Random Utility Theory for Social Choice
Abstract
Random utility theory models an agent's 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 significant 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
@article{arxiv.1211.2476,
title = {Random Utility Theory for Social Choice},
author = {Hossein Azari Soufiani and David C. Parkes and Lirong Xia},
journal= {arXiv preprint arXiv:1211.2476},
year = {2012}
}