Incentive Mechanism Design for Crowdsourcing: An All-Pay Auction Approach
Abstract
Crowdsourcing can be modeled as a principal-agent problem in which the principal (crowdsourcer) desires to solicit a maximal contribution from a group of agents (participants) while agents are only motivated to act according to their own respective advantages. To reconcile this tension, we propose an all-pay auction approach to incentivize agents to act in the principal's interest, i.e., maximizing profit, while allowing agents to reap strictly positive utility. Our rationale for advocating all-pay auctions is based on two merits that we identify, namely all-pay auctions (i) compress the common, two-stage "bid-contribute" crowdsourcing process into a single "bid-cum-contribute" stage, and (ii) eliminate the risk of task nonfulfillment. In our proposed approach, we enhance all-pay auctions with two additional features: an adaptive prize and a general crowdsourcing environment. The prize or reward adapts itself as per a function of the unknown winning agent's contribution, and the environment or setting generally accommodates incomplete and asymmetric information, risk-averse (and risk-neutral) agents, and a stochastic (and deterministic) population. We analytically derive this all-pay auction-based mechanism and extensively evaluate it in comparison to classic and optimized mechanisms. The results demonstrate that our proposed approach remarkably outperforms its counterparts in terms of the principal's profit, agent's utility, and social welfare.
Recommended Citation
T. T. Luo et al., "Incentive Mechanism Design for Crowdsourcing: An All-Pay Auction Approach," ACM Transactions on Intelligent Systems and Technology, vol. 7, no. 3, Association for Computing Machinery (ACM), Feb 2016.
The definitive version is available at https://doi.org/10.1145/2837029
Department(s)
Computer Science
Research Center/Lab(s)
Intelligent Systems Center
Keywords and Phrases
Commerce; Profitability; Stochastic systems; Bayesian Nash equilibria; Incomplete information; Mobile crowd sensing; Participatory Sensing; Risk aversion; Shading effect; Crowdsourcing; Bayesian Nash equilibrium
International Standard Serial Number (ISSN)
2157-6904; 2157-6912
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2016 Association for Computing Machinery (ACM), All rights reserved.
Publication Date
01 Feb 2016
Comments
This work was supported in part by A*STAR Singapore under SERC grant 1224104046, and in part by the U.S. National Science Foundation under grants CNS-1404677, IIS-1404673, CNS-1545037, and CNS1545050.