Journal Competition and the Sustainability of Peer review: An agent based model

Abstract

Peer-review represents the main mechanism of selecting for publishing high-quality content with the purpose of maintaining the quality, validity, and originality of scientific publications. However as peer-review is being performed by humans, it is prone to errors and bias which might underline its incapability to fulfill its purpose. Therefore, we propose to study the peer-review as a cooperation problem that depends heavily on the level of competition between individuals and their interests. We build a new agent-based model where both scientists and journals co-evolve in a competitive environment, but where public good benefits are granted for high-effort scientists. We extend the work of Righi and Takacs (2017) by looking at emergence in a broad set of potential author and reviewer strategies, as well as by exploring the editorial policies that appear from the evolutionary process. We build an agent-based model to study various scenarios and how they can impact the overall scientific quality of publications. Thereby, we explore how different editorial strategies with various levels of strictness improve or deteriorate the commitment of reviewers, or how different cost of producing high-quality work changes the attitude of scientists. Moreover, we observe which editorial strategies would survive in the evolution process, both in the case of co-evolution or independent evolution. We obtained a working agent-based model that produced clear and robust results. Our baseline model can achieve both high-quality papers and reviews from agents, which proves that the introduction of public good benefits stimulates individual commitment toward peer review. The results remain valid even when we considered a different set-up for the scientific social network or when we increased the costs of producing good papers. We also observed that extreme levels of competition would alter the system as agents would start to act according to their interests. Lastly, we saw that editorial strategies converge even in the case of chaotic systems.