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.

本文提出了一种基于人工神经网络 (ANNs) 的新的离散选择模型 ——“特定备选和共享权重神经网络（ASS-NN）”，它在灵活拟合数据的效用函数的同时，与选择理论和货币可替代性（即 “一欧元就是一欧元”）两个假设保持一致，从而实现了经济一致的结果，如边际效用或支付意愿，无需明确定义效用函数的形式。通过蒙特卡洛实验和来自 Swissmetro 数据集的实证数据，我们证明了 ASS-NN 在不同效用规范下的性能优于传统的多项式对数模型（MNL）。此外，我们展示了如何使用 ASS-NN 来计算边际效用和支付意愿的度量值。