Tuesday, 9 October 2012

Predicting failure

The problem with predicting failure is, well, that it often fails...

"A Nobel Prize for Failure"

"Yesterday saw the announcement of the first Nobel Prize of 2012, which was the Nobel Prize for Physiology or Medicine. The prize was awarded jointly to John B. Gurdon and Shinya Yamanaka, for their work on stem cells. Among all the genuine plaudits and explanations about what exactly it is they got the prize for, one amusing element has come up as well.
It turns out that Gurdon was once told by his biology teacher that he was a terrible student. The full quote reads:
"I believe Gurdon has ideas about becoming a scientist; on his present showing this is quite ridiculous; if he can't learn simple biological facts he would have no chance of doing the work of a specialist, and it would be a sheer waste of time, both on his part and of those who would have to teach him."
This prediction, to put it as diplomatically as possible, turns out to have been incorrect. This was same time ago, Gurdon is now in his seventies, but I would still argue with this harsh conclusion (even without the obvious benefit of hindsight). Firmly sticking to the established facts is far from a universal requirement for decent scientists, if anything it's unhelpful. Try filling a PhD thesis with established facts, your examiners might have something to say about that."

This is a phenomenon common to entrepreneurial activity, as Gerry George and I note in our book, Models of Opportunity: How entrepreneurs design firms to achieve the unexpected.

Predicting individual or venture success when uncertainty is high and the innovation is radical or disruptive appears to be nearly impossible. Of course, this makes teaching entrepreneurship an even bigger challenge, since the tools we provide to students for opportunity assessment cannot take this type of unpredictability into account.

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