For centuries, the idea of science as the domain of lone geniuses — figures like Leonardo da Vinci, Isaac Newton, and Albert Einstein — has captivated the public imagination. However, in the 1960s, sociologist R. K. Merton introduced the concept of “multiple discoveries,” arguing that great breakthroughs are not the products of singular minds but of stimulating social conditions, making scientific progress inevitable. Using big data and complex network models, Lingfei Wu provides the first empirical quantification of this phenomenon in decades, revealing the near-universe of multiples in science. Analyzing four datasets — including Merton’s (N=264, 1600-1950), Simonton’s (N=1,434, 1350-1990), the Protein Data Bank (N=1,611, 1999-2017), and his team’s records of major breakthroughs (N=12,564, 1900-2020) — they find a consistent pattern: the grades of multiples follow power-law distributions, not the Poisson model Merton predicted. These findings challenge his mathematical framework but affirm his core idea: science arises not from individual geniuses but from its time.

About the speaker

Lingfei Wu is an Assistant Professor of Information Science at the University of Pittsburgh. His research uses big data, complexity science, and AI to explore Team Science and Innovation. While prior studies highlight the benefits of teams, his work uniquely addresses their costs—specifically, how large teams can overshadow individual autonomy, as well as creativity and recognition. He also investigates organizational and policy changes to resolve conflicts between individual and team success in innovation.