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The miracle of peer review and development in science: an agent-based model

Author

Listed:
  • Simone Righi

    (Alma Mater Studiorum - University of Bologna
    Hungarian Academy of Sciences)

  • Károly Takács

    (Hungarian Academy of Sciences)

Abstract

It is not easy to rationalize how peer review, as the current grassroots of science, can work based on voluntary contributions of reviewers. There is no rationale to write impartial and thorough evaluations. If reviewers are unmotivated to carefully select high quality contributions, there is no risk in submitting low-quality work by authors. As a result, scientists face a social dilemma: if everyone acts according to his or her own self-interest, the outcome is low scientific quality. We examine how the increased relevance of public good benefits (journal impact factor), the editorial policy of handling incoming reviews, and the acceptance decisions that take into account reputational information, can help the evolution of high-quality contributions from authors. High effort from the side of reviewers is problematic even if authors cooperate: reviewers are still best off by producing low-quality reviews, which does not hinder scientific development, just adds random noise and unnecessary costs to it. We show with agent-based simulations why certain self-emerged current practices, such as the increased reliance on journal metrics and the reputation bias in acceptance, work efficiently for scientific development. Our results find no proper guidelines, however, how the system of voluntary peer review with impartial and thorough evaluations could be sustainable jointly with rapid scientific development.

Suggested Citation

  • Simone Righi & Károly Takács, 2017. "The miracle of peer review and development in science: an agent-based model," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(1), pages 587-607, October.
    • Handle: RePEc:spr:scient:v:113:y:2017:i:1:d:10.1007_s11192-017-2244-y
    DOI: 10.1007/s11192-017-2244-y
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    Citations

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    Cited by:

    1. Thomas Feliciani & Ramanathan Moorthy & Pablo Lucas & Kalpana Shankar, 2020. "Grade Language Heterogeneity in Simulation Models of Peer Review," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 23(3), pages 1-8.
    2. Bravo, Giangiacomo & Farjam, Mike & Grimaldo Moreno, Francisco & Birukou, Aliaksandr & Squazzoni, Flaminio, 2018. "Hidden connections: Network effects on editorial decisions in four computer science journals," Journal of Informetrics, Elsevier, vol. 12(1), pages 101-112.
    3. Guy Madison & Knut Sundell, 2022. "Numbers of publications and citations for researchers in fields pertinent to the social services: a comparison of peer-reviewed journal publications across six disciplines," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(10), pages 6029-6046, October.
    4. Michail Kovanis & Ludovic Trinquart & Philippe Ravaud & Raphaël Porcher, 2017. "Evaluating alternative systems of peer review: a large-scale agent-based modelling approach to scientific publication," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(1), pages 651-671, October.
    5. Zhang, Guangyao & Xu, Shenmeng & Sun, Yao & Jiang, Chunlin & Wang, Xianwen, 2022. "Understanding the peer review endeavor in scientific publishing," Journal of Informetrics, Elsevier, vol. 16(2).
    6. Federico Bianchi & Francisco Grimaldo & Giangiacomo Bravo & Flaminio Squazzoni, 2018. "The peer review game: an agent-based model of scientists facing resource constraints and institutional pressures," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(3), pages 1401-1420, September.
    7. 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.
    8. Francisco Grimaldo & Mario Paolucci & Jordi Sabater-Mir, 2018. "Reputation or peer review? The role of outliers," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(3), pages 1421-1438, September.
    9. Monica Aniela Zaharie & Marco Seeber, 2018. "Are non-monetary rewards effective in attracting peer reviewers? A natural experiment," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(3), pages 1587-1609, December.
    10. Zhao, Zhi-Dan & Chen, Jiahao & Lu, Yichuan & Zhao, Na & Jiang, Dazhi & Wang, Bing-Hong, 2021. "Dynamic patterns of open review process," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 582(C).

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    More about this item

    Keywords

    Peer review; Evolution of cooperation; Reputation; Agent based model;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C65 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Miscellaneous Mathematical Tools
    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games

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