22 Pages Posted: 26 May 2016 Last revised: 2 Jun 2016
Date Written: May 1, 2016
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: 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