Random Utility Theory for Social Choice
Abstract
Random utility theory models an agents preferences on alternatives by drawing a real-valued score on each alternative (typically independently) from a parameterized distribution, and then ranking the alternatives according to scores. A special case that has received signicant attention is the Plackett-Luce model, for which fast inference methods for maximum likelihood estimators are available. This paper develops conditions on general random utility models that enable fast inference within a Bayesian framework through MC-EM, providing concave loglikelihood functions and bounded sets of global maxima solutions. Results on both real-world and simulated data provide support for the scalability of the approach and capability for model selection among general random utility models including Plackett-Luce.
Cite
Text
Azari et al. "Random Utility Theory for Social Choice." Neural Information Processing Systems, 2012.Markdown
[Azari et al. "Random Utility Theory for Social Choice." Neural Information Processing Systems, 2012.](https://mlanthology.org/neurips/2012/azari2012neurips-random/)BibTeX
@inproceedings{azari2012neurips-random,
title = {{Random Utility Theory for Social Choice}},
author = {Azari, Hossein and Parks, David and Xia, Lirong},
booktitle = {Neural Information Processing Systems},
year = {2012},
pages = {126-134},
url = {https://mlanthology.org/neurips/2012/azari2012neurips-random/}
}