An Online Inference-Aided Incentive Framework for Information Elicitation Without Verification

We study the design of incentive mechanisms for the problem of information elicitation without verification (IEWV). In IEWV, a data requester seeks to design proper incentives to optimize the tradeoff between the quality of information (collected from distributed crowd workers) and the total cost of incentives (provided to crowd workers) without verifiable ground truth. While prior work often relies on sufficient knowledge of worker information, we study a scenario where the data requester cannot access workers' heterogeneous information quality and costs ex-ante. We propose a continuum-armed bandit-based incentive mechanism that dynamically learns the optimal reward level from workers' reported information. A key challenge is that the data requester cannot evaluate the workers' information quality without verification, which motivates the design of an inference algorithm. The inference problem is non-convex, yet we reformulate it as a bi-convex problem and derive an approximate solution with a performance guarantee, which ensures the effectiveness of our online reward design. We further enhance the inference algorithm using part of the workers' historical reports. We also propose a novel rule for the data requester to aggregate workers' solutions more effectively. We show that our mechanism achieves a sub-linear regret $\tilde {O}(T^{1/2})$ and outperforms several celebrated benchmarks.