Multi-Step Look-Ahead Optimization Methods for Dynamic Pricing With Demand Learning

Dynamic pricing with real-time demand learning

In many service industries, the firm adjusts the product price dynamically by taking into account the current product inventory and the future demand distribution. Because the firm can easily monitor the product inventory, the success of dynamic pricing relies on an accurate demand forecast. In this paper, we consider a situation where the firm does not have an accurate demand forecast, but can...

Dynamic Pricing with Demand Learning under Competition

In this thesis, we focus on oligopolistic markets for a single perishable product, where firms compete by setting prices (Bertrand competition) or by allocating quantities (Cournot competition) dynamically over a finite selling horizon. The price-demand relationship is modeled as a parametric function, whose parameters are unknown, but learned through a data driven approach. The market can be e...

Bounded Rationality in Pricing under State Dependent Demand: Do Firms Look Ahead? How Far Ahead?

The authors propose an empirical procedure to investigate the pricing behavior of manufacturers and retailers in the presence of state dependent demand. Rather than assuming that firms are perfectly forward looking and accordingly solving for dynamic equilibria that would arise in the presence of state dependence, the authors systematically evaluate whether boundedly rational firms indeed look ...

Electronic Companion — “ Dynamic Pricing for Nonperishable Products with Demand Learning

Dynamic Pricing for Nonperishable Products with Demand Learning

A retailer is endowed with a finite inventory of a non-perishable product. Demand for this product is driven by a price-sensitive Poisson process that depends on an unknown parameter, θ; a proxy for the market size. If θ is high then the retailer can take advantage of a large market charging premium prices, but if θ is small then price markdowns can be applied to encourage sales. The retailer h...