Abstract

https://ssrn.com/abstract=2783938
 


 



Where to Sell: Simulating Auctions from Learning Algorithms


Hamid Nazerzadeh


University of Southern California - Marshall School of Business

Renato Paes Leme


Google, Inc.

Afshin Rostamizadeh


Google, Inc.

Umar Syed


Google, Inc.

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.

Number of Pages in PDF File: 22

Keywords: Online Learning, Mechanism Design, Ad Auctions


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Date posted: May 26, 2016 ; Last revised: June 2, 2016

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

Contact Information

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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