Integrable Elasticity via Neural Demand Potentials

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.

Source: arXiv:2605.22820v1 - http://arxiv.org/abs/2605.22820v1 PDF: https://arxiv.org/pdf/2605.22820v1 Original Link: http://arxiv.org/abs/2605.22820v1