It’s common to hear that to work out a big research problem, you need a big team. A new analysis of more than 65 million papers, patents, and software projects suggests otherwise.
Researchers examined 60 years of publications and found that smaller teams are far more likely to introduce new ideas to science and technology, while larger teams more often develop and consolidate existing knowledge.
The findings suggest that experts should reassess recent trends in research policy and funding toward big teams.
“Big teams are almost always more conservative. The work they produce is like blockbuster sequels; very reactive and low-risk,” says coauthor James Evans, professor of sociology and director of the Knowledge Lab at the University of Chicago.
“Bigger teams are always searching the immediate past, always building on yesterday’s hits. Whereas the small teams, they do weird stuff—they’re reaching further into the past, and it takes longer for others to understand and appreciate the potential of what they are doing.”
Disrupt or develop?
For the new study, which appears in Nature, researchers collected 44 million articles and more than 600 million citations from the Web of Science database, 5 million patents from the US Patent and Trademark Office, and 16 million software projects from the Github platform. The researchers then computationally assessed each individual work in the massive dataset for how much it disrupted versus developed its field of science or technology.
“Intuitively, a disruptive paper is like the moon during the lunar eclipse; it overshadows the sun—the idea it builds upon—and redirects all future attention to itself,” says coauthor and postdoctoral researcher Lingfei Wu.
“The fact that most of the future works only cite the focal paper and not its references is evidence for the ‘novelty’ of the focal paper. Therefore, we can use this measure, originally proposed by Funk and Owen-Smith, as a proxy for the creation of new directions in the history of science and technology.”
The findings show that disruption dramatically declined with the addition of each additional team member. The same relationship appeared when authors controlled for publication year, topic, or author, or tested subsets of data, such as Nobel Prize-winning articles.
Even review articles, which simply aggregate the findings of previous publications, are more disruptive when authored by fewer individuals, the study shows.
Less is more
The main driver of the difference in disruption between large and small teams appears to be how each treat the history of their field. Larger teams are more likely to cite more recent, highly cited research in their work, building upon past successes and acknowledging problems already in their field’s zeitgeist.
By contrast, smaller teams more often cite older, less popular ideas, a deeper and wider information search that creates new directions in science and technology.
“Small teams and large teams are different in nature,” Wu says. “Small teams remember forgotten ideas, ask questions, and create new directions, whereas large teams chase hotspots and forget less popular ideas, answer questions, and stabilize established paradigms.”
The analysis shows that both small and large teams play important roles in the research ecosystem, with the former generating new, promising insights that larger teams rapidly develop and refine.
Some experiments are so expensive, like the Large Hadron Collider or the search for dark energy, that a single, massive collaboration is the only way to answer them. But an ensemble of independent, risk-taking small teams rather than a large consortium may more effectively pursue other complex scientific question, the authors argue.
“In the context of science, funders around the world are funding bigger and bigger teams,” Evans says. “What our research proposes is that you really want to fund a greater diversity of approaches. It suggests that if you really want to build science and technology, you need to act like a venture capitalist rather than a big bank—you want to fund a bunch of smaller and largely disconnected efforts to improve the likelihood of major, path-breaking success.”
“Most things are going to fail, or are not going to push the needle within a field. As a result, it’s really about optimizing failure,” Evans says. “If you want to do discovery, you have to gamble.”
Source: University of Chicago
