The Rules of Contagion

(Greg DeLong) #1

the years after Feynman announced his idea. He then went through
each journal page-by-page, looking for references to Feynman
diagrams, and tallying up how many authors adopted the idea over
time. When the team plotted the data, the number of authors using
the diagrams followed the familiar S-shaped adoption curve, rising
exponentially before eventually plateauing.
The next step was to quantify how contagious the idea had been.
Although the diagrams had originated in the US, they had spread
quickly when they arrived in Japan. Things were more sluggish in the
USSR, with a slower uptake than the other two countries. This was
consistent with historical accounts. Japanese universities had
expanded rapidly during the post-war period, with a strong particle
physics community. In contrast, the emerging Cold War – combined
with the scepticism of researchers like Landau – had stifled the
diagrams in the USSR.
With the data they had available, Bettencourt and colleagues could
also estimate the reproduction number, R, of a Feynman diagram: for
each physicist who adopted the idea, how many others did they
eventually pass it on to? Their results suggested a lot: as an idea, it
was highly contagious. Initially R was around 15 in the USA and
potentially as high as 75 in Japan. It was one of the first times that
researchers had tried to measure the reproduction number of an idea,
putting a number on what had previously been a vague notion of
contagiousness.


This raised the question of why the idea had been so catchy.
Perhaps it was because physicists were interacting with each other
frequently during this period? Not necessarily: the high value of R
instead seemed to be because people kept spreading the idea for a
long time once they’d adopted it. ‘The spread of Feynman diagrams
appears analogous to a very slowly spreading disease,’ the
researchers noted. Adoption was ‘due primarily to the very long
lifetime of the idea, rather than to abnormally high contact rates’.


Tracing citation networks doesn’t just tell us how new ideas
spread. We can also learn how they emerge. If high profile scientists
dominate a field, it can hinder the growth of competing ideas. As a
result, new theories may only gain traction once dominant scientists

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