Eliciting Information from Heterogeneous Mobile Crowdsourced Workers Without Verification

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)

Abstract

In mobile crowdsourcing, platforms seek to incentivize heterogeneous workers to complete tasks (e.g., road traffic sensing) and truthfully report their solutions. When platforms cannot verify the quality of the workers' solutions, the crowdsourcing problem is known as information elicitation without verification (IEWV). In an IEWV problem, a platform needs to provide incentives to motivate high-quality solutions and truthful reporting of the solutions from the workers. A common approach to solve the IEWV problem is majority voting, where each worker is rewarded according to whether his solution matches the majority's solution. However, previous work has not considered workers with heterogeneous solution accuracy. This is unrealistic in many domains, where one would expect workers to differ in judgment, expertise, and reliability. Moreover, prior work has not considered how this heterogeneity affects a platform's tradeoff between the quality of the workers' solutions and the platform's cost of achieving this. We address these gaps by studying the interactions between the mobile crowdsourcing platform and workers as a two-stage Stackelberg game. In Stage I, the platform chooses the reward level for majority voting. In Stage II, the workers decide their effort levels and reporting strategies. 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 in terms of improving the platform's optimal reward design and maximizing its payoff. Knowing such information enables a more effective aggregation of the workers' solutions. We further design a discriminatory reward policy to incentivize heterogeneous workers. Surprisingly, such a discriminatory policy can improve both the platform's and the workers' payoffs, and hence improve the social welfare.

Original languageEnglish
Pages (from-to)3551-3564
Number of pages14
JournalIEEE Transactions on Mobile Computing
Volume21
Issue number10
DOIs
Publication statusPublished - 1 Oct 2022

Keywords

  • Mobile crowdsourcing
  • game theory
  • incentive mechanism design
  • majority voting

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