Deep Bayesian Trust : A Dominant and Fair Incentive Mechanism for Crowd
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Abstract (arXiv)
An important class of game-theoretic incentive mechanisms for eliciting effort from a crowd are the peer based mechanisms, in which workers are paid by matching their answers with one another. The other classic mechanism is to have the workers solve some gold standard tasks and pay them according to their accuracy on gold tasks. This mechanism ensures stronger incentive compatibility than the peer based mechanisms but assigning gold tasks to all workers becomes inefficient at large scale. We propose a novel mechanism that assigns gold tasks to only a few workers and exploits transitivity to derive accuracy of the rest of the workers from their peers' accuracy. We show that the resulting mechanism ensures a dominant notion of incentive compatibility and fairness.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Apr 2018.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 0; 2024: 1; 2025: 1.
5 papers.
- 2021Data Pricing in Machine Learning Pipelinescited by 17
- 2025SelectMix: Enhancing Label Noise Robustness through Targeted Sample Mixingcited by 5
- 2024Spot Check Equivalence: an Interpretable Metric for Information Elicitation Mechanismscited by 4
- 2022Better Peer Grading through Bayesian Inferencecited by 1
- 2022Data-Driven Evaluation of Training Action Space for Reinforcement Learningcited by 0
- 2025SelectMix: Enhancing Label Noise Robustness through Targeted Sample Mixing14 Sep 2025
- 2024Spot Check Equivalence: an Interpretable Metric for Information Elicitation Mechanisms21 Feb 2024
- 2022Better Peer Grading through Bayesian Inference2 Sep 2022
- 2022Data-Driven Evaluation of Training Action Space for Reinforcement Learning8 Apr 2022
- 2021Data Pricing in Machine Learning Pipelines18 Aug 2021