Conference item
Revenue maximization for market intermediation with correlated priors
- Abstract:
- We study the computational challenge faced by a intermediary who attempts to profit from trade with a small number of buyers and sellers of some item. In the version of the problem that we study, the number of buyers and sellers is constant, but their joint distribution of the item’s value may be complicated. We consider discretized distributions, where the complexity parameter is the support size, or number of different prices that may occur. We show that maximizing the expected revenue is computationally tractable (via an LP) if we are allowed to use randomized mechanisms. For the deterministic case, we show how an optimal mechanism can be efficiently computed for the oneseller/one-buyer case, but give a contrasting NP-completeness result for the one-seller/two-buyer case.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Access Document
- Files:
-
-
(Preview, Accepted manuscript, pdf, 391.0KB, Terms of use)
-
- Publisher copy:
- 10.1007/978-3-662-53354-3_22
Authors
- Publisher:
- Springer Verlag
- Host title:
- International Symposium on Algorithmic Game Theory: SAGT 2016: Algorithmic Game Theory
- Volume:
- 9928
- Pages:
- 273-285
- Series:
- Lecture Notes in Computer Science
- Publication date:
- 2016-01-01
- Acceptance date:
- 2016-07-01
- DOI:
- ISSN:
-
0302-9743
- ISBN:
- 9783662533536
- Pubs id:
-
pubs:634635
- UUID:
-
uuid:81862785-8040-49a3-adca-fbede1f2e235
- Local pid:
-
pubs:634635
- Source identifiers:
-
634635
- Deposit date:
-
2016-07-19
Terms of use
- Copyright holder:
- Springer-Verlag Berlin Heidelberg
- Copyright date:
- 2016
- Notes:
- Copyright © 2016 Springer-Verlag Berlin Heidelberg. This is the accepted manuscript version of the article. The final version is available online from Springer at: 10.1007/978-3-662-53354-3_22
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