Demand Distribution

Demand distribution modeling focuses on accurately predicting and understanding the variability of consumer demand for goods and services, aiming to optimize pricing, inventory management, and resource allocation. Current research emphasizes developing robust models that account for complexities like time-varying demand, censored data (e.g., unobserved demand due to capacity constraints), and the impact of multiple agents or competitors. This involves employing advanced techniques such as Bayesian methods, censorship-aware machine learning models, and reinforcement learning algorithms, often tailored to specific application domains like ride-sharing or energy grids. Improved demand distribution modeling has significant implications for various industries, enhancing efficiency, profitability, and resource utilization.