Quantum Analysis on Task Allocation and Quality Control for Crowdsourcing With Homogeneous Workers

Abstract

Crowdsourcing has been emerging as a valid problem-solving model that harnesses a large group of contributors to solve a complicated task. However, existing crowdsourcing platforms or systems could suffer from task allocation and quality control problems. In this article, we first prove that there exist two dilemmas while tackling the above issues by using a game-theoretic approach. To overcome this challenge, we are focusing on exploiting quantum crowdsourcing schemes in which the welfare of requestor or worker can be maximized since quantum players share the extended strategy space, and the introduction of entanglement offers a new method of depicting fine-grained relations between players. Specifically, we propose a quantum game model for quota-oriented crowdsourcing game to address dilemmas in task allocation. The result indicates the dilemma based on classical strategy will disappear with the increment of entanglement degree. While in the quality-oriented crowdsourcing game, we adopt a density matrix approach to calculate the optimal payoffs of both sides, which demonstrates the superiority of our quantum strategy. Moreover, our quantum scheme is generic since it is compatible with the schemes from a classical perspective. Hence, our noteworthy quantum crowdsourcing schemes offer a promising alternative route for tackling dilemmas in crowdsourcing scenarios.