Analyzing the Effects of Two-Stage Peer Evaluation

2026-05-22

AI Summary

This study systematically investigates the fairness and effectiveness of strategyproof reviewer assignment algorithms under a two-stage peer review mechanism, specifically tailored to academic conference settings. Leveraging concepts from mechanism design in game theory, theoretical analysis, and multivariate Monte Carlo simulations, the paper evaluates the Partition and ExactDollarPartition mechanisms across varying levels of review noise, acceptance rates, reviewer loads, and inter-reviewer correlations. The findings reveal that low-noise environments disproportionately benefit marginal submissions, whereas high-noise conditions favor top-ranked ones, with mechanism performance exhibiting high sensitivity to parameter choices. This work provides the first quantitative assessment of the collective impact of strategyproof mechanisms in a two-stage review framework, offering both theoretical foundations and practical cautions for designing conference peer review systems.

Abstract

Peer-evaluation and selection systems are used when sets of agents evaluate each other in order to select the best $k$ among them. These are commonly used in real-world settings, including academic conferences where those reviewing papers are often the set of submitters. Conferences have attempted to better allocate their reviewing resources by moving to a two-stage mechanism, in which some papers are eliminated after a first stage of review and remaining papers receive additional reviewers. We investigate how two major strategyproof peer selection mechanisms, Partition and ExactDollarPartition, perform when adapted to a two-stage system, in order to try and understand the effect of the two-stage mechanism on which agents get selected. We also examine how the various parameters of the two-stage mechanism influence the outcome. We provide a theoretical basis by showing how a particular setting is influenced by the two stages. However, solving for the general case seems implausible at the moment, and we use extensive simulations of different scenarios and settings to observe which agents benefit and which are harmed by adopting two-stage mechanisms (and we vary this mechanisms parameters as well). We show that the two-stage mechanism's advantage depends the noisiness of reviewer beliefs. Borderline agents benefit most in a low noise environment, while high rank agents benefit more in noisy environments. We show that the effectiveness of these mechanisms is highly dependent on the number of chosen agents, the number of reviews requested from agents, and reviewers' correlation, indicating that organizers need to exercise caution when selecting these parameters for a reviewing process.

Problem

• peer evaluation
• two-stage mechanism
• strategyproof selection
• reviewer noise
• selection bias

Innovation

• two-stage peer evaluation
• strategyproof mechanisms
• Partition
• ExactDollarPartition
• reviewer noise