  It is ever more common in scientific publishing to ask authors to suggest
some reviewers for their own manuscripts. The question then arises: How many
submissions does it take to discover friendly suggested reviewers? To answer
this question, we present an agent-based simulation of (single-blinded) peer
review, followed by a Bayesian classification of suggested reviewers. To set a
lower bound on the number of submissions possible, we create a 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.
