Multidimensional Dynamic Pricing for Welfare Maximization
Aaron Roth, Aleksandrs Slivkins, Jonathan Ullman, Zhiwei Steven Wu

TL;DR
This paper develops a polynomial-time dynamic pricing algorithm for welfare maximization in a setting with multiple indivisible goods, unknown buyer valuations, and production costs, overcoming non-concavity and observability challenges.
Contribution
It introduces a novel dynamic pricing method that handles non-concave price-response functions and limited supply, extending welfare optimization to complex, realistic market scenarios.
Findings
Achieves welfare optimization with polynomial complexity in goods and approximation parameters.
Introduces a price randomization technique to induce valuation regularization.
Extends results to limited-supply settings with non-replenishable goods.
Abstract
We study the problem of a seller dynamically pricing distinct types of indivisible goods, when faced with the online arrival of unit-demand buyers drawn independently from an unknown distribution. The goods are not in limited supply, but can only be produced at a limited rate and are costly to produce. The seller observes only the bundle of goods purchased at each day, but nothing else about the buyer's valuation function. Our main result is a dynamic pricing algorithm for optimizing welfare (including the seller's cost of production) that runs in time and a number of rounds that are polynomial in and the approximation parameter. We are able to do this despite the fact that (i) the price-response function is not continuous, and even its fractional relaxation is a non-concave function of the prices, and (ii) the welfare is not observable to the seller. We derive this result as…
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