A Two-layer Game-based Incentive Mechanism for Decentralized Crowdsourcing

Abstract

Decentralized crowdsourcing removes the dependence on a trusted centralized platform based on blockchain and ensures system stability through the consensus of miners. The lack of centralized supervision requires all kinds of system nodes to voluntarily participate while their behaviors are profitdriven and unpredictable, thus introducing challenges to system performance. Moreover, the nodes in a decentralized crowdsourcing system inherently observe little information about the system status; therefore, it is difficult for them to discover and adopt theoretically optimal strategies. Current literature still lacks an effective mechanism to motivate the participation of all types of nodes. To this end, this paper employs a two-layer game model to simulate the interactions in the decentralized crowdsourcing system for investigating the participation willingness of different system nodes. Specifically, we apply a Stackelberg game to model the interactions of a crowdsourcing requester and other nodes, where the requester decides its reward policy and the others respond by selecting their roles to play. In addition, the interaction of the bounded rational other nodes is further represented as an evolutionary game. After analyzing the game model, we further design an incentive mechanism to maximize the requester utility while motivating other nodes to actively participate in the crowdsourcing. Through experimental simulations, we verify the effectiveness of the proposed incentive mechanism.