Capturing Variation and Uncertainty in Human Judgment
Abstract
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.
Cite
@article{arxiv.1311.0251,
title = {Capturing Variation and Uncertainty in Human Judgment},
author = {Andrew Mao and Hossein Azari Soufiani and Yiling Chen and David C. Parkes},
journal= {arXiv preprint arXiv:1311.0251},
year = {2014}
}