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Multidimensional Dynamic Pricing for Welfare Maximization

Published: 17 April 2020 Publication History

Abstract

We study the problem of a seller dynamically pricing d 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 d 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 an application of a general technique for optimizing welfare over divisible goods, which is of independent interest. When buyers have strongly concave, Hölder continuous valuation functions over d divisible goods, we give a general polynomial time dynamic pricing technique. We are able to apply this technique to the setting of unit-demand buyers despite the fact that in that setting the goods are not divisible, and the natural fractional relaxation of a unit-demand valuation is not strongly concave. To apply our general technique, we introduce a novel price randomization procedure that has the effect of implicitly inducing buyers to “regularize” their valuations with a strongly concave function. Finally, we also extend our results to a limited-supply setting in which the supply of each good cannot be replenished.

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Published In

cover image ACM Transactions on Economics and Computation
ACM Transactions on Economics and Computation  Volume 8, Issue 1
Special Issue on EC'17
February 2020
150 pages
ISSN:2167-8375
EISSN:2167-8383
DOI:10.1145/3387139
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 April 2020
Accepted: 01 March 2020
Revised: 01 December 2018
Received: 01 May 2017
Published in TEAC Volume 8, Issue 1

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Author Tags

  1. Multidimensional dynamic pricing
  2. convex optimization
  3. limited supply
  4. online learning
  5. revealed preferences

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Funding Sources

  • Sloan Foundation Fellowship
  • NSF
  • DARPA

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Cited By

View all
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  • (2021)Stateful Strategic RegressionProceedings of the 35th International Conference on Neural Information Processing Systems10.5555/3540261.3542462(28728-28741)Online publication date: 6-Dec-2021
  • (undefined)Price Discovery in Waiting Lists: A Connection to Stochastic Gradient DescentSSRN Electronic Journal10.2139/ssrn.4192003
  • (undefined)Equity in Dynamic Matching: Extreme Waitlist PoliciesSSRN Electronic Journal10.2139/ssrn.4007681

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