论文标题
通过划分进行战略防止同行评估：根据评估者的专业知识的价格
Strategyproofing Peer Assessment via Partitioning: The Price in Terms of Evaluators' Expertise
论文作者
论文摘要
战略行为是需要某种形式的同伴评估的各种现实应用程序中的一个基本问题，例如家庭作业的同行评分，赠款提案审查，科学论文的会议同行评审以及对组织中员工的同行评估。由于个人的工作与他们正在评估的提交竞争，因此他们可能会提供不诚实的评估以增加自己提交的相对地位。这个问题通常是通过对个人进行分区并将其分配以评估来自不同子集的人的工作来解决的。尽管此方法确保了防策略性，但每个提交都可能需要不同类型的专业知识才能有效评估。在本文中，我们专注于找到评估人员的分配，以最大程度地提高分配的评估者的专业知识，但要受到战略范围的限制。我们分析了战略性型的价格：即，为了获得策略性抗辩性，所需的评估人员的专业知识所需的妥协数量。我们建立了几种多项式时间算法，用于策略性分配以及任务质量的保证。最后，我们评估了会议同行评审的数据集上的方法。
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.
