Choice Modeling is an interdisciplinary field combining economics, statistics, and behavioral science. It is a popular method for examining people's behavior, ranging from the individual level (behavioral science and philosophy) to the population level (economics and statistics) and is applied across various fields, including transportation, health, agriculture, environment, animal science, and more.
As an applied economist, my goal in choice modeling research is to apply advanced methods to my profession, always staying at the cutting edge. My journey in this field has been extensive, encompassing foundational concepts such as Random Utility Theory, field theory, and logit models, as well as advanced techniques like Bayesian simulations.
From 2024, with support from collaborators with Computer Science expertise at Georgia Tech, we are developing a simulated choice entity that mimics human decision-making. This work aims to validate conventional choice modeling approaches in evaluating policy impacts.
Books I have found helpful and recommend to choice modeling enthusiasts:
Journal of Choice Modeling
Discrete Choice Methods with Simulation by Kenneth E. Train, 2009
Applied Choice Analysis: A Primer by David A. Hensher, John M. Rose, and William H. Greene, 2005
Bayesian Statistics and Marketing by Peter E. Rossi, Greg M. Allenby, and Robert McCulloch, 2005
R Packages:
apollo: Hess, S. & Palma, D., 2019.
"This is a powerful package with a steep learning curve but excellent functionality, including multi-threading capability! If you're not preparing for a meeting tomorrow morning, learn this!"
mlogit: Yves Croissant, 2020
"A classic package—no questions asked."
gmnl: Sarrias M, Daziano R, 2017
"A great all-in-one package. If you want Generalized MNL ready out-of-the-box, this is the way to go."
logitr: John Paul Helveston, 2014
"This was the first package I learned. Simple to code and incredibly fast."