Capturing Variation and Uncertainty in Human Judgment
This work addresses the need for more flexible ranking models to represent collective human preferences or decision-making, which is incremental as it builds on existing models.
The authors tackled the problem of modeling human-generated ranking data, showing that a generalized random utility model captures distinctive patterns and variance in human judgment across three domains, while classical models fail to capture important features.
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.