2013
NeurIPS
NeurIPS 2013
Generalized Random Utility Models with Multiple Types
Abstract
We propose a model for demand estimation in multi-agent, differentiated product settings and present an estimation algorithm that uses reversible jump MCMC techniques to classify agents' types. Our model extends the popular setup in Berry, Levinsohn and Pakes (1995) to allow for the data-driven classification of agents' types using agent-level data. We focus on applications involving data on agents' ranking over alternatives, and present theoretical conditions that establish the identifiability of the model and uni-modality of the likelihood/posterior. Results on both real and simulated data provide support for the scalability of our approach.
🌉
Interdisciplinary Bridge
- Artificial Intelligence and Mathematics & Optimization
🐣
Hot Topic Early Bird
- multi-agent system
Topics
Machine Learning > Core Methods > Classification
Machine Learning > Optimization & Theory > Bayesian Inference
Mathematics & Optimization > Optimization > Stochastic Methods
Machine Learning > Optimization & Theory > Statistics
Artificial Intelligence > Bayesian & Probabilistic > Bayesian Inference
Machine Learning > Learning Types > Multi-Agent Systems
Mathematics & Optimization > Optimization > Game Theory
Machine Learning > Bayesian & Probabilistic > Markov Chain Monte Carlo
Keywords
variational inference
bayesian inference
markov chain monte carlo
reversible jump mcmc
posterior inference
model identifiability
random utility
demand estimation
type classification
reversible jump
random utility model
multi-agent system
agent type classification
likelihood posterior
reversible jump markov chain monte carlo