Stackelberg Game Based Optimal Workload Allocation And Pricing Mechanism In Crowdsourcing

Crowdsourcing has been an inspiration of effectively integrating the power of the crowd. Lots of current contributions on crowdsourcing focus only on the competition between the worker and the requester on resource allocation. However, most of these works lacked attention to the cooperation between the worker and requester. In this paper, we take into consideration the competition-cooperation coexisting trait of the relationship between the worker and the requester in market resource allocation (workload assignment and pricing), and adopt the Stackelberg game model to design a two-stage optimal win-win crowdsourcing workload assignment and pricing mechanism. By sharing the knowledge of strategies and information of each other, strategies of both sides are well-planned, and is free of internecine competitions. We first present the Stackelberg-based general crowdsourcing framework and problem formulation, then implement them with specific structures. Next we present the optimal strategies for both parties that maximize the utility of each, and the proof of obtaining them. The existence of Stackelberg Equilibrium will also be proved, and will be achieved by the optimal strategies we just solved. At last we vary some key parameters to evaluate their impact on the total output, and make convincing comparisons to prove our result optimal.