Reimagining Peer Review Process Through Multi-Agent Mechanism Design

TL;DR

This paper proposes a novel multi-agent reinforcement learning approach to redesign the peer review process in software engineering, aiming to address systemic issues like misaligned incentives and reviewer fatigue.

Contribution

It introduces a multi-agent system model and three innovative interventions to improve peer review incentives, assignment, and verification processes.

Findings

Conceptual framework for a multi-agent peer review system

Design of incentive-compatible protocols for review process

Initial metrics and threat models for pilot testing

Abstract

The software engineering research community faces a systemic crisis: peer review is failing under growing submissions, misaligned incentives, and reviewer fatigue. Community surveys reveal that researchers perceive the process as "broken." This position paper argues that these dysfunctions are mechanism design failures amenable to computational solutions. We propose modeling the research community as a stochastic multi-agent system and applying multi-agent reinforcement learning to design incentive-compatible protocols. We outline three interventions: a credit-based submission economy, MARL-optimized reviewer assignment, and hybrid verification of review consistency. We present threat models, equity considerations, and phased pilot metrics. This vision charts a research agenda toward sustainable peer review.