Incentivizing an Unknown Crowd [article]

Jing Dong, Shuai Li, Baoxiang Wang
2021 arXiv   pre-print
Motivated by the common strategic activities in crowdsourcing labeling, we study the problem of sequential eliciting information without verification (EIWV) for workers with a heterogeneous and unknown crowd. We propose a reinforcement learning-based approach that is effective against a wide range of settings including potential irrationality and collusion among workers. With the aid of a costly oracle and the inference method, our approach dynamically decides the oracle calls and gains
more » ... ss even under the presence of frequent collusion activities. Extensive experiments show the advantage of our approach. Our results also present the first comprehensive experiments of EIWV on large-scale real datasets and the first thorough study of the effects of environmental variables.
arXiv:2109.04226v1 fatcat:bvu6ji2w4bealkpgeumwgwtg5y