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.

翻译：随机效用最大化模型是离散选择建模的基石之一。然而，RUM模型的效用函数设定并非易事，且对最终可解释结果和福利度量具有显著影响。本文提出一种基于人工神经网络的新型离散选择模型——"备选方案特异与共享权重神经网络"，该模型在数据驱动的灵活效用逼近与两大假设一致性（即RUM理论和货币可替代性——"一欧元即为一欧元"）之间实现了进一步平衡。因此，ASS-NN无需显式设定效用函数形式，即可推导出边际效用或支付意愿等经济一致性结果。通过蒙特卡洛实验和瑞士地铁数据集实证数据，我们证明在不同效用函数设定下，ASS-NN在拟合优度方面优于传统多项Logit模型。此外，我们展示了如何运用ASS-NN推导边际效用与支付意愿指标。