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The Miracle of Peer Review and Development inScience: An Agent-Based Model


  • Simone Righi


  • Karoly Takacs



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. Consequently, there is no risk in submitting lowquality work by authors. As a result, scientists face a social dilemma: if everyone acts according to his or her own self-interest, low scientific quality is produced. Still, in practice, reviewers as well as authors invest high effort in reviews and submissions. 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 that tacit agreements between authors that are based on reciprocity might decrease these costs, but does not result in superior scientific quality. Our study underlines why certain self-emerged current practices, such as the increased importance of journal metrics, the reputation-based selection of reviewers, and the reputation bias in acceptance work efficiently for scientific development. Our results find no answers, however, how the system of peer review with impartial and thorough evaluations could be sustainable jointly with rapid scientifi9c development.

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  • Simone Righi & Karoly Takacs, 2016. "The Miracle of Peer Review and Development inScience: An Agent-Based Model," Center for the Analysis of Public Policies (CAPP) 0144, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
  • Handle: RePEc:mod:cappmo:0144

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    References listed on IDEAS

    1. Squazzoni, Flaminio & Bravo, Giangiacomo & Takács, Károly, 2013. "Does incentive provision increase the quality of peer review? An experimental study," Research Policy, Elsevier, vol. 42(1), pages 287-294.
    2. 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.
    3. repec:spr:scient:v:99:y:2014:i:3:d:10.1007_s11192-014-1239-1 is not listed on IDEAS
    4. Chrysanthos Dellarocas, 2003. "The Digitization of Word of Mouth: Promise and Challenges of Online Feedback Mechanisms," Management Science, INFORMS, vol. 49(10), pages 1407-1424, October.
    5. Michail Kovanis & Raphaël Porcher & Philippe Ravaud & Ludovic Trinquart, 2016. "Complex systems approach to scientific publication and peer-review system: development of an agent-based model calibrated with empirical journal data," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(2), pages 695-715, February.
    6. Wang, Jian, 2014. "Unpacking the Matthew effect in citations," Journal of Informetrics, Elsevier, vol. 8(2), pages 329-339.
    7. 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.
    8. Nigel Gilbert, 1997. "A Simulation of the Structure of Academic Science," Sociological Research Online, Sociological Research Online, vol. 2(2), pages 1-3.
    9. Flaminio Squazzoni & Károly Takács, 2011. "Social Simulation That 'Peers into Peer Review'," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 14(4), pages 1-3.
    10. Raj Chetty & Emmanuel Saez & Laszlo Sandor, 2014. "What Policies Increase Prosocial Behavior? An Experiment with Referees at the Journal of Public Economics," Journal of Economic Perspectives, American Economic Association, vol. 28(3), pages 169-188, Summer.
    11. Dellarocas, Chrysanthos, 2003. "The Digitization of Word-of-mouth: Promise and Challenges of Online Feedback Mechanisms," Working papers 4296-03, Massachusetts Institute of Technology (MIT), Sloan School of Management.
    12. repec:spr:scient:v:113:y:2017:i:1:d:10.1007_s11192-017-2264-7 is not listed on IDEAS
    13. Day, Theodore Eugene, 2015. "The big consequences of small biases: A simulation of peer review," Research Policy, Elsevier, vol. 44(6), pages 1266-1270.
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    1. repec:spr:scient:v:113:y:2017:i:1:d:10.1007_s11192-017-2375-1 is not listed on IDEAS

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    peer review; evolution of cooperation; reputation; agent based model.;

    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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