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SCIENTIA SINICA Informationis, Volume 49, Issue 11: 1412-1427(2019) https://doi.org/10.1360/N112018-00303

An approach for developing a highly trustworthy crowd-sourced workforce

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  • ReceivedMay 31, 2019
  • AcceptedSep 11, 2019
  • PublishedNov 4, 2019

Abstract

In applying crowd-sourcing techniques, one of the most critical challenges is building a crowd workforce that is both capable and trustworthy. Previous studies proposed numerous strategies, methods, and mechanism to motivate individuals; however, although the results improved the effectiveness and efficiency of finishing crowd-sourcing tasks, few studies focused on improving the honesty of crowd-sourced workers and assisting requesters in obtaining the correct quality report. To address this, based on the principal-agent model and signaling game theory, we design a novel mechanism for building a capable and trustworthy crowd-sourced workforce. This mechanism enables information exchange between crowd-sourced workers and requesters, and leverages a random inspection strategy to assign financial incentives/punishments to honest/dishonest behaviors accordingly. To validate our mechanism, we conduct an extensive simulation. The results show this mechanism is effective and efficient to motivate workers to behave in a trustworthily manner and capable of changing the behavior of dishonest workers with minimal extra cost.


Funded by

国家重点研发计划(2018YFB1403400)

国家自然科学基金(61690201,61772014,61802171)


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  • Figure 1

    Four types of crowd workers according to their capability and integrity

  • Figure 2

    Signaling game between a crowd worker and a requester

  • Figure 3

    The process of the proposed mechanism. The left part is the overview of all periods, and the right part is the details of interactions in each period

  • Figure 4

    (Color online) Requester's accumulative payoff over 50 periods

  • Figure 5

    (Color online) Individual contribution to requester's payoff in each period

  • Figure 6

    (Color online) Accumulative administration costs

  • Table 1   The composition workforce after 50 periods (averaged over 20 runs), report-sample-and-pay is excluded because it does not kick out any workers
    机制 总余额 高能力工人数 诚信工人数 理想工作者 成功转化工人数
    运行前 运行后 运行前 运行后 运行前 运行后
    Signal-no-tolerance 353.10 102.40 98.50 181.10 137.10 16.75 16.55 336.55
    Report-no-tolerance 17.60 99.65 17.55 183.15 3.30 17.55 3.30 14.30
    Our mechanism 393.15 102.40 102.10 180.40 137.10 18.65 18.60 374.55

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