The burden of knowledge, and science policy

Jordan Dworkin: As Science Evolves, How Can Science Policy? Coming up in science, my peers and I enjoyed critiquing the institutions in which we worked, and occasionally used our computational skills to probe and quantify the scientific ecosystem. But that exercise often stopped short of attempting to fully understand the structures and incentives that produced the inefficiencies we critiqued, or identifying the paths that might be charted out of them. In the mid-2010s, the work of a growing economics-of-science community helped me start to bridge that gap. This 2011 NBER Innovation Policy and the Economy chapter by Ben Jones was particularly influential. Building on his then-recent “burden of knowledge” theory, Ben laid out the evidence that knowledge accumulation was leading to increased specialization, more extended training, and an emerging dominance of team science. But he also took the important step of grappling with how science policy would need to rethink grant mechanisms, evaluation systems, and incentive designs to adapt to the changing enterprise. There was a lot of that grappling in the roughly three-year period surrounding his piece; in hindsight, 2011-2013 saw the publication of many now-classic ideas that shape metascience today.