Integrable Elasticity via Neural Demand Potentials
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866917520780296192 |
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| author | Heredia, Carlos Roncel, Daniel |
| author_facet | Heredia, Carlos Roncel, Daniel |
| contents | We propose the Integrable Context-Dependent Demand Network (ICDN), a demand-first neural model for multiproduct retail demand. The model learns log-demand as a smooth, context-conditioned function of log-prices, allowing elasticities to be derived exactly from the learned demand surface. On the Dominick's beer dataset, ICDN improves out-of-sample generalization over a directed log-log benchmark and yields more stable, economically plausible elasticity estimates, especially for weakly identified cross-price effects. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_22820 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Integrable Elasticity via Neural Demand Potentials Heredia, Carlos Roncel, Daniel Machine Learning We propose the Integrable Context-Dependent Demand Network (ICDN), a demand-first neural model for multiproduct retail demand. The model learns log-demand as a smooth, context-conditioned function of log-prices, allowing elasticities to be derived exactly from the learned demand surface. On the Dominick's beer dataset, ICDN improves out-of-sample generalization over a directed log-log benchmark and yields more stable, economically plausible elasticity estimates, especially for weakly identified cross-price effects. |
| title | Integrable Elasticity via Neural Demand Potentials |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2605.22820 |