Neural demand models can be designed to respect economic constraints (integrability), producing more reliable price-elasticity estimates that are both mathematically consistent and practically useful for retail pricing.
This paper introduces ICDN, a neural network model that learns demand patterns for multiple products based on prices. Unlike traditional approaches, it directly models how demand changes with price (elasticity) in a mathematically consistent way, making the learned relationships more economically realistic and stable.