An economically-consistent discrete choice model with flexible utility specification based on artificial neural networks
arxiv(2024)
摘要
Random utility maximisation (RUM) models are one of the cornerstones of
discrete choice modelling. However, specifying the utility function of RUM
models is not straightforward and has a considerable impact on the resulting
interpretable outcomes and welfare measures. In this paper, we propose a new
discrete choice model based on artificial neural networks (ANNs) named
"Alternative-Specific and Shared weights Neural Network (ASS-NN)", which
provides a further balance between flexible utility approximation from the data
and consistency with two assumptions: RUM theory and fungibility of money
(i.e., "one euro is one euro"). Therefore, the ASS-NN can derive
economically-consistent outcomes, such as marginal utilities or willingness to
pay, without explicitly specifying the utility functional form. Using a Monte
Carlo experiment and empirical data from the Swissmetro dataset, we show that
ASS-NN outperforms (in terms of goodness of fit) conventional multinomial logit
(MNL) models under different utility specifications. Furthermore, we show how
the ASS-NN is used to derive marginal utilities and willingness to pay
measures.
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