Random Utility Theory for Social Choice
Soufiani, Houssein Azari
Xia, LirongNote: Order does not necessarily reflect citation order of authors.
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CitationSoufani, Hossein Azari, David C. Parkes, and Lirong Xia. 2012. Random utility theory for social choice. In Proceeedings of the 25th Annual Conference on Neural Information ProcessingSystems (NIPS'12), 3-6 December 2012, Lake Tahoe, Nevada, 126-134. Rd Hook, NY: Curran Associates and NIPS.
AbstractRandom 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.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:11882033
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