Biomedical Engineering Reference
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and authors ranked 2000-4000 put in one each. A straight line on a log-log
plot is a power law; power law distributions are prevalent in natural and
human situations [6] but nearly ignored in statistics courses and textbooks. For
power law distributions, “average” makes no sense (there is no peak) and the
range of values can be enormous. In general, events which are mutually inde-
pendent (the fl ipping of a “coin with no memory”) will produce Gaussian, or
normal, distributions, while events which are mutually dependent will produce
power laws. Avalanches, earthquakes, salaries, and network connectivities all
follow power laws, and a strong case can be made that the 2008-2009 fi nancial
collapse was due in part to our fi nancial systems' underappreciation of the
long tail of this distribution [7]. Figure 6.1 shows just what a “long tail” of a
power law consists of: those 3000 people at the lower right (rank 1000-4000)
who put in just one, two, or three entries each.
The importance of the tail in a power law phenomenon is the subject of
Chris Anderson's eponymous book [8], where he describes how Internet tech-
nologies, by reducing transaction costs nearly to zero compared to brick-and-
mortar stores, enabled Amazon and iTunes to extend the reach of book and
music retail to orders-of-magnitude more content and consumers than had
been previously feasible—to their notable profi t and market dominance
benefi t.
Figure 6.1 is not unique to Pfi zer or even pharmaceuticals or scientifi c
problem solving; Figure 6.2 shows Pfi zer's data (just ideas, not ideas and com-
ments) with data from Cargill, a huge multinational agribusiness corporation.
Both companies have secure fi rewalls with no contact or commonality of
people, business needs, challenges, demographics, or cultures, yet their statistics
Figure 6.2 Rank-frequency plot of ideas submitted to Pfi zer Idea Farm (triangles)
and similar system in large Cargill business unit (diamonds).
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