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Fair and resilient Incentive Tree mechanisms

Published: 01 February 2016 Publication History

Abstract

We study Incentive Trees for motivating the participation of people in crowdsourcing or human tasking systems. In an Incentive Tree, each participant is rewarded for contributing to the system, as well as for soliciting new participants into the system, who then themselves contribute to it and/or themselves solicit new participants. An Incentive Tree mechanism is an algorithm that determines how much reward each individual participant receives based on all the participants' contributions, as well as the structure of the solicitation tree. The sum of rewards paid by the mechanism to all participants is linear in the sum of their total contribution. In this paper, we investigate the possibilities and limitations of Incentive Trees via an axiomatic approach by defining a set of desirable properties that an Incentive Tree mechanism should satisfy. We give a mutual incompatibility result showing that there is no Incentive Tree mechanism that simultaneously achieves all the properties. We then present two novel families of Incentive Tree mechanisms. The first family of mechanisms achieves all desirable properties, except that it fails to protect against a certain strong form of multi-identity attack; the second set of mechanisms achieves all properties, including the strong multi-identity protection, but fails to give participants the opportunity to achieve unbounded reward. Given the above impossibility result, these two mechanisms are effectively the best we can hope for. Finally, our model and results generalize recent studies on multi-level marketing mechanisms.

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  • (2023)Collusion-proof and sybil-proof reward mechanisms for query incentive networksProceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v37i5.25730(5892-5899)Online publication date: 7-Feb-2023
  • (2021)Crowd-Sourcing Information Dissemination Based on Spatial Behavior and Social NetworksMobile Information Systems10.1155/2021/66527402021Online publication date: 1-Jan-2021
  • (2021)Task Execution Quality Maximization for Mobile Crowdsourcing in Geo-Social NetworksProceedings of the ACM on Human-Computer Interaction10.1145/34760535:CSCW2(1-29)Online publication date: 18-Oct-2021
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Information & Contributors

Information

Published In

cover image Distributed Computing
Distributed Computing  Volume 29, Issue 1
February 2016
73 pages

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 February 2016

Author Tags

  1. Crowdsourcing
  2. Incentive Trees
  3. Multi-level marketing
  4. Reward mechanisms

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View all
  • (2023)Collusion-proof and sybil-proof reward mechanisms for query incentive networksProceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v37i5.25730(5892-5899)Online publication date: 7-Feb-2023
  • (2021)Crowd-Sourcing Information Dissemination Based on Spatial Behavior and Social NetworksMobile Information Systems10.1155/2021/66527402021Online publication date: 1-Jan-2021
  • (2021)Task Execution Quality Maximization for Mobile Crowdsourcing in Geo-Social NetworksProceedings of the ACM on Human-Computer Interaction10.1145/34760535:CSCW2(1-29)Online publication date: 18-Oct-2021
  • (2020)A Paid Message Forwarding Scheme Based on Social NetworkInformation Security and Cryptology10.1007/978-3-030-71852-7_12(177-192)Online publication date: 11-Dec-2020
  • (2019)Designing Incentive Mechanisms for Mobile Crowdsensing with IntermediariesWireless Communications & Mobile Computing10.1155/2019/86035262019Online publication date: 1-Jan-2019

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