The explosion of conference paper submissions in AI and related fields, has underscored the need to improve many aspects of the peer review process, especially the matching of papers and reviewers. Recent work argues that the key to improve this matching is to modify aspects of the \emph{bidding phase} itself, to ensure that the set of bids over papers is balanced, and in particular to avoid \emph{orphan papers}, i.e., those papers that receive no bids. In an attempt to understand and mitigate this problem, we have developed a flexible bidding platform to test adaptations to the bidding process. Using this platform, we performed a field experiment during the bidding phase of a medium-size international workshop that compared two bidding methods. We further examined via controlled experiments on Amazon Mechanical Turk various factors that affect bidding, in particular the order in which papers are presented \cite{cabanac2013capitalizing,fiez2020super}; and information on paper demand \cite{meir2021market}. Our results suggest that several simple adaptations, that can be added to any existing platform, may significantly reduce the skew in bids, thereby improving the allocation for both reviewers and conference organizers.

翻译：人工智能及相关领域会议论文投稿数量的激增，凸显了改进同行评审流程中诸多环节的需求，尤其是论文与审稿人的匹配问题。近期研究表明，改进匹配的关键在于对投标环节本身进行调整，以确保论文投标集的平衡性，并避免出现"孤儿论文"（即未收到任何投标的论文）。为理解并缓解该问题，我们开发了一个灵活的投标平台，用于测试投标流程的改进方案。借助该平台，我们在某中型国际研讨会的投标阶段开展了实地实验，比较了两种投标方法。进一步地，我们通过亚马逊土耳其机器人平台上的受控实验，考察了影响投标行为的多种因素——特别是论文呈现顺序、以及论文需求信息。结果表明，可在任意现有平台上增添的若干简单调整方案，能显著降低投标偏差，从而优化审稿人与会议组织者的资源分配。