Dynamic Pricing with Parametric Demand Learning and Reference-Price Effects

Abstract

In reality, sellers face challenges in obtaining perfect demand information. Demand is influenced not only by price but also by behavioral factors such as reference effects, which complicate optimal pricing for enterprises. To address this problem, we propose a dynamic pricing model that incorporates demand learning and considers consumer reference effects. Using the Bayesian method and based on historical sales and prices, sellers can learn about demand patterns. We analyze the model to determine the existence of an optimal solution and provide an algorithm to solve it. Our numerical simulation demonstrates that the total consumer demand and the impact of price on demand remain relatively stable over time. However, the factors influencing the reference effects exhibit greater variability. Sellers can also gain insights into market demand through their learning behavior in each phase and adjust production based on market size. For instance, our simulation shows an increase in market demand over time, allowing the seller to adjust the production plan according to the demand change.