Peer review is the primary feedback loop and quality filter in science. Each paper that reaches publication has been read and evaluated by multiple reviewers. This evaluation is costly in time and effort. The reviewer debt model quantifies this cost and provides a precise criterion for a fair and sustainable peer-review system.
What Is Reviewer Debt?
When a paper is accepted for publication, it has typically been assessed by three reviewers. Each author of that paper has therefore benefited from peer-review labor. The reviewer debt of a scientist is the number of reviews they owe to the community to offset the reviews their papers have consumed.
The debt for a single paper with N authors is 3 reviews. The first author is typically a PhD student; the senior co-authors bear the review responsibility, so each non-first author approximately owes 3/(N-1) reviews per paper.
For example, a professor who has co-authored (as non-first author) 20 journal papers with an average of 3 authors owes 20 * 3 / 2 = 30 journal reviews. This estimate is a lower bound. Rejected submissions also consume review effort but do not appear in public databases such as DBLP.
A Python script that computes reviewer debt by venue from a DBLP profile is available at https://github.com/monperrus/review-debt.
A Sustainable System Requires Zero Net Debt
The peer-review system is sustainable if and only if the total review supply equals the total review demand. Each published paper creates demand for 3 reviews. If every scientist writes exactly as many reviews as their debt requires, supply equals demand and the system is in equilibrium.
This is a conservation argument. Each paper consumes 3 review efforts. If each author compensates for their share, the system balances. No further assumption is needed.
The argument generalizes: a system where everyone pays their debt is stable; a system with widespread non-payment degrades until a small group of over-contributors compensates for the rest.
No Centralized Infrastructure Is Required
Some proposals for improving peer review rely on centralized tracking systems, where journals or professional societies record review contributions and issue credits accordingly. These systems face adoption barriers, privacy concerns, and coordination costs.
The reviewer debt model requires none of this. Every scientist can compute their own debt. A researcher can determine their exact debt, broken down by venue, without any central authority, without registration, and without institutional coordination. The model is self-auditable and fully decentralized.
The Boy Scout Rule Applied to Peer Review
The boy scout rule in software engineering states: leave the campground cleaner than you found it (Martin, Clean Code, 2008). Applied to a codebase, each contribution should improve the code slightly beyond what the immediate task required. Applied to peer review, the rule is: write slightly more reviews than the strict debt formula requires.
This margin matters for two reasons. First, PhD students and early-career researchers have not yet accumulated a publication record but will eventually publish. Until they do, the community absorbs their future demand. Established researchers must over-contribute to cover the young researchers’ work.
A practical target: for each non-first-authored paper, write one additional review beyond the strict debt. For a professor with 20 co-authored journal papers averaging 3 authors, the strict debt is 30 reviews; the boy-scout target is 31.
Reviewer Scarcity Is a Behavioral Problem
Editors and program chairs routinely report difficulty finding willing reviewers. This scarcity is attributed to submission volume growth, reviewer fatigue, and competing time demands. These observations are accurate but incomplete.
The root cause is that reviewer debt is not uniformly honored. If every active scientist reviewed in proportion to their publication record, the total review capacity of the scientific community would match demand. The bottleneck is not a shortage of qualified reviewers. It is a shortage of contribution from those who are qualified but choose not to contribute.
The reviewer debt model does not resolve this behavioral problem by itself. It does, however, make the obligation visible and quantifiable. A scientist who knows their exact debt, by venue and in total, has a concrete and actionable target. The model converts a vague norm (“you should review”) into a precise one (“you owe 12 reviews to IEEE TSE and 8 to EMSE”).
The Impact of Artificial Intelligence
Large language models can assist in writing papers and, symmetrically, in writing reviews. This symmetry is the key point: if AI reduces the effort required to write a paper, it also reduces the effort required to write a review. The ratio of effort consumed to effort owed does not change. The reviewer debt model is unaffected.
A paper written with AI assistance still consumed the time of three human reviewers. The author’s debt is unchanged. If AI tools reduce the per-review cost, the community’s total capacity increases, but so does submission volume; the effect is symmetric.
Conclusion
If every scientist honored their reviewer debt, and added a small margin following the boy scout rule, no editor would struggle to find reviewers. The qualified reviewers exist; they are the authors of the papers under submission. The difficulty in finding them is not a capacity problem. It is a contribution problem.
Reviewer debt makes this obligation concrete. Each scientist can compute what they owe, to which venues, without waiting for a journal mandate or a professional society to build a tracking system.
The arithmetic is simple. Fix your reviewer debt.
Martin Monperrus
April 2026