An anonymous reader shares an excerpt from a report written by Scott E. Page, who explains why hiring the “best” people produces the least creative results: The burgeoning of teams — most academic research is now done in teams, as is most investing and even most songwriting (at least for the good songs) — tracks the growing complexity of our world. We used to build roads from A to B. Now we construct transportation infrastructure with environmental, social, economic, and political impacts. The complexity of modern problems often precludes any one person from fully understanding them. The multidimensional or layered character of complex problems also undermines the principle of meritocracy: The idea that the “best person” should be hired. There is no best person. When putting together an oncological research team, a biotech company such as Gilead or Genentech would not construct a multiple-choice test and hire the top scorers, or hire people whose resumes score highest according to some performance criteria. Instead, they would seek diversity. They would build a team of people who bring diverse knowledge bases, tools and analytic skills. That team would more likely than not include mathematicians (though not logicians such as Griffeath). And the mathematicians would likely study dynamical systems and differential equations.
Believers in a meritocracy might grant that teams ought to be diverse but then argue that meritocratic principles should apply within each category. Thus the team should consist of the “best” mathematicians, the “best” oncologists, and the “best” biostatisticians from within the pool. That position suffers from a similar flaw. Even with a knowledge domain, no test or criteria applied to individuals will produce the best team. Each of these domains possesses such depth and breadth, that no test can exist. When building a forest, you do not select the best trees as they tend to make similar classifications. You want diversity. Programmers achieve that diversity by training each tree on different data, a technique known as bagging. They also boost the forest ‘cognitively’ by training trees on the hardest cases — those that the current forest gets wrong. This ensures even more diversity and accurate forests.
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