Modeling the choices of individual decision-makers by combining efficient choice experiment designs with extra preference information

By Jordan J. Louviere, Deborah J. Street, Leonie Burgess, Nada Wasi, Towhidul Islam, A. A. J. Marley. 2008

274 citations recorded by the source corpus.

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.

Subfields: Economics and Econometrics, General Decision Sciences, Marketing

View Paper: https://www.semanticscholar.org/paper/be5367a6a5966fc39704d4258459d8a2891803d7

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Citations in the corpus

The impact of attribute preferences on adoption timing: The case of photo-voltaic (PV) solar cells for household electricity generation (2013, methodology): “We used a relatively new approach to constructing DCEs due to Louviere et al (2008), who propose the construction of a set of profiles using an experimental design suitable for identifying particular forms of indirect utility functions.”
Models of best–worst choice and ranking among multiattribute options (profiles) (2012, background): “Chrzan and Golovashkina (2006), Jaeger et al. (2008) and Louviere et al. (2008) present detailed material and references on the potential advantages of BWS over more traditional choice methods (such as those that ask a person to choose the best option; choose the worst option; rank the options; or…”
Discrete Choice Experiments: A Guide to Model Specification, Estimation and Software (2017, background): “[31] to be critical of the standard MXL model.”
Segmenting consumers’ reasons for and against ethical consumption (2014, methodology): “Individual-level best-worst scores for each reason were calculated using the ranking expansion method (see, Louviere et al., 2008).”
The Generalized Multinomial Logit Model: Accounting for Scale and Coefficient Heterogeneity (2010, methodology): “However, as shown by Louviere et al. (2008b), it is possible to estimate individuallevel models from stated preference data obtained using efficient experimental designs.”