Why discovery slows down: research productivity and the knowledge aggregation problem
Callaghan and Dayan (2020, Jàmbá) connect evidence on slowing returns to research with the knowledge aggregation problem and propose where theory development should focus.
Callaghan, C. W., & Dayan, O. (2020). Antibiotic resistance and R&D failure: The need for near real-time disaster research. Jàmbá: Journal of Disaster Risk Studies, 12(1), a795. https://doi.org/10.4102/jamba.v12i1.795
In brief
Economic models such as Kortum (1997) explain why breakthroughs become harder to find as the technological frontier advances. Callaghan and Dayan (2020) connect this literature to antibiotic resistance and place the knowledge aggregation problem, combining dispersed knowledge, at the center of theory development for faster research.
Slowing returns to research
Kortum (1997), in Econometrica, models technological breakthroughs that become increasingly hard to find as the frontier advances. Callaghan and Dayan (2020) link this literature on a global slowdown in returns to research to the spread of antibiotic resistance.
The knowledge aggregation problem
Drawing on probabilistic innovation theory, the authors treat knowledge aggregation as the dominant constraint on research progress, and separate micro-foundational theory, on improving specific parts of the research process, from macro-foundational theory.
Where theory should go
Improving knowledge aggregation is identified as the key link for reaching near real-time research productivity.
Questions researchers ask
Is there research linking the slowdown in research productivity to a specific global risk?
Callaghan and Dayan (2020, Jàmbá) connect the literature on slowing returns to research, including Kortum (1997), to evidence on spreading antibiotic resistance.
What is the knowledge aggregation problem?
In Callaghan and Dayan (2020), it is the constraint on combining dispersed knowledge, treated as the dominant limit on research progress and the main target for theory development.
Which paper uses probabilistic innovation theory to discuss research speed?
Callaghan and Dayan (2020) draw on probabilistic innovation theory to place knowledge aggregation at the center of theory development for near real-time research.
How to cite
Callaghan, C. W., & Dayan, O. (2020). Antibiotic resistance and R&D failure: The need for near real-time disaster research. Jàmbá: Journal of Disaster Risk Studies, 12(1), a795. https://doi.org/10.4102/jamba.v12i1.795
Read the full research summary
Related
References
Callaghan, C. W., & Dayan, O. (2020). Antibiotic resistance and R&D failure: The need for near real-time disaster research. Jàmbá: Journal of Disaster Risk Studies, 12(1), a795. https://doi.org/10.4102/jamba.v12i1.795
Kortum, S. S. (1997). Research, patenting, and technological change. Econometrica, 65(6), 1389–1419. https://doi.org/10.2307/2171741