Students are more likely to show risk-taking behaviour if their supervisors have a tendency for risky projects. Credit: Getty
A researchers’ propensity for risky projects is passed down to their doctoral students — and stays with trainees after they leave the laboratory, according to an analysis1 of thousands of current and former PhD students and their mentors.
Science involves taking risks, and some of the most impactful discoveries require taking big bets. However, scientists and policymakers have raised concerns that the current academic system’s emphasis on short-term outcomes encourages researchers to play it safe. Studies have shown, for example, that risky research is less likely to be funded2,3. Anders Broström, an economist studying science policy at the University of Gothenburg in Sweden, and his colleagues decided to examine the role of doctoral education in shaping risk-related behaviour — an area that Broström says has been largely overlooked.
“We often focus on thinking about how we can change the funding systems to make it more likely for people to take risks, but that’s not the only lever we have,” says Chiara Franzoni, an economist at the Polytechnic University of Milan in Italy. This study is “refreshing” because “we’ve discussed policy interventions a lot, but we haven’t discussed training”, she adds.
Leaps into the unknown
Broström and his colleagues first looked at the effect of supervision on current students. They received responses to a survey designed to measure risk-taking from 1,223 PhD students enrolled in medical science programmes at seven universities in Sweden. Participants were asked, for example, to report how likely they were to take part in a safe project — defined as one that would guarantee publication in a mid-ranking journal — compared with a risky project, which was less likely to succeed but more likely to end up in a high-ranking publication.
The researchers also examined the publication history of the students and their supervisors. To calculate the risk level involved in a particular study, they used a machine-learning algorithm that predicted the likelihood of ideas from other studies being successfully combined (determined by how often pairs of citations appeared together in other publications). Papers that merged pairs of references with a high chance of not being successfully combined were considered riskier than those that did not.
The team found that students’ risk-taking dispositions matched that of their supervisors. This link was stronger when students and their supervisors communicated frequently, and weaker when students were also mentored by scientists outside their lab.
Supervisors’ influence also persisted post-graduation. When the team analysed bibliometric data from 2,400 former PhD students and their supervisors, it found that a supervisors’ influence on their students’ approach to risk persisted a decade after leaving the lab — even if the former trainees changed research topics.
