Don’t give up: Older people can have creative breakthroughs

December 10, 2016
By
Albert-László Barabási

Many of this year’s cohort of Nobel laureates achieved their award-winning efforts when they were quite young — a phenomenon detected by decades of research on creativity. J. Michael Kosterlitz, co-recipient of the Nobel in physics, was 31 at the time of his prizewinning discovery, and his collaborator, David J. Thouless, was 39. Bob Dylan, the literature winner, wrote his defining work even earlier, in his 20s.

Based on this pattern, one might assume that once you pass this early career stage, your chances of making a breakthrough drops precipitously. Einstein, who developed his theory of special relativity at the tender age of 26, put it bluntly: “A person who has not made his great contribution to science before the age of thirty will never do so.”

Yet as we show in a paper recently published in Science, our ability to have a creative breakthrough does not diminish with age. It is our productivity and will to keep trying that decline, not our creative potential. For those who stick with it, success can come at any point in their career, and if they keep trying, it can return over and over again.

Our understanding of creativity comes mainly from studies of recognized geniuses. Studies conducted in the 1980s by psychologist Dean Keith Simonton, who inspected the careers of 2,026 notable scientists and inventors from antiquity to the 20th century, found that most of them made their mark on history around the age of 39, bolstering the contention that youth equals creativity. Benjamin Jones, an economist who analyzed 525 Nobel Prizes between 1900 and 2008, saw a slight increase in the age of winners across the years, rooted mainly in the increased schooling as time passed.

We took a different tack. The long-standing focus on genius prompted Professor Roberta Sinatra and me to ask: When do bursts of creativity happen in not just extraordinary but also ordinary scientific careers? We thought this would give us better insight into the effect age has on creativity at large, as opposed to just creativity in extreme and unusual careers.

We inspected the careers of tens of thousands of scientists in disciplines ranging from physics to math, biology to computer science. Our results confirmed the decades of research on creativity: Most scientists published their defining work within two decades of the start of their scientific career. In other words, geniuses and everyday scientists alike cease to be creative by the third decade of their career.

When we asked why, however, we stumbled across something unexpected. First, we found that productivity — the number of papers published by an individual — has the same early peak as creativity. We scientists are not only the most creative in the first two decades of our careers; we are more productive as well. This made Roberta and me suspicious about the roots of our creative success: Is it because we are young, or is it because we simply buy more raffle tickets during those early decades?

We next arranged every paper the scientists had published in chronological order, asking if the highest impact paper was among the earliest of each person’s career, somewhere in the middle, or perhaps among the last. In other words, we took age and productivity out of the equation, viewing each paper as another attempt at a breakthrough.

And there lay something unexpected. The highest impact papers were rarely the scientists’ earliest ones. Instead, the biggest hits were completely random: They were just as likely to be a first work as a last one, or anywhere in between.

Our surprising conclusion: Fresh-faced thinkers disproportionately break through not because youth and creativity are intertwined but because they produce more work early in their career. Indeed, 30 years into a scientific career there is a sixfold drop in productivity compared with productivity at any time within the first 20 years. Hence scientists’ early success has little to do with the vibrant ideas they bring to the stodgy establishment. Rather, undeterred by disinterest or failure, young people try again and again.

These results are good news for those of us with graying hair. Sure, success can come early, as it did for Frank G. Wilczek, who received the 2004 Nobel in physics for the very first paper he co-authored as a graduate student. But it can also come later, as it did for this year’s Nobel winner in physiology or medicine, Yoshinori Ohsumi, who was 48 when he made his breakthrough. In fact, it can even come very late, as it did for John B. Fenn, a chemist whose Nobel-winning discovery came after Yale shut down his lab when he turned 70, the mandatory retirement age then.

Equally important, our data show that if you were fortunate enough to have had that coveted early-career breakthrough, there may be more to come. Einstein, despite his age-of-30 admonition, was 59 when he published his finding on quantum entanglement, his most cited work today. And our finding is likely not limited to scientists. Steve Jobs, for example, may have founded Apple at 21, but the company’s most commercially successful innovations — the iMac, the iPhone — came only in his 40s and early 50s.

So if you missed that early spark, don’t despair: as long as you stay with it, success can still be yours.

Originally Published by the Washington Post (2016)

Photo Credit: Harris & Ewing/Library of Congress

Figure 1. How hard is to distinguish random from scale-free networks? To show how different are the predictions of the two modeling paradigms, the scale-free and that or the random network models, I show the degree distribution of four systems: Internet at the router level; Protein-protein interaction network of yeast; Email network; Citation network, together with the expected best Poisson distribution fit. It takes no sophisticated statistical tools to notice that the Poisson does not fit.
Box 3: All we need is love

If you have difficulty understanding the need for the super-weak, weakest, weak, strong and strongest classification, you are not alone. It took me several days to get it. So let me explain it in simple terms.

Assume that we want to find the word Love in the following string: "Love". You could of course simply match the string and call it mission accomplished. That, however, would not offer statistical significance for your match.

BC insist that we must use a rigorous algorithm to decide if there is Love in Love. And they propose one, that works like this: Take the original string of letters, and break it into all possible sub-strings: 

{L,o,v,e,Lo,Lv,Le,ov,oe,ve,Lov,Loe,ove,Love}. 

They call the match super-strong if at least 90% of these sub-strings matches Love. In this case we do have Love in the list, but it is only one of the 14 possible sub-strings, so Love is not super strong.  

They call the match super-weak if at least 50% of the strings matches the search string. Love is obviously not super-weak either.

At the end Clauset's algorithm arrives to the inevitable conclusion: There is no Love in Love.

The rest of us: Love is all you need

‍Figure 3. Differentiating model systems Curious about the reason the method adopted by BC cannot distinguish the Erdős-Rényi and the scale-free model, we generated the degree distribution of both models for N=5,000 nodes, the same size BC use for their test. We have implemented the scale-free model described in Appendix E of Ref [1], a version of the original scale-free model (their choice is problematic, btw, but let us not dwell on that now). In the plot we  show three different realizations for each network, allowing us to see the fluctuations between different realisations, which are small at this size. The differences between the two models are impossible to miss: The largest nodes in any of the Erdős-Rényi networks have degree less then 20, while the scale-free model generate hubs with hundreds of links. Even a poorly constructed statistical test could tell the difference. Yet,  38% of the time the method used by BC does not identify the scale-free model to be even ‘weak scale-free,’  while 51% of the time it classifies the ER model to be ‘weak scale-free.’

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