The basic idea underlying the algorithm is that if the dynamic range of the perceptron is not too large then w t would classify most instances correctly most of the time (for most values of t).
reidsma and Carletta (2008) recently showed by simulation that different types of annotator behavior have different impact on the outcomes of machine learning from the annotated data.