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Social Simulation That 'Peers into Peer Review'

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Abstract

This article suggests to view peer review as a social interaction problem and shows reasons for social simulators to investigate it. Although essential for science, peer review is largely understudied and current attempts to reform it are not supported by scientific evidence. We suggest that there is room for social simulation to fill this gap by spotlighting social mechanisms behind peer review at the microscope and understanding their implications for the science system. In particular, social simulation could help to understand why voluntary peer review works at all, explore the relevance of social sanctions and reputational motives to increase the commitment of agents involved, cast light on the economic cost of this institution for the science system and understand the influence of signals and social networks in determining biases in the reviewing process. Finally, social simulation could help to test policy scenarios to maximise the efficacy and efficiency of various peer review schemes under specific circumstances and for everyone involved.

Suggested Citation

  • 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.
    • Handle: RePEc:jas:jasssj:2011-52-1
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    File URL: https://www.jasss.org/14/4/3/3.pdf
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    1. Axelrod, Robert, 1986. "An Evolutionary Approach to Norms," American Political Science Review, Cambridge University Press, vol. 80(4), pages 1095-1111, December.
    2. Lutz Bornmann & Hans-Dieter Daniel, 2005. "Selection of research fellowship recipients by committee peer review. Reliability, fairness and predictive validity of Board of Trustees' decisions," Scientometrics, Springer;Akadémiai Kiadó, vol. 63(2), pages 297-320, April.
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    1. Vladimir Batagelj & Anuška Ferligoj & Flaminio Squazzoni, 2017. "The emergence of a field: a network analysis of research on peer review," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(1), pages 503-532, October.
    2. 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.
    3. Maciej J Mrowinski & Piotr Fronczak & Agata Fronczak & Marcel Ausloos & Olgica Nedic, 2017. "Artificial intelligence in peer review: How can evolutionary computation support journal editors?," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-11, September.
    4. 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.
    5. Maciej J. Mrowinski & Agata Fronczak & Piotr Fronczak & Olgica Nedic & Marcel Ausloos, 2016. "Review time in peer review: quantitative analysis and modelling of editorial workflows," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(1), pages 271-286, April.
    6. 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.
    7. 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.
    8. 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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