Summary of An Economically-consistent Discrete Choice Model with Flexible Utility Specification Based on Artificial Neural Networks, by Jose Ignacio Hernandez et al.
An economically-consistent discrete choice model with flexible utility specification based on artificial neural networks
by Jose Ignacio Hernandez, Niek Mouter, Sander van Cranenburgh
First submitted to arxiv on: 19 Apr 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Econometrics (econ.EM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a new discrete choice model called Alternative-Specific and Shared weights Neural Network (ASS-NN), which combines the flexibility of artificial neural networks with the interpretability of random utility maximization (RUM) models. The ASS-NN aims to balance flexible utility approximation from data with consistency with RUM theory and the fungibility of money. The authors demonstrate that ASS-NN outperforms conventional multinomial logit (MNL) models in terms of goodness of fit using a Monte Carlo experiment and empirical data from the Swissmetro dataset. Furthermore, they show how the ASS-NN can be used to derive marginal utilities and willingness-to-pay measures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to model choices using artificial neural networks. It helps us understand why people make certain decisions by making sure the results are consistent with how money works (fungibility). The new method, called ASS-NN, is better than old methods like MNL at predicting what will happen in different situations. The authors tested it with real data and showed that it can even give us useful information about how much people are willing to pay for things. |
Keywords
» Artificial intelligence » Neural network
