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。

一种基于人工神经网络的经济一致的灵活效用规范的离散选择模型

An economically-consistent discrete choice model with flexible utility  specification based on artificial neural networks

This paper introduces the RUMBoost model, a novel discrete choice modelling
approach that combines the interpretability and behavioural robustness of
Random Utility Models (RUMs) with the generalisation and predictive ability of
deep learning methods. We obtain the full functional form of non-linear utility
specifications by replacing each linear parameter in the utility functions of a
RUM with an ensemble of gradient boosted regression trees. This enables
piece-wise constant utility values to be imputed for all alternatives directly
from the data for any possible combination of input variables. We introduce
additional constraints on the ensembles to ensure three crucial features of the
utility specifications: (i) dependency of the utilities of each alternative on
only the attributes of that alternative, (ii) monotonicity of marginal
utilities, and (iii) an intrinsically interpretable functional form, where the
exact response of the model is known throughout the entire input space.
Furthermore, we introduce an optimisation-based smoothing technique that
replaces the piece-wise constant utility values of alternative attributes with
monotonic piece-wise cubic splines to identify non-linear parameters with
defined gradient. We demonstrate the potential of the RUMBoost model compared
to various ML and Random Utility benchmark models for revealed preference mode
choice data from London. The results highlight the great predictive performance
and the direct interpretability of our proposed approach. Furthermore, the
smoothed attribute utility functions allow for the calculation of various
behavioural indicators and marginal utilities. Finally, we demonstrate the
flexibility of our methodology by showing how the RUMBoost model can be
extended to complex model specifications, including attribute interactions,
correlation within alternative error terms and heterogeneity within the
population.

该论文介绍了 RUMBoost 模型，这是一种新的离散选择建模方法，它结合了随机效用模型（RUMs）的可解释性和行为鲁棒性与深度学习方法的泛化能力和预测能力。

RUMBoost: 梯度提升的随机效用模型

RUMBoost: Gradient Boosted Random Utility Models

Travel providers such as airlines and on-line travel agents are becoming more
and more interested in understanding how passengers choose among alternative
itineraries when searching for flights. This knowledge helps them better
display and adapt their offer, taking into account market conditions and
customer needs. Some common applications are not only filtering and sorting
alternatives, but also changing certain attributes in real-time (e.g., changing
the price). In this paper, we concentrate with the problem of modeling air
passenger choices of flight itineraries. This problem has historically been
tackled using classical Discrete Choice Modelling techniques. Traditional
statistical approaches, in particular the Multinomial Logit model (MNL), is
widely used in industrial applications due to its simplicity and general good
performance. However, MNL models present several shortcomings and assumptions
that might not hold in real applications. To overcome these difficulties, we
present a new choice model based on Pointer Networks. Given an input sequence,
this type of deep neural architecture combines Recurrent Neural Networks with
the Attention Mechanism to learn the conditional probability of an output whose
values correspond to positions in an input sequence. Therefore, given a
sequence of different alternatives presented to a customer, the model can learn
to point to the one most likely to be chosen by the customer. The proposed
method was evaluated on a real dataset that combines on-line user search logs
and airline flight bookings. Experimental results show that the proposed model
outperforms the traditional MNL model on several metrics.

本文介紹了一種基於 Pointer Networks 的新型選擇模型，並使用深度學習技術結合 Recurrent Neural Networks 和 Attention Mechanism 來學習給定多個選擇時如何選擇最可能被顧客選擇的路線。與傳統的 MNL 模型相比，實驗結果顯示該模型在多個指標上表現優異。