We consider the issue of finding optimal designs for Discrete Choice Experiments (DCE). Currently the optimal design for a DCE is predicated on a specific mixed logit model which represents the probability of choosing a specific alternative in a choice set through a linear predictor combining several factors. Using a unique model represents a major limitation which we overcome by allowing for a collection of different models characterized by distinct linear predictors. In this new setting, model choice becomes a major concern. By maximizing the utility of selecting a choice set - computed as the mutual information between the model indicator and the predicted observation at that set - over alternative choice sets, model discrimination is enhanced. We implement our methodology using a sequential Monte Carlo algorithm suitably tailored to deal with model uncertainty.

Consonni, G., Deldossi, L., Saggini, E., Accounting for model uncertainty in individualized designs for discrete choice experiments, in Cladag 2017. Book of short papers, (Milano, 13-15 September 2017), Universitas Studiorum srl, Mantova 2017: N/A-N/A [http://hdl.handle.net/10807/121171]

Accounting for model uncertainty in individualized designs for discrete choice experiments

Consonni
Primo
;
Guido; Deldossi
Secondo
;
Laura; Saggini
Ultimo
2017

Abstract

We consider the issue of finding optimal designs for Discrete Choice Experiments (DCE). Currently the optimal design for a DCE is predicated on a specific mixed logit model which represents the probability of choosing a specific alternative in a choice set through a linear predictor combining several factors. Using a unique model represents a major limitation which we overcome by allowing for a collection of different models characterized by distinct linear predictors. In this new setting, model choice becomes a major concern. By maximizing the utility of selecting a choice set - computed as the mutual information between the model indicator and the predicted observation at that set - over alternative choice sets, model discrimination is enhanced. We implement our methodology using a sequential Monte Carlo algorithm suitably tailored to deal with model uncertainty.
Inglese
Cladag 2017. Book of short papers
CLADAG 2017
Milano
13-set-2017
15-set-2017
9788899459710
Universitas Studiorum srl
Consonni, G., Deldossi, L., Saggini, E., Accounting for model uncertainty in individualized designs for discrete choice experiments, in Cladag 2017. Book of short papers, (Milano, 13-15 September 2017), Universitas Studiorum srl, Mantova 2017: N/A-N/A [http://hdl.handle.net/10807/121171]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10807/121171
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