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Integrable Elasticity via Neural Demand Potentials
Carlos Hered · 2026-05-23 · via cs.LG updates on arXiv.org

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Abstract: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.
Comments: 44 pages, 7 figures
Subjects: Machine Learning (cs.LG)
Cite as: arXiv:2605.22820 [cs.LG]
  (or arXiv:2605.22820v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2605.22820

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Carlos Heredia Pimienta [view email]
[v1] Thu, 21 May 2026 17:59:47 UTC (857 KB)