Revisiting Random Utility Models
Hossein Azari Soufiani · 2014
This thesis explores extensions of Random Utility Models (RUMs), providing more flexible models and adopting a computational perspective. This includes building new models and understanding their properties such as identifiability and the log concavity of their likelihood functions as well as the development of estimation algorithms. A special case of RUMs that has received significant attention is the Luce model, for which there are fast inference methods for maximum likelihood estimation. This thesis introduces RUMs including those with exponential family utility distributions, mixture of RUMs, and non-parametric RUMs. Fast inference is achieved through the Monte-Carlo Expectation-Maximization (MC-EM) algorithm. Results on both real-world and simulated data provide support for the ability of these models to better capture heterogeneity in data and for scalable model estimation. A class of Generalized Method-of-Moments (GMM) algorithms for computing parameters of the Luce model and RUMs is also proposed. The technique is based on breaking full rankings into pairwise comparisons, and then computing parameters that satisfy a set of