DUALITY AND OPTIMALITY OF AUCTIONS FOR UNIFORM DISTRIBUTIONS.

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Title: DUALITY AND OPTIMALITY OF AUCTIONS FOR UNIFORM DISTRIBUTIONS.
Authors: GIANNAKOPOULO, YIANNIS1 ygiannak@cs.ox.ac.uk, KOUTSOUPIAS, ELIAS1 elias@cs.ox.ac.uk
Source: SIAM Journal on Computing. 2018, Vol. 47 Issue 1, p121-165. 45p.
Subjects: Duality (Logic), Uniform distribution (Probability theory), Bayesian analysis
Abstract: We develop a general duality-theory framework for revenue maximization in additive Bayesian auctions. The framework extends linear programming duality and complementarity to constraints with partial derivatives. The dual system reveals the geometric nature of the problem and highlights its connection with the theory of bipartite graph matchings. We demonstrate the power of the framework by applying it to a multiple-good monopoly setting where the buyer has uniformly distributed valuations for the items, the canonical long-standing open problem in the area. We propose a deterministic selling mechanism called straight-jacket auction (SJA), which we prove to be exactly optimal for up to six items, and conjecture its optimality for any number of goods. The duality framework is used not only for proving optimality, but perhaps more importantly for deriving the optimal mechanism itself; as a result, SJA is defined by natural geometric constraints. [ABSTRACT FROM AUTHOR]
Copyright of SIAM Journal on Computing is the property of Society for Industrial & Applied Mathematics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: DUALITY AND OPTIMALITY OF AUCTIONS FOR UNIFORM DISTRIBUTIONS.
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  Data: We develop a general duality-theory framework for revenue maximization in additive Bayesian auctions. The framework extends linear programming duality and complementarity to constraints with partial derivatives. The dual system reveals the geometric nature of the problem and highlights its connection with the theory of bipartite graph matchings. We demonstrate the power of the framework by applying it to a multiple-good monopoly setting where the buyer has uniformly distributed valuations for the items, the canonical long-standing open problem in the area. We propose a deterministic selling mechanism called straight-jacket auction (SJA), which we prove to be exactly optimal for up to six items, and conjecture its optimality for any number of goods. The duality framework is used not only for proving optimality, but perhaps more importantly for deriving the optimal mechanism itself; as a result, SJA is defined by natural geometric constraints. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of SIAM Journal on Computing is the property of Society for Industrial & Applied Mathematics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1137/16M1072218
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      – Code: eng
        Text: English
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        PageCount: 45
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    Subjects:
      – SubjectFull: Duality (Logic)
        Type: general
      – SubjectFull: Uniform distribution (Probability theory)
        Type: general
      – SubjectFull: Bayesian analysis
        Type: general
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      – TitleFull: DUALITY AND OPTIMALITY OF AUCTIONS FOR UNIFORM DISTRIBUTIONS.
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              Text: 2018
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