Where to sell: Simulating auctions from learning algorithms


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 mechanisms that find the best exchange with sub-linear regret and have desirable incentive properties.