Crowdsourcing with Endogenous Entry
  We investigate the design of mechanisms to incentivize high quality in crowdsourcing environments with strategic agents, when entry is an endogenous, strategic choice. Modeling endogenous entry in crowdsourcing is important because there is a nonzero cost to making a contribution of any quality which can be avoided by not participating, and indeed many sites based on crowdsourced content do not have adequate participation. We use a mechanism with monotone, rank-based, rewards in a model where agents strategically make participation and quality choices to capture a wide variety of crowdsourcing environments, ranging from conventional crowdsourcing contests to crowdsourced content as in online Q&A forums.   We first explicitly construct the unique mixed-strategy equilibrium for such monotone rank-order mechanisms, and use these participation probabilities and quality distribution to address the design of incentives for two kinds of rewards that arise in crowdsourcing. We first show that for attention rewards as in crowdsourced content, the entire equilibrium distribution improves when the rewards for every rank but the last are as high as possible. In particular, when producing the lowest quality content has low cost, the optimal mechanism displays all but the worst contribution. We next investigate settings where there is a total reward that can be arbitrarily distributed amongst all participants, as in crowdsourcing contests. Unlike with exogenous entry, here the expected number of participants can be increased by subsidizing entry, which could potentially improve the expected quality of the best contribution. However, we show that free entry is dominated by taxing entry- making all entrants pay a small fee, which is rebated to the winner along with whatever rewards were already assigned, can improve the quality of the best contribution over a winner-take-all contest with no taxes. 
Arpita Ghosh and Preston McAfee
cs.GT