ABT: An Effective Ability-Balanced Team Based Incentive Mechanism in Crowdsourcing System

Yufeng Wang, Jie Huang, Qun Jin, Jianhua Ma

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

    Abstract

    Crowdsourcing systems which rely on a great deal of crowdworkers to perform large quantities of microtasks, have been leveraged in a variety of applications. There are two factors affecting the productive output of each crowdworker. One is skill level, which is private information to each crowdworker, and another is her variable expended effort. In this paper, we construct and analyze a total-Ability-balanced team based incentive mechanism ABT, which can stimulate the strategic crowdworkers to truthfully report their ability levels, and according to crowdworkers' ability levels, form the competing teams. Specifically, a crowdworker with a certain skill level, is askedto choose a specificskill level (i.e., denoted as an ability threshold), and a basic payment scheme is designed to incentivize the crowdworker to truthfully report her ability level. Then, according to the chosen ability thresholds, crowdworkers are organized into total ability balanced teams to earn extra team bonus, which can further motivate crowdworkers to exert more efforts. Compared to team formation process where workers are randomly assigned to the same-scale teams and the pay per task model, our scheme ABT can improve the work efficiency.

    Original languageEnglish
    Title of host publicationProceedings - 5th International Conference on Advanced Cloud and Big Data, CBD 2017
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages220-225
    Number of pages6
    ISBN (Electronic)9781538610725
    DOIs
    Publication statusPublished - 2017 Sep 6
    Event5th International Conference on Advanced Cloud and Big Data, CBD 2017 - Shanghai, China
    Duration: 2017 Aug 132017 Aug 16

    Other

    Other5th International Conference on Advanced Cloud and Big Data, CBD 2017
    CountryChina
    CityShanghai
    Period17/8/1317/8/16

    Fingerprint

    Incentive mechanism
    Private information
    Bonus
    Factors
    Payment
    Workers
    Team formation

    Keywords

    • Crowdsourcing system
    • Incentive mechanism
    • Labor divisions
    • Team competition

    ASJC Scopus subject areas

    • Computer Networks and Communications
    • Hardware and Architecture
    • Information Systems and Management

    Cite this

    Wang, Y., Huang, J., Jin, Q., & Ma, J. (2017). ABT: An Effective Ability-Balanced Team Based Incentive Mechanism in Crowdsourcing System. In Proceedings - 5th International Conference on Advanced Cloud and Big Data, CBD 2017 (pp. 220-225). [8026940] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CBD.2017.45

    ABT : An Effective Ability-Balanced Team Based Incentive Mechanism in Crowdsourcing System. / Wang, Yufeng; Huang, Jie; Jin, Qun; Ma, Jianhua.

    Proceedings - 5th International Conference on Advanced Cloud and Big Data, CBD 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 220-225 8026940.

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

    Wang, Y, Huang, J, Jin, Q & Ma, J 2017, ABT: An Effective Ability-Balanced Team Based Incentive Mechanism in Crowdsourcing System. in Proceedings - 5th International Conference on Advanced Cloud and Big Data, CBD 2017., 8026940, Institute of Electrical and Electronics Engineers Inc., pp. 220-225, 5th International Conference on Advanced Cloud and Big Data, CBD 2017, Shanghai, China, 17/8/13. https://doi.org/10.1109/CBD.2017.45
    Wang Y, Huang J, Jin Q, Ma J. ABT: An Effective Ability-Balanced Team Based Incentive Mechanism in Crowdsourcing System. In Proceedings - 5th International Conference on Advanced Cloud and Big Data, CBD 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 220-225. 8026940 https://doi.org/10.1109/CBD.2017.45
    Wang, Yufeng ; Huang, Jie ; Jin, Qun ; Ma, Jianhua. / ABT : An Effective Ability-Balanced Team Based Incentive Mechanism in Crowdsourcing System. Proceedings - 5th International Conference on Advanced Cloud and Big Data, CBD 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 220-225
    @inproceedings{94c1af3c2422417c8af9d8a607e5cbfc,
    title = "ABT: An Effective Ability-Balanced Team Based Incentive Mechanism in Crowdsourcing System",
    abstract = "Crowdsourcing systems which rely on a great deal of crowdworkers to perform large quantities of microtasks, have been leveraged in a variety of applications. There are two factors affecting the productive output of each crowdworker. One is skill level, which is private information to each crowdworker, and another is her variable expended effort. In this paper, we construct and analyze a total-Ability-balanced team based incentive mechanism ABT, which can stimulate the strategic crowdworkers to truthfully report their ability levels, and according to crowdworkers' ability levels, form the competing teams. Specifically, a crowdworker with a certain skill level, is askedto choose a specificskill level (i.e., denoted as an ability threshold), and a basic payment scheme is designed to incentivize the crowdworker to truthfully report her ability level. Then, according to the chosen ability thresholds, crowdworkers are organized into total ability balanced teams to earn extra team bonus, which can further motivate crowdworkers to exert more efforts. Compared to team formation process where workers are randomly assigned to the same-scale teams and the pay per task model, our scheme ABT can improve the work efficiency.",
    keywords = "Crowdsourcing system, Incentive mechanism, Labor divisions, Team competition",
    author = "Yufeng Wang and Jie Huang and Qun Jin and Jianhua Ma",
    year = "2017",
    month = "9",
    day = "6",
    doi = "10.1109/CBD.2017.45",
    language = "English",
    pages = "220--225",
    booktitle = "Proceedings - 5th International Conference on Advanced Cloud and Big Data, CBD 2017",
    publisher = "Institute of Electrical and Electronics Engineers Inc.",
    address = "United States",

    }

    TY - GEN

    T1 - ABT

    T2 - An Effective Ability-Balanced Team Based Incentive Mechanism in Crowdsourcing System

    AU - Wang, Yufeng

    AU - Huang, Jie

    AU - Jin, Qun

    AU - Ma, Jianhua

    PY - 2017/9/6

    Y1 - 2017/9/6

    N2 - Crowdsourcing systems which rely on a great deal of crowdworkers to perform large quantities of microtasks, have been leveraged in a variety of applications. There are two factors affecting the productive output of each crowdworker. One is skill level, which is private information to each crowdworker, and another is her variable expended effort. In this paper, we construct and analyze a total-Ability-balanced team based incentive mechanism ABT, which can stimulate the strategic crowdworkers to truthfully report their ability levels, and according to crowdworkers' ability levels, form the competing teams. Specifically, a crowdworker with a certain skill level, is askedto choose a specificskill level (i.e., denoted as an ability threshold), and a basic payment scheme is designed to incentivize the crowdworker to truthfully report her ability level. Then, according to the chosen ability thresholds, crowdworkers are organized into total ability balanced teams to earn extra team bonus, which can further motivate crowdworkers to exert more efforts. Compared to team formation process where workers are randomly assigned to the same-scale teams and the pay per task model, our scheme ABT can improve the work efficiency.

    AB - Crowdsourcing systems which rely on a great deal of crowdworkers to perform large quantities of microtasks, have been leveraged in a variety of applications. There are two factors affecting the productive output of each crowdworker. One is skill level, which is private information to each crowdworker, and another is her variable expended effort. In this paper, we construct and analyze a total-Ability-balanced team based incentive mechanism ABT, which can stimulate the strategic crowdworkers to truthfully report their ability levels, and according to crowdworkers' ability levels, form the competing teams. Specifically, a crowdworker with a certain skill level, is askedto choose a specificskill level (i.e., denoted as an ability threshold), and a basic payment scheme is designed to incentivize the crowdworker to truthfully report her ability level. Then, according to the chosen ability thresholds, crowdworkers are organized into total ability balanced teams to earn extra team bonus, which can further motivate crowdworkers to exert more efforts. Compared to team formation process where workers are randomly assigned to the same-scale teams and the pay per task model, our scheme ABT can improve the work efficiency.

    KW - Crowdsourcing system

    KW - Incentive mechanism

    KW - Labor divisions

    KW - Team competition

    UR - http://www.scopus.com/inward/record.url?scp=85031715047&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=85031715047&partnerID=8YFLogxK

    U2 - 10.1109/CBD.2017.45

    DO - 10.1109/CBD.2017.45

    M3 - Conference contribution

    AN - SCOPUS:85031715047

    SP - 220

    EP - 225

    BT - Proceedings - 5th International Conference on Advanced Cloud and Big Data, CBD 2017

    PB - Institute of Electrical and Electronics Engineers Inc.

    ER -