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

• URL: http://arxiv.org/abs/2404.13198v1
• Date: Fri, 19 Apr 2024 22:13:12 GMT
• Title: An economically-consistent discrete choice model with flexible utility specification based on artificial neural networks
• Authors: Jose Ignacio Hernandez, Niek Mouter, Sander van Cranenburgh,
• Abstract summary: We propose a new discrete choice model based on artificial neural networks (ANNs) named "Alternative-Specific and Shared weights Neural Network (ASS-NN)" The ASS-NN can derive economically-consistent outcomes, such as marginal utilities or willingness to pay, without explicitly specifying the utility functional form.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

Related papers

• Bayesian Entropy Neural Networks for Physics-Aware Prediction [14.705526856205454]
  We introduce BENN, a framework designed to impose constraints on Bayesian Neural Network (BNN) predictions. Benn is capable of constraining not only the predicted values but also their derivatives and variances, ensuring a more robust and reliable model output. Results highlight significant improvements over traditional BNNs and showcase competitive performance relative to contemporary constrained deep learning methods.
  arXiv  Detail & Related papers  (2024-07-01T07:00:44Z)
• Discrete-Choice Model with Generalized Additive Utility Network [0.0]
  Multinomial logit models (MNLs) with linear utility functions have been used in practice because they are ease to use and interpretable. We developed utility functions with a novel neural-network architecture based on generalized additive models. Our models were comparable to ASU-DNN in accuracy and exhibited improved interpretability compared to previous models.
  arXiv  Detail & Related papers  (2023-09-29T04:40:01Z)
• Neural Additive Models for Location Scale and Shape: A Framework for Interpretable Neural Regression Beyond the Mean [1.0923877073891446]
  Deep neural networks (DNNs) have proven to be highly effective in a variety of tasks. Despite this success, the inner workings of DNNs are often not transparent. This lack of interpretability has led to increased research on inherently interpretable neural networks.
  arXiv  Detail & Related papers  (2023-01-27T17:06:13Z)
• Bayesian Neural Networks for Macroeconomic Analysis [0.0]
  We develop Bayesian neural networks (BNNs) that are well-suited for handling datasets commonly used for macroeconomic analysis in policy institutions. Our approach avoids extensive specification searches through a novel mixture specification for the activation function. We show that our BNNs produce precise density forecasts, typically better than those from other machine learning methods.
  arXiv  Detail & Related papers  (2022-11-09T09:10:57Z)
• Batch-Ensemble Stochastic Neural Networks for Out-of-Distribution Detection [55.028065567756066]
  Out-of-distribution (OOD) detection has recently received much attention from the machine learning community due to its importance in deploying machine learning models in real-world applications. In this paper we propose an uncertainty quantification approach by modelling the distribution of features. We incorporate an efficient ensemble mechanism, namely batch-ensemble, to construct the batch-ensemble neural networks (BE-SNNs) and overcome the feature collapse problem. We show that BE-SNNs yield superior performance on several OOD benchmarks, such as the Two-Moons dataset, the FashionMNIST vs MNIST dataset, FashionM
  arXiv  Detail & Related papers  (2022-06-26T16:00:22Z)
• Comparative Analysis of Interval Reachability for Robust Implicit and Feedforward Neural Networks [64.23331120621118]
  We use interval reachability analysis to obtain robustness guarantees for implicit neural networks (INNs) INNs are a class of implicit learning models that use implicit equations as layers. We show that our approach performs at least as well as, and generally better than, applying state-of-the-art interval bound propagation methods to INNs.
  arXiv  Detail & Related papers  (2022-04-01T03:31:27Z)
• EIGNN: Efficient Infinite-Depth Graph Neural Networks [51.97361378423152]
  Graph neural networks (GNNs) are widely used for modelling graph-structured data in numerous applications. Motivated by this limitation, we propose a GNN model with infinite depth, which we call Efficient Infinite-Depth Graph Neural Networks (EIGNN) We show that EIGNN has a better ability to capture long-range dependencies than recent baselines, and consistently achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2022-02-22T08:16:58Z)
• Combining Discrete Choice Models and Neural Networks through Embeddings: Formulation, Interpretability and Performance [10.57079240576682]
  This study proposes a novel approach that combines theory and data-driven choice models using Artificial Neural Networks (ANNs) In particular, we use continuous vector representations, called embeddings, for encoding categorical or discrete explanatory variables. Our models deliver state-of-the-art predictive performance, outperforming existing ANN-based models while drastically reducing the number of required network parameters.
  arXiv  Detail & Related papers  (2021-09-24T15:55:31Z)
• Provably Efficient Neural Estimation of Structural Equation Model: An Adversarial Approach [144.21892195917758]
  We study estimation in a class of generalized Structural equation models (SEMs) We formulate the linear operator equation as a min-max game, where both players are parameterized by neural networks (NNs), and learn the parameters of these neural networks using a gradient descent. For the first time we provide a tractable estimation procedure for SEMs based on NNs with provable convergence and without the need for sample splitting.
  arXiv  Detail & Related papers  (2020-07-02T17:55:47Z)
• GANs with Conditional Independence Graphs: On Subadditivity of Probability Divergences [70.30467057209405]
  Generative Adversarial Networks (GANs) are modern methods to learn the underlying distribution of a data set. GANs are designed in a model-free fashion where no additional information about the underlying distribution is available. We propose a principled design of a model-based GAN that uses a set of simple discriminators on the neighborhoods of the Bayes-net/MRF.
  arXiv  Detail & Related papers  (2020-03-02T04:31:22Z)
• Efficient Probabilistic Logic Reasoning with Graph Neural Networks [63.099999467118245]
  Markov Logic Networks (MLNs) can be used to address many knowledge graph problems. Inference in MLN is computationally intensive, making the industrial-scale application of MLN very difficult. We propose a graph neural network (GNN) variant, named ExpressGNN, which strikes a nice balance between the representation power and the simplicity of the model.
  arXiv  Detail & Related papers  (2020-01-29T23:34:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.