mice/html/extremes.html
IMFREX website: http://medias1.mediasfrance.org/imfrex/web/ Website which brings together statistics on extreme events: http://www.isse.ucar.edu/extremevalues/extreme.html

[3] Adapted from different sources on crisis management in businesses.