TY - GEN
T1 - Who Reviews The Reviewers? A Multi-Level Jury Problem
AU - Abramowitz, Ben
AU - Lev, Omer
AU - Mattei, Nicholas
N1 - Publisher Copyright:
© 2025 International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org).
PY - 2025/1/1
Y1 - 2025/1/1
N2 - We consider the problem of determining a binary ground truth using advice from a group of independent reviewers (experts) who express their guess about a ground truth correctly with some independent probability (competence) pi. In this setting, when all reviewers are competent with p ≥ 0.5, the Condorcet Jury Theorem tells us that adding more reviewers increases the overall accuracy, and if all pi's are known, then there exists an optimal weighting of the reviewers. However, in practical settings, reviewers may be noisy or incompetent, i.e., pi ≤ 0.5, and the number of experts may be small, so the asymptotic Condorcet Jury Theorem is not practically relevant. In such cases we explore appointing one or more chairs (judges) who determine the weight of each reviewer for aggregation, creating multiple levels. However, these chairs may be unable to correctly identify the competence of the reviewers they oversee, and therefore unable to compute the optimal weighting. We give conditions on when a set of chairs is able to weight the reviewers optimally, and depending on the competence distribution of the agents, give results about when it is better to have more chairs or more reviewers. Through simulations we show that in some cases it is better to have more chairs, but in many cases it is better to have more reviewers.
AB - We consider the problem of determining a binary ground truth using advice from a group of independent reviewers (experts) who express their guess about a ground truth correctly with some independent probability (competence) pi. In this setting, when all reviewers are competent with p ≥ 0.5, the Condorcet Jury Theorem tells us that adding more reviewers increases the overall accuracy, and if all pi's are known, then there exists an optimal weighting of the reviewers. However, in practical settings, reviewers may be noisy or incompetent, i.e., pi ≤ 0.5, and the number of experts may be small, so the asymptotic Condorcet Jury Theorem is not practically relevant. In such cases we explore appointing one or more chairs (judges) who determine the weight of each reviewer for aggregation, creating multiple levels. However, these chairs may be unable to correctly identify the competence of the reviewers they oversee, and therefore unable to compute the optimal weighting. We give conditions on when a set of chairs is able to weight the reviewers optimally, and depending on the competence distribution of the agents, give results about when it is better to have more chairs or more reviewers. Through simulations we show that in some cases it is better to have more chairs, but in many cases it is better to have more reviewers.
KW - Jury Theorem
KW - Peer Review
KW - Peer Selection
UR - https://www.scopus.com/pages/publications/105009764111
M3 - Conference contribution
AN - SCOPUS:105009764111
T3 - Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
SP - 14
EP - 22
BT - Proceedings of the 24th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2025
A2 - Vorobeychik, Yevgeniy
A2 - Das, Sanmay
A2 - Nowe, Ann
PB - International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
T2 - 24th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2025
Y2 - 19 May 2025 through 23 May 2025
ER -