Strategic Information Revelation Mechanism in Crowdsourcing Applications Without Verification

We study a crowdsourcing problem, where a platform aims to incentivize distributed workers to provide high-quality and truthful solutions that are not verifiable. We focus on a largely overlooked yet pratically important asymmetric information scenario, where the platform knows more information regarding workers' average solution accuracy and can strategically reveal such information to workers. Workers will utilize the announced information to determine the likelihood of obtaining a reward. We first study the case where the platform and workers share the same prior regarding the average worker accuracy (but only the platform observes the realized value). We consider two types of workers: (1) naive workers who fully trust the platform's announcement, and (2) strategic workers who update prior belief based on the announcement. For naive workers, we show that the platform should always announce a high average accuracy to maximize its payoff. However, this is not always optimal when facing strategic workers, and the platform may benefit from announcing an average accuracy lower than the actual value. We further study the more challenging non-common prior case, and show the counter-intuitive result that when the platform is uninformed of the workers' prior, both the platform payoff and the social welfare may decrease as the high accuracy workers' solutions become more accurate.