Bounds on the revenue gap of linear posted pricing for selling a divisible item

Selling a perfectly divisible item to potential buyers is a fundamental task with apparent applications to pricing communication bandwidth and cloud computing services. Surprisingly, despite the rich literature on single-item auctions, revenue maximization when selling a divisible item is a much les...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Caragiannis, Ioannis, Jiang, Zhile, Kerentzis, Apostolis
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page
container_title
container_volume
creator Caragiannis, Ioannis
Jiang, Zhile
Kerentzis, Apostolis
description Selling a perfectly divisible item to potential buyers is a fundamental task with apparent applications to pricing communication bandwidth and cloud computing services. Surprisingly, despite the rich literature on single-item auctions, revenue maximization when selling a divisible item is a much less understood objective. We introduce a Bayesian setting, in which the potential buyers have concave valuation functions (defined for each possible item fraction) that are randomly chosen according to known probability distributions. Extending the sequential posted pricing paradigm, we focus on mechanisms that use linear pricing, charging a fixed price for the whole item and proportional prices for fractions of it. Our goal is to understand the power of such mechanisms by bounding the gap between the expected revenue that can be achieved by the best among these mechanisms and the maximum expected revenue that can be achieved by any mechanism assuming mild restrictions on the behavior of the buyers. Under regularity assumptions for the probability distributions, we show that this revenue gap depends only logarithmically on a natural parameter characterizing the valuation functions and the number of agents. Our results follow by bounding the objective value of a mathematical program that maximizes the ex-ante relaxation of optimal revenue under linear pricing revenue constraints.
doi_str_mv 10.48550/arxiv.2007.08246
format Article
fullrecord <record><control><sourceid>arxiv_GOX</sourceid><recordid>TN_cdi_arxiv_primary_2007_08246</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2007_08246</sourcerecordid><originalsourceid>FETCH-arxiv_primary_2007_082463</originalsourceid><addsrcrecordid>eNqFjrEOwiAURVkcjPoBTr4fELG22lmjcXNxb7B91JdQIECJ_r22cXe6d7g59zC23Aqel0UhNtK_KPFMiAMXZZbvp-x2tL1pAlgD8YngMaHpEVrpwCrQZFB6cDZEbMB5qsm0oKyHgFoPXUJDiQI9NAJF7OZsoqQOuPjljK0u5_vpuh6vqy-ik_5dDQrVqLD7v_gAlxk8AA</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Bounds on the revenue gap of linear posted pricing for selling a divisible item</title><source>arXiv.org</source><creator>Caragiannis, Ioannis ; Jiang, Zhile ; Kerentzis, Apostolis</creator><creatorcontrib>Caragiannis, Ioannis ; Jiang, Zhile ; Kerentzis, Apostolis</creatorcontrib><description>Selling a perfectly divisible item to potential buyers is a fundamental task with apparent applications to pricing communication bandwidth and cloud computing services. Surprisingly, despite the rich literature on single-item auctions, revenue maximization when selling a divisible item is a much less understood objective. We introduce a Bayesian setting, in which the potential buyers have concave valuation functions (defined for each possible item fraction) that are randomly chosen according to known probability distributions. Extending the sequential posted pricing paradigm, we focus on mechanisms that use linear pricing, charging a fixed price for the whole item and proportional prices for fractions of it. Our goal is to understand the power of such mechanisms by bounding the gap between the expected revenue that can be achieved by the best among these mechanisms and the maximum expected revenue that can be achieved by any mechanism assuming mild restrictions on the behavior of the buyers. Under regularity assumptions for the probability distributions, we show that this revenue gap depends only logarithmically on a natural parameter characterizing the valuation functions and the number of agents. Our results follow by bounding the objective value of a mathematical program that maximizes the ex-ante relaxation of optimal revenue under linear pricing revenue constraints.</description><identifier>DOI: 10.48550/arxiv.2007.08246</identifier><language>eng</language><subject>Computer Science - Computer Science and Game Theory</subject><creationdate>2020-07</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2007.08246$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2007.08246$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Caragiannis, Ioannis</creatorcontrib><creatorcontrib>Jiang, Zhile</creatorcontrib><creatorcontrib>Kerentzis, Apostolis</creatorcontrib><title>Bounds on the revenue gap of linear posted pricing for selling a divisible item</title><description>Selling a perfectly divisible item to potential buyers is a fundamental task with apparent applications to pricing communication bandwidth and cloud computing services. Surprisingly, despite the rich literature on single-item auctions, revenue maximization when selling a divisible item is a much less understood objective. We introduce a Bayesian setting, in which the potential buyers have concave valuation functions (defined for each possible item fraction) that are randomly chosen according to known probability distributions. Extending the sequential posted pricing paradigm, we focus on mechanisms that use linear pricing, charging a fixed price for the whole item and proportional prices for fractions of it. Our goal is to understand the power of such mechanisms by bounding the gap between the expected revenue that can be achieved by the best among these mechanisms and the maximum expected revenue that can be achieved by any mechanism assuming mild restrictions on the behavior of the buyers. Under regularity assumptions for the probability distributions, we show that this revenue gap depends only logarithmically on a natural parameter characterizing the valuation functions and the number of agents. Our results follow by bounding the objective value of a mathematical program that maximizes the ex-ante relaxation of optimal revenue under linear pricing revenue constraints.</description><subject>Computer Science - Computer Science and Game Theory</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNqFjrEOwiAURVkcjPoBTr4fELG22lmjcXNxb7B91JdQIECJ_r22cXe6d7g59zC23Aqel0UhNtK_KPFMiAMXZZbvp-x2tL1pAlgD8YngMaHpEVrpwCrQZFB6cDZEbMB5qsm0oKyHgFoPXUJDiQI9NAJF7OZsoqQOuPjljK0u5_vpuh6vqy-ik_5dDQrVqLD7v_gAlxk8AA</recordid><startdate>20200716</startdate><enddate>20200716</enddate><creator>Caragiannis, Ioannis</creator><creator>Jiang, Zhile</creator><creator>Kerentzis, Apostolis</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20200716</creationdate><title>Bounds on the revenue gap of linear posted pricing for selling a divisible item</title><author>Caragiannis, Ioannis ; Jiang, Zhile ; Kerentzis, Apostolis</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-arxiv_primary_2007_082463</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Computer Science - Computer Science and Game Theory</topic><toplevel>online_resources</toplevel><creatorcontrib>Caragiannis, Ioannis</creatorcontrib><creatorcontrib>Jiang, Zhile</creatorcontrib><creatorcontrib>Kerentzis, Apostolis</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Caragiannis, Ioannis</au><au>Jiang, Zhile</au><au>Kerentzis, Apostolis</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Bounds on the revenue gap of linear posted pricing for selling a divisible item</atitle><date>2020-07-16</date><risdate>2020</risdate><abstract>Selling a perfectly divisible item to potential buyers is a fundamental task with apparent applications to pricing communication bandwidth and cloud computing services. Surprisingly, despite the rich literature on single-item auctions, revenue maximization when selling a divisible item is a much less understood objective. We introduce a Bayesian setting, in which the potential buyers have concave valuation functions (defined for each possible item fraction) that are randomly chosen according to known probability distributions. Extending the sequential posted pricing paradigm, we focus on mechanisms that use linear pricing, charging a fixed price for the whole item and proportional prices for fractions of it. Our goal is to understand the power of such mechanisms by bounding the gap between the expected revenue that can be achieved by the best among these mechanisms and the maximum expected revenue that can be achieved by any mechanism assuming mild restrictions on the behavior of the buyers. Under regularity assumptions for the probability distributions, we show that this revenue gap depends only logarithmically on a natural parameter characterizing the valuation functions and the number of agents. Our results follow by bounding the objective value of a mathematical program that maximizes the ex-ante relaxation of optimal revenue under linear pricing revenue constraints.</abstract><doi>10.48550/arxiv.2007.08246</doi><oa>free_for_read</oa></addata></record>
fulltext fulltext_linktorsrc
identifier DOI: 10.48550/arxiv.2007.08246
ispartof
issn
language eng
recordid cdi_arxiv_primary_2007_08246
source arXiv.org
subjects Computer Science - Computer Science and Game Theory
title Bounds on the revenue gap of linear posted pricing for selling a divisible item
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-21T15%3A59%3A22IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-arxiv_GOX&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Bounds%20on%20the%20revenue%20gap%20of%20linear%20posted%20pricing%20for%20selling%20a%20divisible%20item&rft.au=Caragiannis,%20Ioannis&rft.date=2020-07-16&rft_id=info:doi/10.48550/arxiv.2007.08246&rft_dat=%3Carxiv_GOX%3E2007_08246%3C/arxiv_GOX%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true