Mitigating Skewed Bidding for Conference Paper Assignment

Inbal Rozencweig, Reshef Meir, Nicholas Mattei, Ofra Amir

Research output: Contribution to journalConference articlepeer-review


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 bidding phase itself, to ensure that the set of bids over papers is balanced, and in particular to avoid 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 [11, 17]; and information on paper demand [33]. 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.

Original languageEnglish
Pages (from-to)573-581
Number of pages9
JournalProceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
StatePublished - 2023
Event22nd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2023 - London, United Kingdom
Duration: 29 May 20232 Jun 2023


  • Allocation
  • Bidding
  • Peer Review

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering


Dive into the research topics of 'Mitigating Skewed Bidding for Conference Paper Assignment'. Together they form a unique fingerprint.

Cite this