Abstract

This paper studies incentive mechanisms for crowd-powered systems, including applications such as collection of personal data for big-data analytics and crowdsourcing. In big-data analytics using personal data, an individual may control the quality of reported data via a privacy-preserving mechanism that randomizes the answer. In crowdsourcing, the quality of the reported answer depends on the amount of effort spent by a worker or a team. In these applications, incentive mechanisms are critical for eliciting data/answers with target quality. This paper focuses the following two fundamental questions: what is the minimum payment required to incentivize an individual to submit a data/answer with quality level ∈? and what incentive mechanisms can achieve the minimum payment? Let ∈i denote the quality of the data/answer reported by individual i: In this paper, we first derive a lower bound on the minimum amount of payment required for guaranteeing quality level ∈i: Inspired by the lower bound, we propose an incentive mechanism, named Winners-Take-All (WINTALL). WINTALL first decides a winning answer based on the reported data, cost functions of individuals, and some prior distribution; and then pays to individuals whose reported data match the winning answer. Under some assumptions, we show that the expected payment of WINTALL matches the lower bound. In the application of private discrete distribution estimation, we show that WINTALL simply rewards individuals whose reported answers match the most popular answer from the reported ones (the prior distribution is not needed in this case).

Original languageEnglish (US)
Title of host publicationProceedings of NetEcon 2018
Subtitle of host publicationThe 13th Workshop on the Economics of Networks, Systems, and Computation - In conjunction with ACM SIGMETRICS 2018: The ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)9781450359160
DOIs
StatePublished - Jun 18 2018
Event13th Workshop on the Economics of Networks, Systems, and Computation, NetEcon 2018 - In conjunction with the ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, ACM SIGMETRICS 2018 - Irvine, United States
Duration: Jun 18 2018 → …

Other

Other13th Workshop on the Economics of Networks, Systems, and Computation, NetEcon 2018 - In conjunction with the ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, ACM SIGMETRICS 2018
CountryUnited States
CityIrvine
Period6/18/18 → …

Fingerprint

Data privacy
Cost functions
Big data

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Networks and Communications

Cite this

Jiang, P., Wang, W., Ying, L., Zhou, Y., & He, J. (2018). A winners-take-all incentive mechanism for crowd-powered systems. In Proceedings of NetEcon 2018: The 13th Workshop on the Economics of Networks, Systems, and Computation - In conjunction with ACM SIGMETRICS 2018: The ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems [3] Association for Computing Machinery, Inc. https://doi.org/10.1145/3230654.3230657

A winners-take-all incentive mechanism for crowd-powered systems. / Jiang, Pengfei; Wang, Weina; Ying, Lei; Zhou, Yao; He, Jingrui.

Proceedings of NetEcon 2018: The 13th Workshop on the Economics of Networks, Systems, and Computation - In conjunction with ACM SIGMETRICS 2018: The ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems. Association for Computing Machinery, Inc, 2018. 3.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Jiang, P, Wang, W, Ying, L, Zhou, Y & He, J 2018, A winners-take-all incentive mechanism for crowd-powered systems. in Proceedings of NetEcon 2018: The 13th Workshop on the Economics of Networks, Systems, and Computation - In conjunction with ACM SIGMETRICS 2018: The ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems., 3, Association for Computing Machinery, Inc, 13th Workshop on the Economics of Networks, Systems, and Computation, NetEcon 2018 - In conjunction with the ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, ACM SIGMETRICS 2018, Irvine, United States, 6/18/18. https://doi.org/10.1145/3230654.3230657
Jiang P, Wang W, Ying L, Zhou Y, He J. A winners-take-all incentive mechanism for crowd-powered systems. In Proceedings of NetEcon 2018: The 13th Workshop on the Economics of Networks, Systems, and Computation - In conjunction with ACM SIGMETRICS 2018: The ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems. Association for Computing Machinery, Inc. 2018. 3 https://doi.org/10.1145/3230654.3230657
Jiang, Pengfei ; Wang, Weina ; Ying, Lei ; Zhou, Yao ; He, Jingrui. / A winners-take-all incentive mechanism for crowd-powered systems. Proceedings of NetEcon 2018: The 13th Workshop on the Economics of Networks, Systems, and Computation - In conjunction with ACM SIGMETRICS 2018: The ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems. Association for Computing Machinery, Inc, 2018.
@inproceedings{e985a714fc5f41ff8fa0cbca24e8ca77,
title = "A winners-take-all incentive mechanism for crowd-powered systems",
abstract = "This paper studies incentive mechanisms for crowd-powered systems, including applications such as collection of personal data for big-data analytics and crowdsourcing. In big-data analytics using personal data, an individual may control the quality of reported data via a privacy-preserving mechanism that randomizes the answer. In crowdsourcing, the quality of the reported answer depends on the amount of effort spent by a worker or a team. In these applications, incentive mechanisms are critical for eliciting data/answers with target quality. This paper focuses the following two fundamental questions: what is the minimum payment required to incentivize an individual to submit a data/answer with quality level ∈? and what incentive mechanisms can achieve the minimum payment? Let ∈i denote the quality of the data/answer reported by individual i: In this paper, we first derive a lower bound on the minimum amount of payment required for guaranteeing quality level ∈i: Inspired by the lower bound, we propose an incentive mechanism, named Winners-Take-All (WINTALL). WINTALL first decides a winning answer based on the reported data, cost functions of individuals, and some prior distribution; and then pays to individuals whose reported data match the winning answer. Under some assumptions, we show that the expected payment of WINTALL matches the lower bound. In the application of private discrete distribution estimation, we show that WINTALL simply rewards individuals whose reported answers match the most popular answer from the reported ones (the prior distribution is not needed in this case).",
author = "Pengfei Jiang and Weina Wang and Lei Ying and Yao Zhou and Jingrui He",
year = "2018",
month = "6",
day = "18",
doi = "10.1145/3230654.3230657",
language = "English (US)",
booktitle = "Proceedings of NetEcon 2018",
publisher = "Association for Computing Machinery, Inc",

}

TY - GEN

T1 - A winners-take-all incentive mechanism for crowd-powered systems

AU - Jiang, Pengfei

AU - Wang, Weina

AU - Ying, Lei

AU - Zhou, Yao

AU - He, Jingrui

PY - 2018/6/18

Y1 - 2018/6/18

N2 - This paper studies incentive mechanisms for crowd-powered systems, including applications such as collection of personal data for big-data analytics and crowdsourcing. In big-data analytics using personal data, an individual may control the quality of reported data via a privacy-preserving mechanism that randomizes the answer. In crowdsourcing, the quality of the reported answer depends on the amount of effort spent by a worker or a team. In these applications, incentive mechanisms are critical for eliciting data/answers with target quality. This paper focuses the following two fundamental questions: what is the minimum payment required to incentivize an individual to submit a data/answer with quality level ∈? and what incentive mechanisms can achieve the minimum payment? Let ∈i denote the quality of the data/answer reported by individual i: In this paper, we first derive a lower bound on the minimum amount of payment required for guaranteeing quality level ∈i: Inspired by the lower bound, we propose an incentive mechanism, named Winners-Take-All (WINTALL). WINTALL first decides a winning answer based on the reported data, cost functions of individuals, and some prior distribution; and then pays to individuals whose reported data match the winning answer. Under some assumptions, we show that the expected payment of WINTALL matches the lower bound. In the application of private discrete distribution estimation, we show that WINTALL simply rewards individuals whose reported answers match the most popular answer from the reported ones (the prior distribution is not needed in this case).

AB - This paper studies incentive mechanisms for crowd-powered systems, including applications such as collection of personal data for big-data analytics and crowdsourcing. In big-data analytics using personal data, an individual may control the quality of reported data via a privacy-preserving mechanism that randomizes the answer. In crowdsourcing, the quality of the reported answer depends on the amount of effort spent by a worker or a team. In these applications, incentive mechanisms are critical for eliciting data/answers with target quality. This paper focuses the following two fundamental questions: what is the minimum payment required to incentivize an individual to submit a data/answer with quality level ∈? and what incentive mechanisms can achieve the minimum payment? Let ∈i denote the quality of the data/answer reported by individual i: In this paper, we first derive a lower bound on the minimum amount of payment required for guaranteeing quality level ∈i: Inspired by the lower bound, we propose an incentive mechanism, named Winners-Take-All (WINTALL). WINTALL first decides a winning answer based on the reported data, cost functions of individuals, and some prior distribution; and then pays to individuals whose reported data match the winning answer. Under some assumptions, we show that the expected payment of WINTALL matches the lower bound. In the application of private discrete distribution estimation, we show that WINTALL simply rewards individuals whose reported answers match the most popular answer from the reported ones (the prior distribution is not needed in this case).

UR - http://www.scopus.com/inward/record.url?scp=85058410306&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85058410306&partnerID=8YFLogxK

U2 - 10.1145/3230654.3230657

DO - 10.1145/3230654.3230657

M3 - Conference contribution

BT - Proceedings of NetEcon 2018

PB - Association for Computing Machinery, Inc

ER -