Promoting Higher Revenues for Both Crowdsourcer and Crowds in Crowdsourcing via Contest
Song Xu, Lei Liu, Lizhen Cui, Qingzhong Li, Zhongmin Yan · 2019
Crowdsourcing emerges as a promising means of solution generation, which creates tremendous value by leveraging the intelligence of crowds in the web services. With the rise of the business of crowdsourcing services, both crowdsourcers and workers expect to gain better Quality of Experience, as well as more profits. But there is a contradiction between the incentives of the crowdsourcer and the quality of result of the crowds. In order to balance this conflict, the paper develops a profit optimization model for all parties in crowdsourcing by employing Tullock Contests. The model consists of two parts. Firstly, optimized incentives are provided by crowdsourcer to encourage workers to achieve a better quality of result. Secondly, an optimal fee schedule is provided as guidance to workers. The visualization of the equilibria of benefits is helpful to reach a win-win situation for crowdsourcer and crowds, which in turn impacts the development of crowdsourcing services. In addition, we simulate the acquisition of Nash-equilibrium as a repeated crowdsourcing task. The effectiveness of our model is testified by the experimental results.