How many submissions does it take to discover friendly suggested reviewers?

TL;DR

This study uses simulation and Bayesian analysis to determine how many manuscript submissions are needed to identify friendly reviewers, concluding that it requires hundreds of submissions, making it practically unfeasible.

Contribution

It introduces an agent-based simulation and Bayesian classification approach to assess the difficulty of discovering friendly reviewers in peer review.

Findings

Hundreds of submissions are needed to identify friendly reviewers.

The model's optimistic assumptions still require many submissions.

Peer review system remains robust against reviewer friendliness detection.

Abstract

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.