Optimal Prizes for All-Pay Contests in Heterogeneous Crowdsourcing

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 conte...

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Main Authors: T. Luo, S. Kanhere, S. Das, TAN, Hwee-Pink
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Language:English
Published: Institutional Knowledge at Singapore Management University 2014
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Online Access:https://ink.library.smu.edu.sg/sis_research/2941
https://ink.library.smu.edu.sg/context/sis_research/article/3941/viewcontent/mass2014.pdf
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spelling sg-smu-ink.sis_research-39412016-01-28T03:40:55Z Optimal Prizes for All-Pay Contests in Heterogeneous Crowdsourcing T. Luo, S. Kanhere, S. Das, TAN, Hwee-Pink 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. 2014-10-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/2941 info:doi/10.1109/MASS.2014.66 https://ink.library.smu.edu.sg/context/sis_research/article/3941/viewcontent/mass2014.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Incentive mechanism all-pay auction asymmetric contest network economics participatory sensing strategy autonomy Computer Sciences
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Incentive mechanism
all-pay auction
asymmetric contest
network economics
participatory sensing
strategy autonomy
Computer Sciences
spellingShingle Incentive mechanism
all-pay auction
asymmetric contest
network economics
participatory sensing
strategy autonomy
Computer Sciences
T. Luo,
S. Kanhere,
S. Das,
TAN, Hwee-Pink
Optimal Prizes for All-Pay Contests in Heterogeneous Crowdsourcing
description 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.
format text
author T. Luo,
S. Kanhere,
S. Das,
TAN, Hwee-Pink
author_facet T. Luo,
S. Kanhere,
S. Das,
TAN, Hwee-Pink
author_sort T. Luo,
title Optimal Prizes for All-Pay Contests in Heterogeneous Crowdsourcing
title_short Optimal Prizes for All-Pay Contests in Heterogeneous Crowdsourcing
title_full Optimal Prizes for All-Pay Contests in Heterogeneous Crowdsourcing
title_fullStr Optimal Prizes for All-Pay Contests in Heterogeneous Crowdsourcing
title_full_unstemmed Optimal Prizes for All-Pay Contests in Heterogeneous Crowdsourcing
title_sort optimal prizes for all-pay contests in heterogeneous crowdsourcing
publisher Institutional Knowledge at Singapore Management University
publishDate 2014
url https://ink.library.smu.edu.sg/sis_research/2941
https://ink.library.smu.edu.sg/context/sis_research/article/3941/viewcontent/mass2014.pdf
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