Truth discovery is a general name for a broad range of statistical methods
aimed to extract the correct answers to questions, based on multiple answers
coming from noisy sources. For example, workers in a crowdsourcing platform. In
this paper, we consider an extremely simple heuristic for estimating workers'
competence using average proximity to other workers. We prove that this
estimates well the actual competence level and enables separating high and low
quality workers in a wide spectrum of domains and statistical models. Under
Gaussian noise, this simple estimate is the unique solution to the MLE with a
constant regularization factor.
Finally, weighing workers according to their average proximity in a
crowdsourcing setting, results in substantial improvement over unweighted
aggregation and other truth discovery algorithms in practice.

本文研究了一种简单的启发式方法来估计工人的能力，使用平均接近度来区分高质量和低质量的工人以及证明了这种方法在广泛的领域和统计模型中能够很好地估计实际能力水平，并且在众包平台上按照工人的平均接近度加权会显著提高聚合结果。该简单的估计是具有恒定正则化因子的 MLE 中的唯一解，在高斯噪声下表现出色。