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Ranking is fundamental to many areas, such as search engine optimization,human feedback for language models, as well as peer grading. Crowdsourcing,which is often used for these tasks, requires proper incentivization to ensureaccurate inputs. In this work, we draw on the field of \emph{contract theory}from Economics to propose a novel mechanism that enables a \emph{principal} toaccurately rank a set of items by incentivizing agents to provide pairwisecomparisons of the items. Our mechanism implements these incentives byverifying a subset of each agent's comparisons, a task we assume to be costly.The agent is compensated (for example, monetarily or with class credit) basedon the accuracy of these comparisons. Our mechanism achieves the followingguarantees: (1) it only requires the principal to verify $O(\log s)$comparisons, where $s$ is the total number of agents, and (2) it provablyachieves higher total utility for the principal compared to ranking the itemsherself with no crowdsourcing.

Algorithmic Contract Design for Crowdsourced Ranking