This paper designs a simple, efficient and truthful mechanism to to elicit self-evaluations about items jointly owned by owners. A key application of this mechanism is to improve the peer review of large scientific conferences where a paper often has multiple authors and many authors have multiple papers. Our mechanism is designed to generate an entirely new source of review data truthfully elicited from paper owners, and can be used to augment the traditional approach of eliciting review data only from peer reviewers. Our approach starts by partitioning all submissions of a conference into disjoint blocks, each of which shares a common set of co-authors. We then elicit the ranking of the submissions from each author and employ isotonic regression to produce adjusted review scores that align with both the reported ranking and the raw review scores. Under certain conditions, truth-telling by all authors is a Nash equilibrium for any valid partition of the overlapping ownership sets. We prove that to ensure truthfulness for such isotonic regression based mechanisms, partitioning the authors into blocks and eliciting only ranking information independently from each block is necessary. This leave the optimization of block partition as the only room for maximizing the estimation efficiency of our mechanism, which is a computationally intractable optimization problem in general. Fortunately, we develop a nearly linear-time greedy algorithm that provably finds a performant partition with appealing robust approximation guarantees. Extensive experiments on both synthetic data and real-world conference review data demonstrate the effectiveness of this owner-assisted calibration mechanism.

翻译：本文设计了一种简单、高效且诚实的机制，用于诱使共同拥有某物品的所有者提供关于该物品的自我评价。该机制的核心应用在于改进大型科学会议的同行评审流程——此类会议中论文通常有多位作者，且许多作者同时参与多篇论文。我们的机制旨在从论文所有者处诚实获取全新的评审数据源，可补充传统仅依赖同行评审员的评审数据采集方法。具体而言，我们将会议所有投稿划分为若干不相交的区块，每个区块共享相同的共同作者集合。随后，我们要求每位作者对其所在区块内的论文进行排序，并采用保序回归生成与报告排序及原始评审分数相一致的调整后评审分数。在特定条件下，对于任意有效的重叠所有权集合划分，所有作者的真实陈述构成纳什均衡。我们证明，为确保此类基于保序回归的机制的诚实性，必须将作者划分为区块并独立获取每个区块的排序信息。这使得区块划分优化成为提升机制估计效率的唯一可设计空间——该优化问题在一般情况下计算复杂度极高。幸运的是，我们提出了一种近线性时间复杂度的贪心算法，该算法可证明找到具有鲁棒近似保证的有效划分。基于合成数据及真实会议评审数据的广泛实验验证了这种所有者辅助校准机制的有效性。