Dynamic Pricing with Adversarially-Censored Demands
This work addresses a problem relevant to online decision-making under uncertainty, particularly for businesses or organizations dealing with perishable inventory and stochastic demand.
The authors tackled the problem of dynamic pricing with adversarially-censored demands, achieving an optimal regret of $ ilde{O}(sqrt{T})$. This result was obtained even in the presence of adversarial inventory series.
We study an online dynamic pricing problem where the potential demand at each time period $t=1,2,\ldots, T$ 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 $\tilde{O}(\sqrt{T})$ 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.