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Where to Sell: Simulating Auctions from Learning Algorithms

22 Pages Posted: 26 May 2016 Last revised: 2 Jun 2016

Hamid Nazerzadeh

University of Southern California - Marshall School of Business

Renato Paes Leme

Google Inc.

Afshin Rostamizadeh

Google, Inc.

Umar Syed

Google, Inc.

Date Written: May 1, 2016

Abstract

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

Keywords: Online Learning, Mechanism Design, Ad Auctions

Suggested Citation

Nazerzadeh, Hamid and Paes Leme, Renato and Rostamizadeh, Afshin and Syed, Umar, Where to Sell: Simulating Auctions from Learning Algorithms (May 1, 2016). Available at SSRN: https://ssrn.com/abstract=2783938

Hamid Nazerzadeh (Contact Author)

University of Southern California - Marshall School of Business ( email )

Bridge Memorial Hall
Los Angeles, CA 90089
United States

HOME PAGE: http://www-bcf.usc.edu/~nazerzad/

Renato Paes Leme

Google Inc. ( email )

1600 Amphitheatre Parkway
Second Floor
Mountain View, CA 94043
United States

Afshin Rostamizadeh

Google, Inc. ( email )

1600 Amphitheatre Parkway
Second Floor
Mountain View, CA 94043
United States

Umar Syed

Google, Inc. ( email )

1600 Amphitheatre Parkway
Second Floor
Mountain View, CA 94043
United States

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