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Truthfulness and approximation with value-maximizing bidders

  • Technische Universität München

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

7 Zitate (Scopus)

Abstract

In many markets bidders want to maximize value rather than payoff. This is different to the quasi-linear utility functions, and leads to different strategies and outcomes. We refer to bidders who maximize value as value bidders. While simple single-object auction formats are truthful for value bidders, standard multi-object auction formats allow for manipulation. It is straightforward to show that there cannot be a truthful and revenue-maximizing deterministic auction mechanism with value bidders and general valuations. Using approximation as a means to achieve truthfulness, we study truthful approximation mechanisms for value bidders. We show that the approximation ratio that can be achieved with a deterministic and truthful approximation mechanism with n bidders and m items cannot be higher than 1/n for general valuations. For randomized approximation mechanisms there is a framework with a ratio of O(√m/ϵ3) with probability at least 1 − ϵ, for 0 < ϵ < 1.

OriginalspracheEnglisch
TitelAlgorithmic Game Theory - 9th International Symposium, SAGT 2016, Proceedings
Redakteure/-innenMartin Gairing, Rahul Savani
Herausgeber (Verlag)Springer Verlag
Seiten235-246
Seitenumfang12
ISBN (Print)9783662533536
DOIs
PublikationsstatusVeröffentlicht - 2016
Veranstaltung9th International Symposium on Algorithmic Game Theory, SAGT 2016 - Liverpool, Großbritannien/Vereinigtes Königreich
Dauer: 19 Sept. 201621 Sept. 2016

Publikationsreihe

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Band9928 LNCS
ISSN (Print)0302-9743
ISSN (elektronisch)1611-3349

Konferenz

Konferenz9th International Symposium on Algorithmic Game Theory, SAGT 2016
Land/GebietGroßbritannien/Vereinigtes Königreich
OrtLiverpool
Zeitraum19/09/1621/09/16

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