Eliciting Information from Heterogeneous Mobile Crowdsourced Workers Without Verification

Chao Huang, Haoran Yu, Jianwei Huang, Randall Berry

Research output: Contribution to journalArticlepeer-review


We study the incentive mechanism design in mobile crowdsourcing where platforms aims to incentivize high-quality solutions from workers but cannot verify them. A common incentive mechanism is majority voting, where each worker is rewarded if his solution matches the majority's solution. However, previous work typically assumes that workers have homogeneous solution accuracy. This is unrealistic in many domains, as one expects workers to differ in judgment, expertise, and reliability. Moreover, prior work has not considered how this heterogeneity affects a platform's tradeoff between inducing high- quality solutions from workers and the cost of achieving this. We address these gaps in this paper. We show that as a worker's solution accuracy increases, he is more likely, in equilibrium, to exert effort and truthfully report his solution. However, given a fixed total worker population, surprisingly, the platform's payoff may decrease in the number of high-accuracy workers. We further characterize the value of knowing the workers solution accuracy for the platform. Knowing such information enables a more effective aggregation of the workers solutions, and motivates a discriminatory reward policy to incentivize the heterogeneous workers. Surprisingly, such a discriminatory policy can improve both the platform's and the workers payoffs, and hence improve social welfare.

Original languageEnglish (US)
JournalIEEE Transactions on Mobile Computing
StateAccepted/In press - 2021


  • Crowdsensing
  • Games
  • Mobile computing
  • Roads
  • Robot sensing systems
  • Sensors
  • Task analysis
  • game theory
  • incentive mechanism design
  • majority voting
  • mobile crowdsourcing

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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