通过划分进行策略验证的同行评价：评价者专业知识的代价
Strategyproofing Peer Assessment via Partitioning: The Price in Terms of Evaluators' Expertise
摘要：
战略行为是一个基本的问题，在各种现实世界的应用，需要某种形式的同行评估，如同行评分的家庭作业，拨款建议审查，会议同行评审的科学论文，并同行评估的员工在组织。由于一个人自己的作品与他们正在评估的提交作品存在竞争，他们可能会提供不诚实的评估，以提高自己提交作品的相对地位。这个问题通常通过划分个体并指定他们仅评估来自不同子集的那些个体的工作来解决。虽然这种方法确保了战略性，但每一项提交可能需要不同类型的专业知识进行有效评估。在本文中，我们专注于找到一个分配的评估意见书，最大限度地提高分配的评估专家的专业知识受到战略的约束。我们分析了防策略性的代价：即为了获得防策略性而对所分配的评估者的专业知识做出的妥协。我们建立了几个多项式时间算法的策略证明分配以及分配质量保证。最后，我们评估的方法在一个数据集从会议同行评议。
英文摘要：
Strategic behavior is a fundamental problem in a variety of real-world applications that require some form of peer assessment, such as peer grading of homeworks, grant proposal review, conference peer review of scientific papers, and peer assessment of employees in organizations. Since an individual's own work is in competition with the submissions they are evaluating, they may provide dishonest evaluations to increase the relative standing of their own submission. This issue is typically addressed by partitioning the individuals and assigning them to evaluate the work of only those from different subsets. Although this method ensures strategyproofness, each submission may require a different type of expertise for effective evaluation. In this paper, we focus on finding an assignment of evaluators to submissions that maximizes assigned evaluators' expertise subject to the constraint of strategyproofness. We analyze the price of strategyproofness: that is, the amount of compromise on the assigned evaluators' expertise required in order to get strategyproofness. We establish several polynomial-time algorithms for strategyproof assignment along with assignment-quality guarantees. Finally, we evaluate the methods on a dataset from conference peer review.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
