Dynamic Pricing with Adversarially-Censored Demands

Abstract

We study an online dynamic pricing problem where the potential demand at each time period t=1,2,, T is stochastic and dependent on the price. However, a perishable inventory is imposed at the beginning of each time t, censoring the potential demand if it exceeds the inventory level. To address this problem, we introduce a pricing algorithm based on the optimistic estimates of derivatives. We show that our algorithm achieves O(T) optimal regret even with adversarial inventory series. Our findings advance the state-of-the-art in online decision-making problems with censored feedback, offering a theoretically optimal solution against adversarial observations.

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Reproductions