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Finding Five-Sigma Talent November 26, 2008

Posted by federalist in Human Markets.

The Normal distribution has proven to be a very good model for the distribution of virtually every human characteristic and ability across populations.  Most people cluster into a small range of values around the average, but there are a few outliers with exceptional abilities and disabilities.  You can’t always predict where those extremes will appear.  But if a characteristic is in fact normally distributed then you can predict, for example, that if 70% (one standard deviation, or “sigma”) of adult men are within 3″ of the average height of 5’9″, then 1 man in a billion will grow to a height of 7 feet.

Suppose you want to find the five-sigma human specimens for some physical or mental feat.  With a global population exceeding six billion we’re essentially talking about finding the single most talented adult in the world.  A sports management company made an interesting play for this, running a contest in India (population: 1.1BB+) to find a major-league caliber baseball pitcher.  The structure of the contest may not have been ideal, but the $1MM prize and publicity were plausible incentives.  (In the end, two contestants signed contracts with a professional baseball team in the U.S.)

Searching for five-sigma talent soon leads us back to the nature vs. nurture debate: How important are innate abilities?  Is it easier to build a five-sigma athlete or scholar if you start with a person who was born with five-sigma physical or mental traits?  Perhaps transformational traits like “trainability” and “perseverance” are separate faculties that have to be included in the equation with raw talents.  Or perhaps innate ability can never contribute more than a certain fraction of a learned skill, which could mean that trained two-sigma performers are practically as good as trained five-sigma specimens.

These are not purely academic questions: Anyone who funds or profits from performers should want to know the answers.



1. deusexviro - December 14, 2008

Honestly, I have never heard this form of human classification. Where does it come from?

Still, I take five-sigma to mean the highest of the elite. And, of course, nature vs. nurture becomes nature+nurture in this case: those nearest to the highest will require less training (saving money and resources for those doing the training). Whether people can be fully trained into a “five-sigma” from a “two-sigma” is relevant, but the “four sigmas” will require less training. To most efficiently train people into “five sigmas” a testing dragnet that finds those nearest or withing trainable range would be the best way, I would think.

That’s really all I feel I can say…I don’t much know what this classification system is or where it comes from, and don’t much like speaking of human beings in this purely scientific and efficiency-based way!

2. deusexviro - December 14, 2008

Oh yes, but what if trainability can, in turn, be trained! Kidding, but the susceptibility to training would obviously, according to your theory, be a trait that must be found and quantified. But how would you decide how trainable someone is? And if we are talking of training many people, some cuts in quality must be made to train a quantity in a mass-produced environment. The clone training facilities of Kamino come to mind–but should Star Wars be consulted as a supreme textbook? Unlikely…

3. federalist - December 15, 2008

“Sigma” or “standard deviation” is an elementary statistical classification system. It is a staple of social sciences. Perhaps an inherent discomfort with such a sterile analysis of human traits has kept it from popular exposure.

However the underyling concept is well known: For example, everyone is familiar with the idea of “genius.” This is often quantified as someone who measures beyond the 3-sigma level of some intelligence indicator, which is just another way of saying that they are in the top 0.25 percentile, or that they are fewer than “1 in a 369.”

4. deusexviro - December 17, 2008

That’s fairly near to what I assumed.
Thanks for the explanation.

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