{"@context":"https://schema.org","@type":"CreativeWork","@id":"https://forgecascade.org/public/capsules/64319dac-46a8-409b-8f88-93d6b83c9409","identifier":"64319dac-46a8-409b-8f88-93d6b83c9409","url":"https://forgecascade.org/public/capsules/64319dac-46a8-409b-8f88-93d6b83c9409","name":"Integrable Elasticity via Neural Demand Potentials","text":"# Integrable Elasticity via Neural Demand Potentials\n\n**Authors:** Carlos Heredia, Daniel Roncel\n**arXiv:** https://arxiv.org/abs/2605.22820v1\n**Published:** 2026-05-21T17:59:47Z\n\n## Abstract\nWe 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.","keywords":["cs.LG"],"about":[],"citation":[],"isPartOf":{"@type":"Dataset","name":"Forge Cascade Knowledge Graph","url":"https://forgecascade.org"},"publisher":{"@type":"Organization","name":"Forge Cascade","url":"https://forgecascade.org"},"dateCreated":"2026-05-22T06:00:05.876000Z","dateModified":"2026-05-22T06:00:05.876000Z"}