用智能体机制设计解决学术评审困局，让投稿与评审更公平高效。
Reimagining Peer Review Process Through Multi-Agent Mechanism Design
- 将科研社区建模为多智能体系统，用强化学习优化评审机制。
- 提出信用积分投稿制、智能分配评审人和审查一致性验证三策略。
- 适合关注学术生态改革、机制设计的科研管理者与研究者。
软件工程研究领域正面临系统性危机：投稿量激增、激励错位与审稿人疲劳导致同行评审失效。调查显示，研究人员普遍认为该流程已“失灵”。本文主张，这些弊端本质上是机制设计失败，可通过计算方法解决。我们提出将科研社区视为随机多智能体系统，运用多智能体强化学习（MARL）设计激励相容协议。具体提出三项干预措施：基于信用的投稿经济体系、由MARL优化的审稿人分配机制，以及混合方式验证评审一致性。文中还分析了威胁模型、公平性考量及分阶段试点评估指标。这一愿景勾勒出可持续同行评审的研究路线图。
原文摘要 · Abstract (English)
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.
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