Right now, science is in a bit of an odd situation. Since 2010 we have imaged a black hole, revolutionized genetic engineering, listened in on gravitational waves, approved the first immunotherapy treatments for cancer, and developed mRNA vaccines. Yet at the same time, research productivity in terms of societal gains has decreased by about 5% per year since the 1930s.
So as the world has beefed up its scientific spending in the last century, there have been major discoveries in the backdrop of decreased expected proportional gains. Why is this? What in science has decreased productivity since the 1930s, but doesn’t prevent the scientific enterprise from making ground-breaking discoveries?
Thinking in terms of individual productivity, an obvious cure or contaminant (depending on your perspective) is management. Compared to modern industry, academic research has relatively few practices associated with good management. Individual laboratories develop their own systems in an ad hoc manner, and operate completely independently from one another or a broader hierarchy. A survey by the Wellcome Trust of 1,934 PIs and 3885 graduate students and postdocs illustrates the current state of management in research laboratories:
“Only 48% of managers said that they had received training on managing people. And, from the point of view of the people reporting to them, only 11% of employed or student researchers said that they had been asked for feedback by their manager in the preceding year. In fact, they reported experiencing very few behaviors typically associated with effective management (on average 4 out of the 14 they were asked about). In the last 12 months, only half had received feedback on their performance (55%) or had a formal appraisal (49%). A quarter of junior researchers and students disagreed that their supervisors regularly reviewed their work (24%).”
It is appealing to conclude from this that a lack of management is holding science back and is contributing to that missing productivity. However, this snapshot misses the directionality of this trend. There's no reason to believe that management has been decreasing over time; there was certainly less management employed in research labs in the 1930s than there is today. In fact, bureaucratic demands on researchers' time has significantly increased over the last decades, with researchers now reporting they spend almost half of their time applying for and managing grant funding. Other work has shown that adding more lab members can make the lab less productive overall, and that small teams are more effective at coming up with new ideas than large teams.
In fact, given the near-complete lack of management in research labs, whether or not science could be marginally improved with management is not really the point. The more obvious observation is that research is incredibly effective without it: since the 1960s, life expectancy in the United States has increased by 8 years, and the basic discoveries that made the achievements noted above possible (i.e. CRISPR, immunotherapy, the Covid-19 vaccine) were all done in the absence of key performance indicators or even deadlines. And while management practices are not standardized across academia, increased bureaucratic and personnel demands have correlated with diminishing scientific returns over time. So before we jump and assume that importing management practices will improve productivity, we should question what tradeoffs pushing the culture of science in that direction might trigger.
Bell Labs vs. the Superconducting Super Collider
The archetype of a successful research environment is Bell Labs. Bell Labs researchers won nine Nobel Prizes and four Turing awards; they invented the transistor and the laser, developed information theory, the UNIX operating system, and C programming language. They both invented the technology required to detect astronomical radiation, and made the fundamental discovery that the unexplained cosmic microwave background was landmark evidence for the Big Bang theory. How did the leadership of Bell Labs create this special environment that supported both scientific breakthroughs and advanced technological development, which ultimately shaped the modern world?
The answer is not management as we think of it today. Mervin Kelly, physicist and director of Bell Labs, argued that fundamental discovery was “unscheduled work”. He believed management was a distraction, and that "the nonscientific duties of management should be minimized for all levels of the research supervision.” Most of all he prioritized individual autonomy, claiming that “with all the needed emphasis on leadership, organization and teamwork, the individual has remained supreme—of paramount importance. It is in the mind of a single person that creative ideas and concepts are born."
Compare this philosophy with the management practices of a great failed scientific organization: the superconducting super collider. The goal of building a particle accelerator underneath Waxahachie, Texas died in 1993 after six years and $2 billion invested. This was a great disappointment to some, but surprisingly not all scientists were sorry to see it go. Contemporary reporting from the New York Times found:
“Life at the SSC Laboratory … had become an exercise in frustration and anxiety. Supercollider physicists and staff members talked about living under a microscope, dogged by endless audits, inspections and investigations, and suffocated by a complex bureaucracy of overseers that Dr. Sidney Drell, deputy director of the Stanford Linear Accelerator Laboratory, likened it to "the Russian management system minus the word Communism."
Other reports agree that “cultural differences between the scientific side of the accelerator’s management and the military-industrial culture imposed by the U.S. Department of Energy (DoE) led to conflicts, seemingly endless audits and an overall lack of trust.”
In this case, management was a death sentence.
Management goals are not aligned with research goals
Why might this be the case? Consider what management theory is trying to accomplish. Classical management theory emerged in the Industrial Revolution. Its goal was to streamline operations and increase efficiency in factories. To achieve these goals, it champions hierarchy, specialization, and standardization to reduce waste at every step in the process. The term ‘manage’ literally derives from the Old French word for “the handling or training of a horse.” Of course modern management theory incorporates human behavior and emphasizes team dynamics, communication, and motivation, not just brute efficiency. But there is a connective tissue linking the last century of management. The goal is to make work consistent, meaning minimize high fluctuations in the quality and quantity of work being done. Work that is predictable can be planned and accounted for in quarterly projections; work that is wild and untamed could produce something great but more likely will produce nothing at all.
For scientists, the goal is often the opposite. The fluctuations are the point; they are what bring the potential for something truly new. Uri Alon, a systems biology professor at the Weizmann Institute, said, “truly innovative science demands a leap into the unknown.” Ellen Rothenberg, a Distinguished Professor of biology at Caltech, described the essence of being a scientist as “constantly questioning everything.” Physicist and professor at the Institute for Advanced Studies Nima Arkani-Hamed went even farther, saying “often the concepts that end up being relevant, that we end up needing to understand things more deeply, are so foreign to the ideas we have now that we can’t even articulate the correct question before we happen to be in the neighborhood of the right answer.” Working with that amount of uncertainty for years is very uncomfortable, even painful. It is also the only way to make meaningful progress.
Unsurprisingly, these demands often chafe against the things that management theory champions: hierarchy, specialization, and standardization. Academic scientists do not see themselves as cogs in an assembly line or individual contributors to a project determined by the higher-ups. Ruth Lehmann, biologist and director of the Whitehead Institute for Biomedical Research, described the source of her creativity as self-determination: “I know that I can determine what I want to do and that is the most creative thing.”
To be clear, effective leadership is absolutely important for research. People need to be able to communicate, navigate conflict, and motivate each other. Many of the management practices that people ignore are basic functions like asking for feedback, noting achievements, and discussing career options. Every advisor should be doing these things, and support for these practices could have a positive impact in both research culture and productivity. Lack of management is not an excuse for bullying or neglect.
But in other aspects research requires something new, which incorporates lessons from other fields that invest in inefficiency. My own PhD advisor Gord Fishell told me many times, “you can run your lab like a ‘factory’ or like an ‘artists’ colony’." In many ways scientists are much more like artists than industrialists. Rick Rubin, legendary music producer and nine-time Grammy award winner, said:
“Business thinks in terms of quarterly earnings and production schedules. The artist thinks in terms of timeless excellence”
This is the mindset that scientists should aim for. Theoretical physicist Tom McLeish said “Our job is to re-imagine the universe. ... The work of a scientist is the work of a huge centuries’ long work of human imagination.” How can you do work that will be relevant in 100 years? How can you build a system and culture that supports that work? There is surely no ‘one size fits all’ solution to these questions, but I do think that they are the right questions to ask. Maybe that means we’re in the neighborhood of the right answer.
Here at research theory we’re hoping we’re in the neighborhood. We’re working hard to understand effective creative, organizational, and leadership patterns and practices to help unlock creativity and innovation in today's modern research environment. To do this we’re looking to the past for traits of the people and organizations that lead to breakthroughs and building programs to help teach and test those traits.
"Claude Monet Painting by the Edge of a Wood" at the Colony of Giverny, by John Singer Sargent, 1885