The fate of science modeled and explained in new study

What is the common thread between mRNA vaccines, genomic drugs, NASA’s mission to the moon and the use of nuclear energy? They are all products of scientific convergence, integrating knowledge from multiple scientific disciplines into new overarching knowledge that propels modern civilization. In the past 70 years, convergence has achieved more than what science has achieved in all of its previous multi-millennium history combined.

In a new article in American scientist magazine, professors Ioannis Pavlidis (University of Houston), Ergun Akleman (Texas A&M University), and Alexander M. Petersen (University of California, Merced) show that despite appearances to the contrary, convergence is not a new phenomenon that has taken science by storm, but a tendency that penetrates deep into the nature of science.

Over the course of 10 years, the researchers modeled the evolution of convergence by analyzing millions of scientific works using machine learning and other advanced data analysis methods.

In their report, the researchers identify several stages in the evolution of science, each characterized by a different form of convergence. First, polymath convergence, which characterized early science up to the Renaissance, was exemplified by famous polymaths, such as Aristotle and Leonardo da Vinci. In polymathic convergence, knowledge integration was taking place in the minds of singular scholars at that time.

This was followed by a period of disciplinary divergence in which theories developed within specific disciplines were turned into general templates with wider applications – a phenomenon the authors refer to as convergence through divergence. Darwin’s theory of evolution in biology, which has been used by others to explain economic and social systems, is a good example.

Towards the middle of the 20th century, the era of multidisciplinary team convergence dawned, in which experts from different disciplines worked together towards a common goal. In multidisciplinary team convergence, knowledge integration has taken place in teams of scientists with diverse expertise. A famous example of this kind of convergence was the Manhattan Project, which ushered humanity into the nuclear age.

“Now, in the early 21st century, we have discovered the emergence of yet another form of convergence, which we call polymatic team convergence,” said Pavlidis, Eckhard-Pfeiffer Professor of Computer Science and director of the Computational Physiology Laboratory at UH. “In polymathic team convergence, knowledge integration occurs both within and between scientists, that is, a mix of individual polymath and multidisciplinary team convergence. Recent research in brain science is showing telltale signs of polymathic team convergence.”

Interim results of the research published in the journals Nature physics (2014), scientific progress (2018) and Humanities and Social Sciences Communication (2021). The recent article in American scientist brings all these developments into a coherent and comprehensive theory.

“This is not the first theory about the underlying mechanisms of scientific evolution. It is, however, the first” science evolutionary theory based largely on massive data analysis and modelling, allowing us to not only ‘prove’ the theory’s points for the past, but also estimate confidence in the theory’s predictions for the future,” Pavlidis said.

On the latter, the team of researchers predicts that by the middle of the 21st century, convergence will evolve into what they call cyborg team convergence, where polymath scientists will collaborate with artificial intelligence (AI) agents in mixed human-machine teams.

“The first signs of cyborg team convergence are here and are detailed in our article,” Petersen noted.

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