Eliciting Joint Truthful Answers and Profiles From Strategic Workers in Mobile Crowdsourcing Systems

Abstract

Due to the openness of mobile crowdsourcing, workers may yield low-quality task answers. To alleviate this problem, substantial efforts have been devoted to eliciting truthful data from workers. On the other hand, to facilitate task assignment, workers are required to upload to the platform their profiles, such as locations and expertise. Therefore, task assignment outcomes and thus mobile crowdsourcing service accuracy are subject to the quality of worker's self-reported profiles. In this paper, we leverage incentive design to motivate workers to honestly reveal both task answers and their profiles. The challenge is to design one incentive payment for truth elicitation in two kinds of submissions. For this, we first derive the sufficient and necessary conditions for answer truthfulness and profile truthfulness separately. We then construct an incentive optimization problem that incorporates these conditions as constraints. Its optimal solution lists the payment to each worker that elicits answers and profiles jointly. Our proposed mechanism, with formally proved bounded approximation ratio, ensures that truth-telling is a Bayesian Nash equilibrium. We prototype the mechanism and conduct a series of experiments that involve 30 volunteers to validate the efficacy and efficiency of the proposed mechanism