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2012 NIPS NeurIPS 2012

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.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Machine Learning
📈 Trend Setter — Probabilistic Modeling
🧭 Keyword Pioneer — random utility theory
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
🌱 Topic Pioneer — Social
🐣 Hot Topic Early Bird — model selection

Authors

Hossein Azari , David Parks , Lirong Xia

Topics

Artificial Intelligence > Bayesian & Probabilistic > Bayesian Learning Artificial Intelligence > Bayesian & Probabilistic > Probabilistic Modeling Machine Learning > Core Methods > Regression Machine Learning > Optimization & Theory > Bayesian Inference Interdisciplinary > Social Machine Learning > Bayesian & Probabilistic > Probabilistic Modeling Machine Learning > Bayesian & Probabilistic > Bayesian Inference Mathematics & Optimization > Optimization > Game Theory Mathematics & Optimization > Probability Machine Learning > Learning Types > Preference Learning

Keywords

model selection probabilistic modeling bayesian inference preference learning maximum likelihood plackett-luce model random utility theory preference modeling social choice random utility maximum likelihood estimator monte carlo em
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