  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.
