AI

Abstract

Contests are widely used as a means for effort elicitation in settings ranging from government R&D contests to online crowdsourcing contests on platforms such as Kaggle, Innocentive, or TopCoder. Such rank-order mechanisms—— where agents' rewards depend only on the relative ranking of their submissions' qualities——are natural mechanisms for incentivizing effort when it is easier to obtain ordinal, rather than cardinal, information about agents' outputs, or where absolute measures of quality are unverifiable. An increasing number of online contests, however, rank entries according to some numerical evaluation of their absolute quality——for instance, the performance of an algorithm on a test dataset, or the performance of an intervention in a randomized trial. Can the contest designer incentivize higher effort by making the rewards in an ordinal rank-order mechanism contingent on such cardinal information?

We model and analyze cardinal contests, where a principal running a rank-order tournament has access to an absolute measure of the qualities of agents' submissions in addition to their relative rankings, and ask how modifying the rank-order tournament to incorporate cardinal information can improve incentives for effort. Our main result is that a simple threshold mechanism——a mechanism that awards the prize for a rank if and only if the absolute quality of the agent at that rank exceeds a certain threshold——is optimal amongst all mixed cardinal-ordinal mechanisms where the fraction of the j-th prize awarded to the j-th-ranked agent is any arbitrary non-decreasing function of her submission's quality. Further, the optimal threshold mechanism uses exactly the same threshold for each rank. We study what contest parameters determine the extent of the benefit from incorporating such cardinal information into an ordinal rank-order contest, and investigate the extent of improvement in equilibrium effort via numerical simulations.