It is common in scientific publishing to request from authors reviewer suggestions for their own manuscripts. The question then arises: How many submissions are needed to discover friendly suggested reviewers? To answer this question, as the data we would need is anonymized, we present an agent-based simulation of (single-blinded) peer review to generate synthetic data. We then use a Bayesian framework to classify suggested reviewers. To set a lower bound on the number of submissions possible, we create an optimistically simple model that should allow us to more readily deduce the degree of friendliness of the reviewer. Despite this model's optimistic conditions, we find that one would need hundreds of submissions to classify even a small reviewer subset. Thus, it is virtually unfeasible under realistic conditions. This ensures that the peer review system is sufficiently robust to allow authors to suggest their own reviewers.
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