Dynamic Pricing with Limited Supply
Moshe Babaioff, Shaddin Dughmi, Robert Kleinberg, Aleksandrs, Slivkins

TL;DR
This paper develops prior-independent dynamic pricing mechanisms for limited supply scenarios, achieving near-optimal revenue compared to the offline benchmark without prior distribution knowledge, and connects the problem to multi-armed bandits.
Contribution
It introduces a novel prior-independent mechanism for limited supply dynamic pricing with theoretical performance guarantees and establishes a connection to multi-armed bandit problems.
Findings
Mechanism achieves $O((k \log n)^{2/3})$ revenue gap for regular distributions.
Guarantees improve to $O(\sqrt{k} \log n)$ under certain conditions.
Matching lower bounds demonstrate the optimality of the approach.
Abstract
We consider the problem of dynamic pricing with limited supply. A seller has identical items for sale and is facing potential buyers ("agents") that are arriving sequentially. Each agent is interested in buying one item. Each agent's value for an item is an IID sample from some fixed distribution with support . The seller offers a take-it-or-leave-it price to each arriving agent (possibly different for different agents), and aims to maximize his expected revenue. We focus on "prior-independent" mechanisms -- ones that do not use any information about the distribution. They are desirable because knowing the distribution is unrealistic in many practical scenarios. We study how the revenue of such mechanisms compares to the revenue of the optimal offline mechanism that knows the distribution ("offline benchmark"). We present a prior-independent dynamic pricing mechanism…
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