Incentive Mechanism Design for Heterogeneous Crowdsourcing Using All-Pay Contests

Incentive Mechanism Design for Heterogeneous Crowdsourcing Using All-Pay Contests
Title:
Incentive Mechanism Design for Heterogeneous Crowdsourcing Using All-Pay Contests
Other Titles:
IEEE Transactions on Mobile Computing
Keywords:
Publication Date:
15 September 2016
Citation:
T. Luo; S. Kanhere; S. Das; H. Tan, "Incentive Mechanism Design for Heterogeneous Crowdsourcing Using All-Pay Contests," in IEEE Transactions on Mobile Computing , vol.15, no.9, pp.2234-2246, September 2016 doi: 10.1109/TMC.2015.2485978
Abstract:
Many crowdsourcing scenarios are heterogeneous in the sense that, not only the workers’ types (e.g., abilities, costs) are different, but the beliefs (probabilistic knowledge) about their respective types are also different. In this paper, we design an incentive mechanism for such scenarios using an asymmetric all-pay contest (or auction) model. Our design objective is an optimal mechanism, i.e., one that maximizes the crowdsourcing revenue minus cost. To achieve this, we furnish the contest with a prize tuple which is an array of reward functions for each potential winner (worker). We prove and characterize the unique equilibrium of this contest, and solve the optimal prize tuple. In addition, this study discovers a counter-intuitive property, strategy autonomy (SA), which means that heterogeneous workers behave independently of one another as if they were in a homogeneous setting. In game-theoretical terms, it says that an asymmetric auction admits a symmetric equilibrium. Not only theoretically interesting, but SA also has important practical implications on mechanism complexity, energy efficiency, crowdsourcing revenue, and system scalability. By scrutinizing seven mechanisms, our extensive performance evaluation demonstrates the superior performance of our mechanism as well as offers insights into the SA property.
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PublisherCopyrights
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Description:
(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
ISSN:
1536-1233
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