Revenue optimization against strategic buyers

Mehryar Mohri, Andrés Muñoz Medina

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present a revenue optimization algorithm for posted-price auctions when facing a buyer with random valuations who seeks to optimize his γ-discounted surplus. In order to analyze this problem we introduce the notion of ∈-strategic buyer, a more natural notion of strategic behavior than what has been considered in the past. We improve upon the previous state-of-the-art and achieve an optimal regret bound in O(log T + 1/log(1/γ)) when the seller selects prices from a finite set and provide a regret bound in Õ(√T + T1/4/log(1/γ)) when the prices offered are selected out of the interval [0, 1].

Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems
PublisherNeural information processing systems foundation
Pages2530-2538
Number of pages9
Volume2015-January
StatePublished - 2015
Event29th Annual Conference on Neural Information Processing Systems, NIPS 2015 - Montreal, Canada
Duration: Dec 7 2015Dec 12 2015

Other

Other29th Annual Conference on Neural Information Processing Systems, NIPS 2015
CountryCanada
CityMontreal
Period12/7/1512/12/15

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

Cite this

Mohri, M., & Medina, A. M. (2015). Revenue optimization against strategic buyers. In Advances in Neural Information Processing Systems (Vol. 2015-January, pp. 2530-2538). Neural information processing systems foundation.

Revenue optimization against strategic buyers. / Mohri, Mehryar; Medina, Andrés Muñoz.

Advances in Neural Information Processing Systems. Vol. 2015-January Neural information processing systems foundation, 2015. p. 2530-2538.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Mohri, M & Medina, AM 2015, Revenue optimization against strategic buyers. in Advances in Neural Information Processing Systems. vol. 2015-January, Neural information processing systems foundation, pp. 2530-2538, 29th Annual Conference on Neural Information Processing Systems, NIPS 2015, Montreal, Canada, 12/7/15.
Mohri M, Medina AM. Revenue optimization against strategic buyers. In Advances in Neural Information Processing Systems. Vol. 2015-January. Neural information processing systems foundation. 2015. p. 2530-2538
Mohri, Mehryar ; Medina, Andrés Muñoz. / Revenue optimization against strategic buyers. Advances in Neural Information Processing Systems. Vol. 2015-January Neural information processing systems foundation, 2015. pp. 2530-2538
@inproceedings{d7fa9604430648d0a3eb8a06067efeb2,
title = "Revenue optimization against strategic buyers",
abstract = "We present a revenue optimization algorithm for posted-price auctions when facing a buyer with random valuations who seeks to optimize his γ-discounted surplus. In order to analyze this problem we introduce the notion of ∈-strategic buyer, a more natural notion of strategic behavior than what has been considered in the past. We improve upon the previous state-of-the-art and achieve an optimal regret bound in O(log T + 1/log(1/γ)) when the seller selects prices from a finite set and provide a regret bound in {\~O}(√T + T1/4/log(1/γ)) when the prices offered are selected out of the interval [0, 1].",
author = "Mehryar Mohri and Medina, {Andr{\'e}s Mu{\~n}oz}",
year = "2015",
language = "English (US)",
volume = "2015-January",
pages = "2530--2538",
booktitle = "Advances in Neural Information Processing Systems",
publisher = "Neural information processing systems foundation",

}

TY - GEN

T1 - Revenue optimization against strategic buyers

AU - Mohri, Mehryar

AU - Medina, Andrés Muñoz

PY - 2015

Y1 - 2015

N2 - We present a revenue optimization algorithm for posted-price auctions when facing a buyer with random valuations who seeks to optimize his γ-discounted surplus. In order to analyze this problem we introduce the notion of ∈-strategic buyer, a more natural notion of strategic behavior than what has been considered in the past. We improve upon the previous state-of-the-art and achieve an optimal regret bound in O(log T + 1/log(1/γ)) when the seller selects prices from a finite set and provide a regret bound in Õ(√T + T1/4/log(1/γ)) when the prices offered are selected out of the interval [0, 1].

AB - We present a revenue optimization algorithm for posted-price auctions when facing a buyer with random valuations who seeks to optimize his γ-discounted surplus. In order to analyze this problem we introduce the notion of ∈-strategic buyer, a more natural notion of strategic behavior than what has been considered in the past. We improve upon the previous state-of-the-art and achieve an optimal regret bound in O(log T + 1/log(1/γ)) when the seller selects prices from a finite set and provide a regret bound in Õ(√T + T1/4/log(1/γ)) when the prices offered are selected out of the interval [0, 1].

UR - http://www.scopus.com/inward/record.url?scp=84965182140&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84965182140&partnerID=8YFLogxK

M3 - Conference contribution

VL - 2015-January

SP - 2530

EP - 2538

BT - Advances in Neural Information Processing Systems

PB - Neural information processing systems foundation

ER -