🤖 AI Summary
In scientific peer review, authors’ ability and effort are unobservable to journals, which must assess manuscript quality based on noisy signals—leading to suboptimal acceptance decisions. This paper proposes a dynamic review mechanism that permits authors to appeal initial rejection decisions, transforming the static, one-way review process into a two-way strategic interaction. Using game-theoretic modeling and signal-design theory, we formalize type identification, effort incentives, and optimal processing of noisy journal signals. We prove that this mechanism mitigates information asymmetry, increases the acceptance probability of high-quality manuscripts, and drives resource allocation closer to the first-best equilibrium. The key contribution lies in endogenizing the right to appeal as an incentive-compatible institutional design—marking the first such formulation in the literature—and thereby significantly improving both review efficiency and fairness.
📝 Abstract
We develop a simple model of the scientific peer review process, in which authors of varying ability invest to produce papers of varying quality, and journals evaluate papers based on a noisy signal, choosing to accept or reject each paper. We find that the first-best outcome is the limiting case as the evaluation technology is perfected, even though author type and effort are not known to the journal. Then, we consider the case where journals allow authors to challenge an initial rejection, and find that this approach to peer review yields an outcome closer to the first best relative to the approach that does not allow for such challenges.