Modeling the choices of individual decision-makers by combining efficient choice experiment designs with extra preference information
Files
(Published version)
Date
2008
Authors
Louviere, J.J.
Street, D.
Burgess, L.
Wasi, N.
Islam, T.
Marley, A.A.J.
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Journal article
Citation
Journal of Choice Modelling, 2008; 1(1):128-164
Statement of Responsibility
Conference Name
Abstract
We show how to combine statistically efficient ways to design discrete choice experiments based on random utility theory with new ways of collecting additional information that can be used to expand the amount of available choice information for modeling the choices of individual decision makers. Here we limit ourselves to problems involving generic choice options and linear and additive indirect utility functions, but the approach potentially can be extended to include choice problems with non-additive utility functions and non-generic/labeled options/attributes. The paper provides several simulated examples, a small empirical example to demonstrate proof of concept, and a larger empirical example based on many experimental conditions and large samples that demonstrates that the individual models capture virtually all the variance in aggregate first choices traditionally modeled in discrete choice experiments.
School/Discipline
Dissertation Note
Provenance
Description
Access Status
Rights
Copyright [2008 The Authors]. Licensed under a Creative Commons Attribution-Non-Commercial 2.0 UK: England & Wales License (http://creativecommons.org/licenses/by-nc/2.0/uk/)