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abstract

Where to Sell: Simulating Auctions From Learning Algorithms

Published:21 July 2016Publication History

ABSTRACT

Ad exchange platforms connect online publishers and advertisers and facilitate the sale of billions of impressions every day. We study these environments from the perspective of a publisher who wants to find the profit-maximizing exchange in which to sell his inventory. Ideally, the publisher would run an auction among exchanges. However, this is not usually possible due to practical business considerations. Instead, the publisher must send each impression to only one of the exchanges, along with an asking price. We model the problem as a variation of the multi-armed bandits problem in which exchanges (arms) can behave strategically in order to maximizes their own profit. We propose e mechanisms that find the best exchange with sub-linear regret and have desirable incentive properties.

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        cover image ACM Conferences
        EC '16: Proceedings of the 2016 ACM Conference on Economics and Computation
        July 2016
        874 pages
        ISBN:9781450339360
        DOI:10.1145/2940716

        Copyright © 2016 Owner/Author

        Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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        Association for Computing Machinery

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        Publication History

        • Published: 21 July 2016

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