A Crowdsourcing Task Allocation Mechanism for Hybrid Worker Context Based on Skill Level Updating

Abstract

Existing crowdsourcing research has traditionally focused on high-skilled workers during the task allocation phase while neglected the potential value and skill enhancement opportunities of workers who are with lower skill levels. Consequently, as the number of completed tasks increases, skill disparities among workers have worsened. This can be primarily attributed to the platform’s heavy reliance on high-skilled workers, which makes it difficult for lower-skilled ordinary workers to meet task requirements and limits their chances for skill development. As a result, when a large volume of tasks awaits allocation, the limited number of high-skilled workers falls short of meeting the task demand, resulting in prolonged task completion periods and reduced task allocation success rates. In reality, the majority of tasks on crowdsourcing platforms contain straightforward components that can be handled by ordinary workers. Additionally, collaborative efforts among multiple workers can enhance task execution efficiency and reduce task durations. Building on these practical observations, this paper proposes a task allocation model and simulates the improvement of worker skill levels. In situations where teams take a longer time to complete tasks, the model allows ordinary workers to join the team, thereby assisting professional workers in enhancing work efficiency. Experimental results demonstrate that this model can alleviate worker skill disparities, diminish task durations, and facilitate the development of crowdsourcing platforms.