Rethinking resource allocation in science
Johan Bollen,
School of Informatics, Computing, and Engineering, Indiana UniversityStephen R Carpenter,
Center for Limnology, University of Wisconsin-MadisonJane Lubchenco,
Department of Integrative Biology, Oregon State UniversityMarten Scheffer,
Department of Environmental Sciences, Wageningen University, The Netherlands; South American Institute for Resilience and Sustainability Studies, SARAS, Uruguay
DOI: http://dx.doi.org/10.5751/ES-11005-240329
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Abstract
Many funding agencies rely on grant proposal peer review to allocate scientific funding, i.e., researchers compete for funding by submitting proposals that are reviewed and ranked by committees of their peers. Only a fraction of applicants are awarded the requested funds. This system has a long and venerable tradition, but it is increasingly struggling to handle the larger number of applications, suffers from high levels of administrative overhead, may be unreliable in separating successful from unsuccessful projects, and may suffer from bias against innovative ideas, young researchers, and female scientists. We have proposed redesigning funding systems according to a few simple principles, namely, focusing on funding people instead of projects and involving as many scientists in funding decisions as possible. This underpins a proposal for a novel funding system in which every scientist periodically receives an equal, unconditional amount of funding but must anonymously donate a given fraction of everything he or she receives to other scientists of his or her choice. Over time, this simple process will lead to a funding distribution that reflects the entire scientific community, fosters young scientists, and reduces overhead. However, in spite of its simplicity, we must address certain challenges in its implementation such as deciding who participates in the funding system, how to control for conflicts of interest and bias, and how to manage its application. Funding agencies will play a pivotal role in the development and management of this system.
Key words
computational science; peer review; science funding; science policy; self-organization
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