On Cost-Effective Incentive Mechanisms in Microtask Crowdsourcing
Yang Gao, Yan Chen, K. J. Ray Liu

TL;DR
This paper investigates cost-effective incentive mechanisms in microtask crowdsourcing, proposing a new quality-aware training approach that ensures high-quality solutions at low costs, supported by theoretical analysis and behavioral experiments.
Contribution
It introduces a novel incentive mechanism using quality-aware training to achieve high-quality results with minimal costs in microtask crowdsourcing.
Findings
Proved lower bounds on worker costs for existing mechanisms.
Designed a mechanism that guarantees high quality at arbitrarily low cost.
Validated effectiveness through behavioral experiments.
Abstract
While microtask crowdsourcing provides a new way to solve large volumes of small tasks at a much lower price compared with traditional in-house solutions, it suffers from quality problems due to the lack of incentives. On the other hand, providing incentives for microtask crowdsourcing is challenging since verifying the quality of submitted solutions is so expensive that will negate the advantage of microtask crowdsourcing. We study cost-effective incentive mechanisms for microtask crowdsourcing in this paper. In particular, we consider a model with strategic workers, where the primary objective of a worker is to maximize his own utility. Based on this model, we analyze two basic mechanisms widely adopted in existing microtask crowdsourcing applications and show that, to obtain high quality solutions from workers, their costs are constrained by some lower bounds. We then propose a…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMobile Crowdsensing and Crowdsourcing · Auction Theory and Applications · Privacy-Preserving Technologies in Data
