“Talent vs. Luck: the role of randomness in success,” written by two theoretical physicists, A. Pulchino, and A. Rapisarda, and one economist, A.E. Biondo, contrasts the “normal distribution of inputs,” in this case talent and other personal traits competitive cultures tend to associate with success, with the actual distribution of the wealth we find in our real world, which follows a power, or Pareto law.
We’ll highlight one random example, that old aardvark thing. One team found the lucky owners of surnames with initials that occur early in the alphabet are “significantly more likely” to be tenured in top-ten economics departments—where the norm is the list co-authors alphabetically—and somewhat more likely to receive Clark medals and Nobel prizes, even controlling for place of birth, religion, and ethnicity. That effect disappears as the lens takes in the top 35 departments, and doesn’t exist in top psychology departments where co-authors are listed by contribution.
There is lots more in the name department, valuable middle initials, easy to pronounce names, women with “masculine monikers” better off in law careers, noble-sounding names leading to management positions. And, one to focus on, that innovative ideas are often the results of a “random walk in our brain network.”
The idea is not that successful people are without talent, need we say?, but that talent isn’t enough, and that random luck plays out in subtle ways, leading to what Nassim Taleb calls “narrative fallacy,” and Paul Lazarsfeld, “hindsight bias.” The study finds that “ordinary people with an average level of talent,” are more likely to be successful if luck cooperates than are the “most talented” ones.
And here they get to a more promising place. Serendipity is important in research breakthroughs—penicillin, cosmic radiation, and graphene, one million times thinner than paper and stronger than diamond—were all happened upon by people looking for something else. Which is why, since we can’t know before we get there, many believe curiosity-based research should “always be funded.”
The tams cites some strategies to back off “naively meritocratic assumptions,” that impede innovation by of yet unrecognized talent, while pumping up the incumbents. They cite one study of scientific publications that found impact per dollar of funding was lower when the money went to large-grant holders, another that found “concentration” of research funding shows narrowing marginal returns, and that the best-funded researchers do not “stand out in terms of output or scientific impact.”
What we believe to be a meritocratic strategy pursues “excellence while driving out variety,” by directing resources away from research that a priori appears less promising, but a posteriori, could be “extremely innovative.”
So, which criteria? Best to give the same funding to everyone, egalitarian; only to the apparently most successful, elitarian; give a premium to the most successful and the left over to all others, mixed; or only to a given number of randomly selected researchers, selective random?
The authors find it is “much more convenient,” in that it seems to double the number of researchers both successful and among the most talented, to award periodic, even small, amounts to all individuals than to give greater capital to only a smaller percentage.
As you have probably guessed from the set-up, the elitarian strategies produce the weakest results, with the increase in the number of successful people “very small” compared to no funding at all. The mixed approach does better, but does not overtake egalitarian strategies, or the random strategies that occupy two of the three top results. With some random alignments, we get to four times the results enabled by the elite strategies.
The authors suggest equal distribution could both give unlucky yet talented people the chance to “express their potential,” while fostering serendipity at the aggregate level, which would contribute both to the progress of research, and our entire society, fulfilling the “main aim of a truly meritocratic approach.”
Disparity in research funding is often cited as a reason it’s hard to attract top talent to state universities, institutions central to promoting upward mobility. Curiosity-driven research wants to know what might happen with some random funding.
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Curiosity-Driven Research & a Random Walk
“Talent vs. Luck: the role of randomness in success,” written by two theoretical physicists, A. Pulchino, and A. Rapisarda, and one economist, A.E. Biondo, contrasts the “normal distribution of inputs,” in this case talent and other personal traits competitive cultures tend to associate with success, with the actual distribution of the wealth we find in our real world, which follows a power, or Pareto law.
We’ll highlight one random example, that old aardvark thing. One team found the lucky owners of surnames with initials that occur early in the alphabet are “significantly more likely” to be tenured in top-ten economics departments—where the norm is the list co-authors alphabetically—and somewhat more likely to receive Clark medals and Nobel prizes, even controlling for place of birth, religion, and ethnicity. That effect disappears as the lens takes in the top 35 departments, and doesn’t exist in top psychology departments where co-authors are listed by contribution.
There is lots more in the name department, valuable middle initials, easy to pronounce names, women with “masculine monikers” better off in law careers, noble-sounding names leading to management positions. And, one to focus on, that innovative ideas are often the results of a “random walk in our brain network.”
The idea is not that successful people are without talent, need we say?, but that talent isn’t enough, and that random luck plays out in subtle ways, leading to what Nassim Taleb calls “narrative fallacy,” and Paul Lazarsfeld, “hindsight bias.” The study finds that “ordinary people with an average level of talent,” are more likely to be successful if luck cooperates than are the “most talented” ones.
And here they get to a more promising place. Serendipity is important in research breakthroughs—penicillin, cosmic radiation, and graphene, one million times thinner than paper and stronger than diamond—were all happened upon by people looking for something else. Which is why, since we can’t know before we get there, many believe curiosity-based research should “always be funded.”
The tams cites some strategies to back off “naively meritocratic assumptions,” that impede innovation by of yet unrecognized talent, while pumping up the incumbents. They cite one study of scientific publications that found impact per dollar of funding was lower when the money went to large-grant holders, another that found “concentration” of research funding shows narrowing marginal returns, and that the best-funded researchers do not “stand out in terms of output or scientific impact.”
What we believe to be a meritocratic strategy pursues “excellence while driving out variety,” by directing resources away from research that a priori appears less promising, but a posteriori, could be “extremely innovative.”
So, which criteria? Best to give the same funding to everyone, egalitarian; only to the apparently most successful, elitarian; give a premium to the most successful and the left over to all others, mixed; or only to a given number of randomly selected researchers, selective random?
The authors find it is “much more convenient,” in that it seems to double the number of researchers both successful and among the most talented, to award periodic, even small, amounts to all individuals than to give greater capital to only a smaller percentage.
As you have probably guessed from the set-up, the elitarian strategies produce the weakest results, with the increase in the number of successful people “very small” compared to no funding at all. The mixed approach does better, but does not overtake egalitarian strategies, or the random strategies that occupy two of the three top results. With some random alignments, we get to four times the results enabled by the elite strategies.
The authors suggest equal distribution could both give unlucky yet talented people the chance to “express their potential,” while fostering serendipity at the aggregate level, which would contribute both to the progress of research, and our entire society, fulfilling the “main aim of a truly meritocratic approach.”
Disparity in research funding is often cited as a reason it’s hard to attract top talent to state universities, institutions central to promoting upward mobility. Curiosity-driven research wants to know what might happen with some random funding.