Extortion and Cooperation in Rating Protocol Design for Competitive Crowdsourcing

Although crowdsourcing has emerged as a paradigm for leveraging human intelligence and activity to solve a wide range of tasks, strategic workers will find enticement in their self-interest to free-ride and attack in a crowdsourcing contest dilemma game. Existing incentive mechanisms are not effecti...

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Bibliographic Details
Published in:IEEE transactions on computational social systems 2021-02, Vol.8 (1), p.246-259
Main Authors: Lu, Jianfeng, Xin, Yun, Zhang, Zhao, Tang, Shaojie, Tang, Changbing, Wan, Shaohua
Format: Article
Language:English
Subjects:
Algorithms
Competitive crowdsourcing
Cooperation
Crowdsourcing
Design parameters
differential punishment
Game theory
Games
incentive mechanism
Monitoring
Optimization
Pricing
Protocols
rating protocol
Task analysis
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Summary:Although crowdsourcing has emerged as a paradigm for leveraging human intelligence and activity to solve a wide range of tasks, strategic workers will find enticement in their self-interest to free-ride and attack in a crowdsourcing contest dilemma game. Existing incentive mechanisms are not effective to avoid socially undesirable equilibrium due to the following features of competitive crowdsourcing: in the presence of imperfect monitoring, heterogeneous workers with competing interest tend to beat their opponents for larger self-profit, and the fact that they can freely and frequently change their opponents makes the situation much more complicated. Taking these features into consideration, this article proposes a mechanism design problem to enforce cooperation and extort selfish works simultaneously, with the objective of maximizing the requester's utility. To solve the problem, we integrate binary ratings with differential pricing to develop a novel rating protocol. By establishing a mathematical model for the problem and quantifying necessary and sufficient conditions for a sustainable social norm, we provide design guidelines for optimal rating protocols and design a low-complexity algorithm to select optimal design parameters. Finally, extensive evaluation results demonstrate the performance of our proposed rating protocol and reveal how intrinsic parameters impact on design parameters.
ISSN:2329-924X
2329-924X
2373-7476
DOI:10.1109/TCSS.2020.2964284