Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
10-2014
Abstract
Incentive is key to the success of crowd sourcing which heavily depends on the level of user participation. This paper designs an incentive mechanism to motivate a heterogeneous crowd of users to actively participate in crowd sourcing campaigns. We cast the problem in a new, asymmetric all-pay contest model with incomplete information, where an arbitrary n of users exert irrevocable effort to compete for a prize tuple. The prize tuple is an array of prize functions as opposed to a single constant prize typically used by conventional contests. We design an optimal contest that (a) induces the maximum profit -- total user effort minus the prize payout -- for the crowdsourcer, and (b) ensures users to strictly have incentive to participate. In stark contrast to intuition and prior related work, our mechanism induces an equilibrium in which heterogeneous users behave independently of one another as if they were in a homogeneous setting. This newly discovered property, which we coin as strategy autonomy (SA), is of practical significance: it (a) reduces computational and storage complexity by n-fold for each user, (b) increases the crowdsourcer's revenue by counteracting an effort reservation effect existing in asymmetric contests, and (c) neutralizes the (almost universal) law of diminishing marginal returns (DMR). Through an extensive numerical case study, we demonstrate and scrutinize the superior profitability of our mechanism, as well as draw insights into the SA property.
Keywords
Incentive mechanism, all-pay auction, asymmetric contest, network economics, participatory sensing, strategy autonomy
Discipline
Computer Sciences
Research Areas
Software and Cyber-Physical Systems
Publication
2014 IEEE 11th International Conference on Mobile Ad Hoc and Sensor Systems (MASS),
First Page
136
Last Page
144
ISBN
9781479960354
Identifier
10.1109/MASS.2014.66
Publisher
IEEE
City or Country
Philadelphia, PA
Citation
T. Luo; S. Kanhere; S. Das; and TAN, Hwee-Pink.
Optimal Prizes for All-Pay Contests in Heterogeneous Crowdsourcing. (2014). 2014 IEEE 11th International Conference on Mobile Ad Hoc and Sensor Systems (MASS),. 136-144.
Available at: https://ink.library.smu.edu.sg/sis_research/2941
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