  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.
