  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.
