Abstract-While microtask crowdsourcing provides a new way to solve large volumes of small tasks at a much lower price compared with traditional inhouse solutions, it suffers from quality problems due to the lack of incentives. On the other hand, providing incentives for microtask crowdsourcing is challenging since verifying the quality of submitted solutions is so expensive that it will negate the advantage of microtask crowdsourcing. We study cost-effective incentive mechanisms for microtask crowdsourcing in this paper. In particular, we consider a model with strategic workers, where the primary objective of a worker is to maximize his own utility. Based on this model, we first analyze two basic mechanisms and show their limitations in collecting high-quality solutions with low cost. Then, we propose a cost-effective mechanism that employs quality-aware worker training as a tool to stimulate workers to provide high-quality solutions. We prove theoretically that the proposed mechanism can be designed to obtain high-quality solutions from workers and ensure the budget constraint of the requester at the same time. Beyond its theoretical guarantees, we further demonstrate the effectiveness of our proposed mechanisms through a set of behavioral experiments.
The authors in REF consider the hardness in the verification of submitted quality and propose a cost-effective mechanism with guaranteed symmetric Nash Equilibrium.
On Cost-Effective Incentive Mechanisms in Microtask Crowdsourcing
IEEE Transactions on Computational Intelligence and AI in Games
IEEE Transactions on Computational Intelligence and AI in Games
Computer Science Mathematics