  Peer review is fundamental to the integrity and advancement of scientific
publication. Traditional methods of peer review analyses often rely on
exploration and statistics of existing peer review data, which do not
adequately address the multivariate nature of the process, account for the
latent variables, and are further constrained by privacy concerns due to the
sensitive nature of the data. We introduce AgentReview, the first large
language model (LLM) based peer review simulation framework, which effectively
disentangles the impacts of multiple latent factors and addresses the privacy
issue. Our study reveals significant insights, including a notable 37.1%
variation in paper decisions due to reviewers' biases, supported by
sociological theories such as the social influence theory, altruism fatigue,
and authority bias. We believe that this study could offer valuable insights to
improve the design of peer review mechanisms.
