A Misreport- and Collusion-Proof Crowdsourcing Mechanism without Quality Verification
Kun Li, Shengling Wang, Xiuzhen Cheng, Qin Hu

TL;DR
This paper introduces a new crowdsourcing mechanism that ensures truthful quality reporting and prevents collusion without needing to verify task quality, improving scalability and strategic planning.
Contribution
It presents a novel misreport- and collusion-proof mechanism that controls quality without verification, revealing counterintuitive insights about worker behavior and requestor utility.
Findings
High-quality workers may pretend to be low-quality.
Increasing task quality does not always benefit the requestor.
More workers do not necessarily increase the requestor's utility.
Abstract
Quality control plays a critical role in crowdsourcing. The state-of-the-art work is not suitable for large-scale crowdsourcing applications, since it is a long haul for the requestor to verify task quality or select professional workers in a one-by-one mode. In this paper, we propose a misreport- and collusion-proof crowdsourcing mechanism, guiding workers to truthfully report the quality of submitted tasks without collusion by designing a mechanism, so that workers have to act the way the requestor would like. In detail, the mechanism proposed by the requester makes no room for the workers to obtain profit through quality misreport and collusion, and thus, the quality can be controlled without any verification. Extensive simulation results verify the effectiveness of the proposed mechanism. Finally, the importance and originality of our work lie in that it reveals some interesting and…
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Taxonomy
TopicsMobile Crowdsensing and Crowdsourcing · Auction Theory and Applications · Multi-Agent Systems and Negotiation
