Strategyproof Peer Selection using Randomization, Partitioning, and Apportionment

TL;DR

This paper introduces a strategyproof peer selection mechanism that uses randomization and partitioning, ensuring truthful reporting and effective resource allocation in various decision-making contexts.

Contribution

The paper presents a novel strategyproof mechanism for peer selection that incorporates a randomized rounding technique for proportional apportionment, validated through simulations.

Findings

Mechanism is strategyproof, preventing agents from benefiting by misreporting.

Simulation results show the mechanism outperforms existing methods.

The randomized rounding technique effectively solves the apportionment problem.

Abstract

Peer reviews, evaluations, and selections are a fundamental aspect of modern science. Funding bodies the world over employ experts to review and select the best proposals from those submitted for funding. The problem of peer selection, however, is much more general: a professional society may want to give a subset of its members awards based on the opinions of all members; an instructor for a Massive Open Online Course (MOOC) or an online course may want to crowdsource grading; or a marketing company may select ideas from group brainstorming sessions based on peer evaluation. We make three fundamental contributions to the study of peer selection, a specific type of group decision-making problem, studied in computer science, economics, and political science. First, we propose a novel mechanism that is strategyproof, i.e., agents cannot benefit by reporting insincere valuations. Second, we demonstrate the effectiveness of our mechanism by a comprehensive simulation-based comparison with a suite of mechanisms found in the literature. Finally, our mechanism employs a randomized rounding technique that is of independent interest, as it solves the apportionment problem that arises in various settings where discrete resources such as parliamentary representation slots need to be divided proportionally.