Does Information Revelation Improve Revenue?
We study the problem of optimal auction design in a valuation model, explicitly motivated by online ad auctions, in which there is two-way informational asymmetry, in the sense that private information is available to both the seller (the item type) and the bidders (their type), and the value of eac...
| मुख्य लेखकों: | , , |
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| अन्य लेखक: | |
| स्वरूप: | लेख |
| भाषा: | en_US |
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Association for Computing Machinery
2017
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| ऑनलाइन पहुंच: | http://hdl.handle.net/1721.1/110837 https://orcid.org/0000-0002-5451-0490 https://orcid.org/0000-0002-7560-5069 |
| _version_ | 1826208576471826432 |
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| author | Daskalakis, Konstantinos Papadimitriou, Christos H Tzamos, Christos |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Daskalakis, Konstantinos Papadimitriou, Christos H Tzamos, Christos |
| author_sort | Daskalakis, Konstantinos |
| collection | MIT |
| description | We study the problem of optimal auction design in a valuation model, explicitly motivated by online ad auctions, in which there is two-way informational asymmetry, in the sense that private information is available to both the seller (the item type) and the bidders (their type), and the value of each bidder for the item depends both on his own and the item's type. Importantly, we allow arbitrary auction formats involving, potentially, several rounds of signaling from the seller and decisions by the bidders, and seek to find the optimum co-design of signaling and auction (we call this optimum the "optimum augmented auction"). We characterize exactly the optimum augmented auction for our valuation model by establishing its equivalence with a multi-item Bayesian auction with additive bidders. Surprisingly, in the optimum augmented auction there is no signaling whatsoever, and in fact the seller need not access the available information about the item type until after the bidder chooses his bid. Suboptimal solutions to this problem, which have appeared in the recent literature, are shown to correspond to well-studied ways to approximate multi-item auctions by simpler formats, such as grand-bundling (this corresponds to Myerson's auction without any information revelation), selling items separately (this corresponds to Myerson's auction preceded by full information revelation as in [Fu et al. 2012]), and fractional partitioning (this corresponds to Myerson's auction preceded by optimal signaling). Consequently, all these solutions are separated by large approximation gaps from the optimum revenue. |
| first_indexed | 2024-09-23T14:07:49Z |
| format | Article |
| id | mit-1721.1/110837 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T14:07:49Z |
| publishDate | 2017 |
| publisher | Association for Computing Machinery |
| record_format | dspace |
| spelling | mit-1721.1/1108372022-09-28T18:43:26Z Does Information Revelation Improve Revenue? Daskalakis, Konstantinos Papadimitriou, Christos H Tzamos, Christos Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Daskalakis, Konstantinos Papadimitriou, Christos H Tzamos, Christos We study the problem of optimal auction design in a valuation model, explicitly motivated by online ad auctions, in which there is two-way informational asymmetry, in the sense that private information is available to both the seller (the item type) and the bidders (their type), and the value of each bidder for the item depends both on his own and the item's type. Importantly, we allow arbitrary auction formats involving, potentially, several rounds of signaling from the seller and decisions by the bidders, and seek to find the optimum co-design of signaling and auction (we call this optimum the "optimum augmented auction"). We characterize exactly the optimum augmented auction for our valuation model by establishing its equivalence with a multi-item Bayesian auction with additive bidders. Surprisingly, in the optimum augmented auction there is no signaling whatsoever, and in fact the seller need not access the available information about the item type until after the bidder chooses his bid. Suboptimal solutions to this problem, which have appeared in the recent literature, are shown to correspond to well-studied ways to approximate multi-item auctions by simpler formats, such as grand-bundling (this corresponds to Myerson's auction without any information revelation), selling items separately (this corresponds to Myerson's auction preceded by full information revelation as in [Fu et al. 2012]), and fractional partitioning (this corresponds to Myerson's auction preceded by optimal signaling). Consequently, all these solutions are separated by large approximation gaps from the optimum revenue. United States. Office of Naval Research (grant N0 0014-12-1-0999) National Science Foundation (U.S.) (Award CCF-0953960 (CAREER)) National Science Foundation (U.S.). Division of Computing and Communication Foundations (CCF-1551875) National Science Foundation (U.S.). Division of Computing and Communication Foundations (CCF-1408635) Simons Award for Graduate Students in Theoretical Computer Science 2017-07-25T17:21:00Z 2017-07-25T17:21:00Z 2016-07 Article http://purl.org/eprint/type/ConferencePaper 9781450339360 http://hdl.handle.net/1721.1/110837 Daskalakis, Constantinos, Christos Papadimitriou, and Christos Tzamos. “Does Information Revelation Improve Revenue?” Proceedings of the 2016 ACM Conference on Economics and Computation - EC ’16 (2016). https://orcid.org/0000-0002-5451-0490 https://orcid.org/0000-0002-7560-5069 en_US http://dx.doi.org/10.1145/2940716.2940789 Proceedings of the 2016 ACM Conference on Economics and Computation - EC '16 Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Association for Computing Machinery MIT Web Domain |
| spellingShingle | Daskalakis, Konstantinos Papadimitriou, Christos H Tzamos, Christos Does Information Revelation Improve Revenue? |
| title | Does Information Revelation Improve Revenue? |
| title_full | Does Information Revelation Improve Revenue? |
| title_fullStr | Does Information Revelation Improve Revenue? |
| title_full_unstemmed | Does Information Revelation Improve Revenue? |
| title_short | Does Information Revelation Improve Revenue? |
| title_sort | does information revelation improve revenue |
| url | http://hdl.handle.net/1721.1/110837 https://orcid.org/0000-0002-5451-0490 https://orcid.org/0000-0002-7560-5069 |
| work_keys_str_mv | AT daskalakiskonstantinos doesinformationrevelationimproverevenue AT papadimitriouchristosh doesinformationrevelationimproverevenue AT tzamoschristos doesinformationrevelationimproverevenue |