# A SUPER* Algorithm to Optimize Paper Bidding in Peer Review

arXiv: https://arxiv.org/abs/2007.07079 · HTML: https://mlsanity.com/paper/2007.07079/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2020.

## Built on by (most cited first, 10 of 10)

- [What Can Natural Language Processing Do for Peer Review?](https://mlsanity.com/paper/2405.06563/) (2024)
- [A Gold Standard Dataset for the Reviewer Assignment Problem](https://mlsanity.com/paper/2303.16750/) (2023)
- [A Dataset on Malicious Paper Bidding in Peer Review](https://mlsanity.com/paper/2207.02303/) (2022)
- [Near-Optimal Reviewer Splitting in Two-Phase Paper Reviewing and Conference Experiment Design](https://mlsanity.com/paper/2108.06371/) (2021)
- [Strategyproofing Peer Assessment via Partitioning: The Price in Terms of Evaluators' Expertise](https://mlsanity.com/paper/2201.10631/) (2022)
- [Assisting Human Decisions in Document Matching](https://mlsanity.com/paper/2302.08450/) (2023)
- [Auctions and Peer Prediction for Academic Peer Review](https://mlsanity.com/paper/2109.00923/) (2021)
- [RATE: Reviewer Profiling and Annotation-free Training for Expertise Ranking in Peer Review Systems](https://mlsanity.com/paper/2601.19637/) (2026)
- [CABAL: Multi-Agent Simulacra for Tracing the Effects of Collusive Bidding in Peer Review](https://mlsanity.com/paper/2609.05227/) (2026)
- [Which Coauthor Should I Nominate in My 99 ICLR Submissions? A Mathematical Analysis of the ICLR 2026 Reciprocal Reviewer Nomination Policy](https://mlsanity.com/paper/2512.17950/) (2025)

## Abstract (arXiv)

A number of applications involve sequential arrival of users, and require showing each user an ordering of items. A prime example (which forms the focus of this paper) is the bidding process in conference peer review where reviewers enter the system sequentially, each reviewer needs to be shown the list of submitted papers, and the reviewer then "bids" to review some papers. The order of the papers shown has a significant impact on the bids due to primacy effects. In deciding on the ordering of papers to show, there are two competing goals: (i) obtaining sufficiently many bids for each paper, and (ii) satisfying reviewers by showing them relevant items. In this paper, we begin by developing a framework to study this problem in a principled manner. We present an algorithm called SUPER*, inspired by the A* algorithm, for this goal. Theoretically, we show a local optimality guarantee of our algorithm and prove that popular baselines are considerably suboptimal. Moreover, under a community model for the similarities, we prove that SUPER* is near-optimal whereas the popular baselines are considerably suboptimal. In experiments on real data from ICLR 2018 and synthetic data, we find that SUPER* considerably outperforms baselines deployed in existing systems, consistently reducing the number of papers with fewer than requisite bids by 50-75% or more, and is also robust to various real world complexities.