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Saint Matthew strikes again: An agent-based model of peer review and the scientific community structure

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  • Squazzoni, Flaminio
  • Gandelli, Claudio

Abstract

This paper investigates the impact of referee reliability on the quality and efficiency of peer review. We modeled peer review as a process based on knowledge asymmetries and subject to evaluation bias. We tested various levels of referee reliability and different mechanisms of reviewing effort distribution among agents. We also tested different scientific community structures (cohesive vs. parochial) and competitive science environments (high vs. low competition). We found that referee behavior drastically affects peer review and an equal distribution of the reviewing effort is beneficial only if the scientific community is homogeneous and referee reliability is the rule. We also found that the Matthew effect in the allocation of resources and credit is inherent to a ‘winner takes all’ well functioning science system, more than a consequence of evaluation bias.

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  • 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.
    • Handle: RePEc:eee:infome:v:6:y:2012:i:2:p:265-275
    DOI: 10.1016/j.joi.2011.12.005
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    Cited by:

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    2. Zheng Xie & Zonglin Xie & Miao Li & Jianping Li & Dongyun Yi, 2017. "Modeling the coevolution between citations and coauthorship of scientific papers," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(1), pages 483-507, July.
    3. Pawel Sobkowicz, 2015. "Innovation Suppression and Clique Evolution in Peer-Review-Based, Competitive Research Funding Systems: An Agent-Based Model," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 18(2), pages 1-13.
    4. 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.
    5. 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.
    6. Lukac, Martin & Grow, André, 2020. "Reputation systems and recruitment in online labor markets: insights from an agent-based model," LSE Research Online Documents on Economics 114454, London School of Economics and Political Science, LSE Library.
    7. 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.
    8. Tol, Richard S.J., 2013. "The Matthew effect for cohorts of economists," Journal of Informetrics, Elsevier, vol. 7(2), pages 522-527.
    9. Martin Lukac & André Grow, 2021. "Reputation systems and recruitment in online labor markets: insights from an agent-based model," Journal of Computational Social Science, Springer, vol. 4(1), pages 207-229, May.
    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).
    11. Yun Liu & Mengya Zhang & Gupeng Zhang & Xiongxiong You, 2022. "Scientific elites versus other scientists: who are better at taking advantage of the research collaboration network?," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(6), pages 3145-3166, June.

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