Incentivizing crowdsourced workers via truth detection

Chao Huang, Haoran Yu, Jianwei Huang, Randall A. Berry

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

1 Scopus citations

Abstract

Crowdsourcing platforms often want to incentivize workers to finish tasks with high quality and truthfully report their solutions. A high quality solution requires a worker to exert effort; a platform can motivate such effort exertion and truthful reporting by providing a reward. We propose a novel rewarding mechanism based on using a truth detection technology, which can verify the correctness of workers' responses to questions with an imperfect accuracy (e.g., questions regarding whether the workers exert effort finishing the tasks and whether they truthfully report their solutions). We model the interactions between the platform and workers as a two-stage Stackelberg game. In Stage I, the platform optimizes the reward design associated with truth detection to maximize its payoff. In Stage II, the workers decide their effort levels and reporting strategies to maximize their payoffs (which depend on the output of the truth detection). We analyze the game's equilibrium and show that as the truth detection accuracy improves, the platform should incentivize more workers to exert effort finishing the tasks and truthfully report their solutions. Moreover, our mechanism performs well even when the detection accuracy is not very high. A 60% accurate detection can yield a platform payoff that is more than 85% of the maximum achieved under perfect (100% accurate) detection.

Original languageEnglish (US)
Title of host publicationGlobalSIP 2019 - 7th IEEE Global Conference on Signal and Information Processing, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728127231
DOIs
StatePublished - Nov 2019
Event7th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2019 - Ottawa, Canada
Duration: Nov 11 2019Nov 14 2019

Publication series

NameGlobalSIP 2019 - 7th IEEE Global Conference on Signal and Information Processing, Proceedings

Conference

Conference7th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2019
CountryCanada
CityOttawa
Period11/11/1911/14/19

ASJC Scopus subject areas

  • Information Systems
  • Information Systems and Management
  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Signal Processing

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