Manipulation and Peer Mechanisms: A Survey
Matthew Olckers, Toby Walsh
TL;DR
This survey analyzes manipulation in peer mechanisms where participants both compete for a prize and influence who wins. It synthesizes theoretical work on impartiality, axioms, and approximation with empirical evidence from field, lab, and observational studies, emphasizing three prevention approaches: impartial mechanisms, audits, and rewards. The work provides a comprehensive taxonomy of models (inputs, outputs, information) and mechanisms (partition, random dictatorship, threshold, and peer-prediction-inspired payments), distilling lessons on error robustness, nepotism, and the tradeoffs between flexibility and incentive compatibility. It concludes with open challenges (collusion, nepotism, punishments vs. prizes, ML-assisted design, and constraint-respecting mechanisms) and calls for future research to bridge theory and practice in real-world high-stakes settings.
Abstract
In peer mechanisms, the competitors for a prize also determine who wins. Each competitor may be asked to rank, grade, or nominate peers for the prize. Since the prize can be valuable, such as financial aid, course grades, or an award at a conference, competitors may be tempted to manipulate the mechanism. We survey approaches to prevent or discourage the manipulation of peer mechanisms. We conclude our survey by identifying several important research challenges.