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A duality-based unified approach to Bayesian mechanism design

A duality-based unified approach to Bayesian mechanism design A Duality-Based Unified Approach to Bayesian Mechanism Design YANG CAI McGill University and NIKHIL R. DEVANUR Microsoft Research and S. MATTHEW WEINBERG Princeton University In this letter we briefly survey our recent work [Cai et al. 2016]. In it, we provide a new duality theory for Bayesian mechanism design which is quite general, and applies for any objective the designer wishes to optimize, and for arbitrary agent valuations. We then apply our theory to auction design settings with many independent buyers who have independent values for many items, and are able to provide a unified proof of several recent exciting works on this front [Hart and Nisan 2012; Li and Yao 2013; Babaioff et al. 2014; Yao 2015; Chawla et al. 2007; Chawla et al. 2010; Chawla et al. 2015]. These works all show that simple mechanisms are approximately optimal in various settings. In some cases, our principled approach yields greatly improved approximation ratios as well. Categories and Subject Descriptors: J.4 [Social and Behavioral Science]: Economics General Terms: Algorithms, Design, Economics, Theory Additional Key Words and Phrases: Bayesian Mechanism Design, Simple Mechanisms, Revenue, Approximation, Duality 1. INTRODUCTION In this letter, we briefly overview our recent paper [Cai et http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM SIGecom Exchanges Association for Computing Machinery

A duality-based unified approach to Bayesian mechanism design

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2016 by ACM Inc.
ISSN
1551-9031
DOI
10.1145/2994501.2994506
Publisher site
See Article on Publisher Site

Abstract

A Duality-Based Unified Approach to Bayesian Mechanism Design YANG CAI McGill University and NIKHIL R. DEVANUR Microsoft Research and S. MATTHEW WEINBERG Princeton University In this letter we briefly survey our recent work [Cai et al. 2016]. In it, we provide a new duality theory for Bayesian mechanism design which is quite general, and applies for any objective the designer wishes to optimize, and for arbitrary agent valuations. We then apply our theory to auction design settings with many independent buyers who have independent values for many items, and are able to provide a unified proof of several recent exciting works on this front [Hart and Nisan 2012; Li and Yao 2013; Babaioff et al. 2014; Yao 2015; Chawla et al. 2007; Chawla et al. 2010; Chawla et al. 2015]. These works all show that simple mechanisms are approximately optimal in various settings. In some cases, our principled approach yields greatly improved approximation ratios as well. Categories and Subject Descriptors: J.4 [Social and Behavioral Science]: Economics General Terms: Algorithms, Design, Economics, Theory Additional Key Words and Phrases: Bayesian Mechanism Design, Simple Mechanisms, Revenue, Approximation, Duality 1. INTRODUCTION In this letter, we briefly overview our recent paper [Cai et

Journal

ACM SIGecom ExchangesAssociation for Computing Machinery

Published: Sep 6, 2016

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