  The well-studied problem of statistical rank aggregation has been applied to
comparing sports teams, information retrieval, and most recently to data
generated by human judgment. Such human-generated rankings may be substantially
different from traditional statistical ranking data. In this work, we show that
a recently proposed generalized random utility model reveals distinctive
patterns in human judgment across three different domains, and provides a
succinct representation of variance in both population preferences and
imperfect perception. In contrast, we also show that classical statistical
ranking models fail to capture important features from human-generated input.
Our work motivates the use of more flexible ranking models for representing and
describing the collective preferences or decision-making of human participants.
