A payment scheme in crowdsourcing
Xiao Chen, Kaiqi Xiong · 2017
Crowdsourcing coordinates a large group of workers online to do self-contained small tasks that are published by job requesters on a crowdsourcing platform. Many papers propose incentive strategies to motivate workers to participate in crowdsourcing. In this paper, we shift the focus from the workers to the job requesters by addressing two of their issues: how to design a good payment scheme to maximize profit and how to select qualified workers to do the job. We use a widely-adopted payment formula consisting of a base salary and extra bonus. We first formulate the problem as an optimization problem and then provide a general solution in which we show that the pay rate can be the same to all the workers. Next we instantiate the solution with a concrete example to derive more concrete results and propose a worker selection algorithm WS. In WS, we not only consider workers' workload demands but also their past working performance to guarantee crowdsourcing quality. Simulation results show that a job requester can pay much less to get the job done in a crowdsourcing environment and our worker selection algorithm is efficient in that it only searches a tiny space to find the solution to the optimization problem. Our effort here provides an evidence to support the benefits of using crowdsourcing in our daily lives.