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Peer Review, Program Officers and Science Funding

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  • Paul J Roebber
  • David M Schultz

Abstract

Increased competition for research funding has led to growth in proposal submissions and lower funding-success rates. An agent-based model of the funding cycle, accounting for variations in program officer and reviewer behaviors, for a range of funding rates, is used to assess the efficiency of different proposal-submission strategies. Program officers who use more reviewers and require consensus can improve the chances of scientists submitting fewer proposals. Selfish or negligent reviewers reduce the effectiveness of submitting more proposals, but have less influence as available funding declines. Policies designed to decrease proposal submissions reduce reviewer workload, but can lower the quality of funded proposals. When available funding falls below 10–15% in this model, the most effective strategy for scientists to maintain funding is to submit many proposals.

Suggested Citation

  • Paul J Roebber & David M Schultz, 2011. "Peer Review, Program Officers and Science Funding," PLOS ONE, Public Library of Science, vol. 6(4), pages 1-6, April.
    • Handle: RePEc:plo:pone00:0018680
    DOI: 10.1371/journal.pone.0018680
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    Cited by:

    1. Feliciani, Thomas & Morreau, Michael & Luo, Junwen & Lucas, Pablo & Shankar, Kalpana, 2022. "Designing grant-review panels for better funding decisions: Lessons from an empirically calibrated simulation model," Research Policy, Elsevier, vol. 51(4).
    2. Jiang Wu & Miao Jin & Xiu-Hao Ding, 2015. "Diversity of individual research disciplines in scientific funding," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(2), pages 669-686, May.
    3. Thomas Feliciani & Junwen Luo & Lai Ma & Pablo Lucas & Flaminio Squazzoni & Ana Marušić & Kalpana Shankar, 2019. "A scoping review of simulation models of peer review," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(1), pages 555-594, October.
    4. J. A. Garcia & Rosa Rodriguez-Sánchez & J. Fdez-Valdivia, 2021. "The interplay between the reviewer’s incentives and the journal’s quality standard," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 3041-3061, April.
    5. Richard R Snell, 2015. "Menage a Quoi? Optimal Number of Peer Reviewers," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-14, April.
    6. J. A. Garcia & Rosa Rodriguez-Sánchez & J. Fdez-Valdivia, 2020. "The author–reviewer game," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(3), pages 2409-2431, September.
    7. Mario Paolucci & Francisco Grimaldo, 2014. "Mechanism change in a simulation of peer review: from junk support to elitism," Scientometrics, Springer;Akadémiai Kiadó, vol. 99(3), pages 663-688, June.
    8. Wu, Jiang, 2015. "Distributions of scientific funding across universities and research disciplines," Journal of Informetrics, Elsevier, vol. 9(1), pages 183-196.
    9. Marco Seeber & Alberto Bacchelli, 2017. "Does single blind peer review hinder newcomers?," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(1), pages 567-585, October.
    10. Le, Tam-Tri & Nguyen, Minh-Hoang, 2022. "Innovative ideas need these ‘scouts’," OSF Preprints f6qta, Center for Open Science.
    11. Fong, Eric A. & Wilhite, Allen W., 2021. "The Impact of False Investigators on Grant Funding," Research Policy, Elsevier, vol. 50(10).
    12. Squazzoni, Flaminio & Gandelli, Claudio, 2012. "Saint Matthew strikes again: An agent-based model of peer review and the scientific community structure," Journal of Informetrics, Elsevier, vol. 6(2), pages 265-275.

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