A.V. den Boer

Bio: A.V. den Boer is an academic researcher. The author has contributed to research in topics: Dynamic pricing & Estimator. The author has an hindex of 4, co-authored 7 publications receiving 510 citations.

Dynamic pricing and learning: Historical origins, current research, and new directions

Abstract: The topic of dynamic pricing and learning has received a considerable amount of attention in recent years, from different scientific communities. We survey these literature streams: we provide a brief introduction to the historical origins of quantitative research on pricing and demand estimation, point to different subfields in the area of dynamic pricing, and provide an in-depth overview of the available literature on dynamic pricing and learning. Our focus is on the operations research and management science literature, but we also discuss relevant contributions from marketing, economics, econometrics, and computer science. We discuss relations with methodologically related research areas, and identify directions for future research.

Dynamic pricing and learning with finite inventories

Abstract: We study a dynamic pricing problem with finite inventory and parametric uncertainty on the demand distribution. Products are sold during selling seasons of finite length, and inventory that is unsold at the end of a selling season, perishes. The goal of the seller is to determine a pricing strategy that maximizes the expected revenue. Inference on the unknown parameters is made by maximum likelihood estimation. We propose a pricing strategy for this problem, and show that the Regret - which is the expected revenue loss due to not using the optimal prices - after T selling seasons is O(log2(T)). Apart from a small modification, our pricing strategy is a certainty equivalent pricing strategy, which means that at each moment, the price is chosen that is optimal w.r.t. the current parameter estimates. The good performance of our strategy is caused by an endogenous-learning property: using a pricing policy that is optimal w.r.t. a certain parameter sufficiently close to the optimal one, leads to a.s. convergence of the parameter estimates to the true, unknown parameter. We also show an instance in which the regret for all pricing policies grows as log(T). This shows that ourupper bound on the growth rate of the regret is close to the best achievable growth rate.

Applied Probability and Queues

Abstract: (1987). Applied Probability and Queues. Journal of the Operational Research Society: Vol. 38, No. 11, pp. 1095-1096.

Dynamic pricing and learning: Historical origins, current research, and new directions

Abstract: The topic of dynamic pricing and learning has received a considerable amount of attention in recent years, from different scientific communities. We survey these literature streams: we provide a brief introduction to the historical origins of quantitative research on pricing and demand estimation, point to different subfields in the area of dynamic pricing, and provide an in-depth overview of the available literature on dynamic pricing and learning. Our focus is on the operations research and management science literature, but we also discuss relevant contributions from marketing, economics, econometrics, and computer science. We discuss relations with methodologically related research areas, and identify directions for future research.

Simultaneously Learning and Optimizing Using Controlled Variance Pricing

Abstract: Price experimentation is an important tool for firms to find the optimal selling price of their products. It should be conducted properly, since experimenting with selling prices can be costly. A firm, therefore, needs to find a pricing policy that optimally balances between learning the optimal price and gaining revenue. In this paper, we propose such a pricing policy, called controlled variance pricing CVP. The key idea of the policy is to enhance the certainty equivalent pricing policy with a taboo interval around the average of previously chosen prices. The width of the taboo interval shrinks at an appropriate rate as the amount of data gathered gets large; this guarantees sufficient price dispersion. For a large class of demand models, we show that this procedure is strongly consistent, which means that eventually the value of the optimal price will be learned, and derive upper bounds on the regret, which is the expected amount of money lost due to not using the optimal price. Numerical tests indicate that CVP performs well on different demand models and time scales. This paper was accepted by Assaf Zeevi, stochastic models and simulation.