Enhancing the Quality in Crowdsourcing E-Markets Through Team Formation Games
Bing Ai, Wanyuan Wang, Minghui Hua, Yichuan Jiang, Jiuchuan Jiang, Yifeng Zhou · IEEE Intelligent Systems · 2020
Crowdsourcing e-markets have been widely used to complete various complex tasks with the help of seamlessly integrating the ubiquitous intelligent systems and artificial intelligence. Most of traditional crowdsourcing e-markets focus primarily on individual incentives to motivate workers to participate. However, workers generally have heterogeneous values, which brings forth an undesirable outcome that workers with larger values monopolize a fixed total payment. This monopolization discourages the workers with smaller values from participating, thereby severely reducing the quality of individual-oriented crowdsourcing e-markets. Thus, this article proposes a team formation (TF) games-based mechanism in which workers are incentivized to form teams and obtain payments according to their contributions. Moreover, the existence of Nash equilibrium for a given TF game is rigorously analyzed. Finally, the experimental results in terms of both simulated and realistic datasets demonstrate that, as compared with state-of-the-art methods, our mechanism can achieve higher crowdsourcing e-markets quality.